diff --git a/animation/UniAnimate/.gitignore b/animation/UniAnimate/.gitignore new file mode 100644 index 0000000..9fa509b --- /dev/null +++ b/animation/UniAnimate/.gitignore @@ -0,0 +1,18 @@ +*.pkl +*.pt +*.mov +*.pth +*.mov +*.npz +*.npy +*.boj +*.onnx +*.tar +*.bin +cache* +.DS_Store +*DS_Store +outputs/ +**/__pycache__ +***/__pycache__ +*/__pycache__ diff --git a/animation/UniAnimate/README.md b/animation/UniAnimate/README.md new file mode 100644 index 0000000..c5c3d52 --- /dev/null +++ b/animation/UniAnimate/README.md @@ -0,0 +1,344 @@ + + + +
+ + +# UniAnimate: Taming Unified Video Diffusion Models for Consistent Human Image Animation + +[Xiang Wang](https://scholar.google.com.hk/citations?user=cQbXvkcAAAAJ&hl=zh-CN&oi=sra)1, [Shiwei Zhang](https://scholar.google.com.hk/citations?user=ZO3OQ-8AAAAJ&hl=zh-CN)2, [Changxin Gao](https://scholar.google.com.hk/citations?user=4tku-lwAAAAJ&hl=zh-CN)1, [Jiayu Wang](#)2, [Xiaoqiang Zhou](https://scholar.google.com.hk/citations?user=Z2BTkNIAAAAJ&hl=zh-CN&oi=ao)3, [Yingya Zhang](https://scholar.google.com.hk/citations?user=16RDSEUAAAAJ&hl=zh-CN)2 , [Luxin Yan](#)1 , [Nong Sang](https://scholar.google.com.hk/citations?user=ky_ZowEAAAAJ&hl=zh-CN)1 +1HUST   2Alibaba Group   3USTC + + +[🎨 Project Page](https://unianimate.github.io/) + + +

+ + + Demo cases generated by the proposed UniAnimate +

+ + +
+ +## 🔥 News +- **[2024/07/19]** 🔥 We added a **noise prior** to the code (refer to line 381: `noise = diffusion.q_sample(random_ref_frame.clone(), getattr(cfg, "noise_prior_value", 939), noise=noise)` in `tools/inferences/inference_unianimate_long_entrance.py`), which can help achieve better appearance preservation (such as background), especially in long video generation. In addition, we are considering releasing an upgraded version of UniAnimate if we obtain an open source license from the company. +- **[2024/06/26]** For cards with large GPU memory, such as A100 GPU, we support multiple segments parallel denoising to accelerate long video inference. You can change `context_batch_size: 1` in `configs/UniAnimate_infer_long.yaml` to other values greater than 1, such as `context_batch_size: 4`. The inference speed will be improved to a certain extent. +- **[2024/06/15]** 🔥🔥🔥 By offloading CLIP and VAE and explicitly adding torch.float16 (i.e., set `CPU_CLIP_VAE: True` in `configs/UniAnimate_infer.yaml`), the GPU memory can be greatly reduced. Now generating a 32x768x512 video clip only requires **~12G GPU memory**. Refer to [this issue](https://github.com/ali-vilab/UniAnimate/issues/10) for more details. Thanks to [@blackight](https://github.com/blackight) for the contribution！ +- **[2024/06/13]** **🔥🔥🔥 We released the code and models for human image animation, enjoy it!** +- **[2024/06/13]** We have submitted the code to the company for approval, and **the code is expected to be released today or tomorrow**. +- **[2024/06/03]** We initialized this github repository and planed to release the paper. + + +## TODO + +- [x] Release the models and inference code, and pose alignment code. +- [x] Support generating both short and long videos. +- [ ] Release the models for longer video generation in one batch. +- [ ] Release models based on VideoLCM for faster video synthesis. +- [ ] Training the models on higher resolution videos. + + + +## Introduction + +
+

+ + + Overall framework of UniAnimate +

+
+ +Recent diffusion-based human image animation techniques have demonstrated impressive success in synthesizing videos that faithfully follow a given reference identity and a sequence of desired movement poses. Despite this, there are still two limitations: i) an extra reference model is required to align the identity image with the main video branch, which significantly increases the optimization burden and model parameters; ii) the generated video is usually short in time (e.g., 24 frames), hampering practical applications. To address these shortcomings, we present a UniAnimate framework to enable efficient and long-term human video generation. First, to reduce the optimization difficulty and ensure temporal coherence, we map the reference image along with the posture guidance and noise video into a common feature space by incorporating a unified video diffusion model. Second, we propose a unified noise input that supports random noised input as well as first frame conditioned input, which enhances the ability to generate long-term video. Finally, to further efficiently handle long sequences, we explore an alternative temporal modeling architecture based on state space model to replace the original computation-consuming temporal Transformer. Extensive experimental results indicate that UniAnimate achieves superior synthesis results over existing state-of-the-art counterparts in both quantitative and qualitative evaluations. Notably, UniAnimate can even generate highly consistent one-minute videos by iteratively employing the first frame conditioning strategy. + + +## Getting Started with UniAnimate + + +### (1) Installation + +Installation the python dependencies: + +``` +git clone https://github.com/ali-vilab/UniAnimate.git +cd UniAnimate +conda create -n UniAnimate python=3.9 +conda activate UniAnimate +conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia +pip install -r requirements.txt +``` +We also provide all the dependencies in `environment.yaml`. + +**Note**: for Windows operating system, you can refer to [this issue](https://github.com/ali-vilab/UniAnimate/issues/11) to install the dependencies. Thanks to [@zephirusgit](https://github.com/zephirusgit) for the contribution. If you encouter the problem of `The shape of the 2D attn_mask is torch.Size([77, 77]), but should be (1, 1).`, please refer to [this issue](https://github.com/ali-vilab/UniAnimate/issues/61) to solve it, thanks to [@Isi-dev](https://github.com/Isi-dev) for the contribution. + +### (2) Download the pretrained checkpoints + +Download models: +``` +!pip install modelscope +from modelscope.hub.snapshot_download import snapshot_download +model_dir = snapshot_download('iic/unianimate', cache_dir='checkpoints/') +``` +Then you might need the following command to move the checkpoints to the "checkpoints/" directory: +``` +mv ./checkpoints/iic/unianimate/* ./checkpoints/ +``` + +Finally, the model weights will be organized in `./checkpoints/` as follows: +``` +./checkpoints/ +|---- dw-ll_ucoco_384.onnx +|---- open_clip_pytorch_model.bin +|---- unianimate_16f_32f_non_ema_223000.pth +|---- v2-1_512-ema-pruned.ckpt +└---- yolox_l.onnx +``` + +### (3) Pose alignment **(Important)** + +Rescale the target pose sequence to match the pose of the reference image: +``` +# reference image 1 +python run_align_pose.py --ref_name data/images/WOMEN-Blouses_Shirts-id_00004955-01_4_full.jpg --source_video_paths data/videos/source_video.mp4 --saved_pose_dir data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full + +# reference image 2 +python run_align_pose.py --ref_name data/images/musk.jpg --source_video_paths data/videos/source_video.mp4 --saved_pose_dir data/saved_pose/musk + +# reference image 3 +python run_align_pose.py --ref_name data/images/WOMEN-Blouses_Shirts-id_00005125-03_4_full.jpg --source_video_paths data/videos/source_video.mp4 --saved_pose_dir data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full + +# reference image 4 +python run_align_pose.py --ref_name data/images/IMG_20240514_104337.jpg --source_video_paths data/videos/source_video.mp4 --saved_pose_dir data/saved_pose/IMG_20240514_104337 +``` +We have already provided the processed target pose for demo videos in ```data/saved_pose```, if you run our demo video example, this step can be skipped. In addition, you need to install onnxruntime-gpu (`pip install onnxruntime-gpu==1.13.1`) to run pose alignment on GPU. + +**✔ Some tips**: + +- > In pose alignment, the first frame in the target pose sequence is used to calculate the scale coefficient of the alignment. Therefore, if the first frame in the target pose sequence contains the entire face and pose (hand and foot), it can help obtain more accurate estimation and better video generation results. + + + +### (4) Run the UniAnimate model to generate videos + +#### (4.1) Generating video clips (32 frames with 768x512 resolution) + +Execute the following command to generate video clips: +``` +python inference.py --cfg configs/UniAnimate_infer.yaml +``` +After this, 32-frame video clips with 768x512 resolution will be generated: + + + +
+
+ + + + + + + + +
+ +
+ +
+

Click HERE to view the generated video.

+
+

Click HERE to view the generated video.

+
+ + + + + +**✔ Some tips**: + +- > To run the model, **~12G** ~~26G~~ GPU memory will be used. If your GPU is smaller than this, you can change the `max_frames: 32` in `configs/UniAnimate_infer.yaml` to other values, e.g., 24, 16, and 8. Our model is compatible with all of them. + + +#### (4.2) Generating video clips (32 frames with 1216x768 resolution) + +If you want to synthesize higher resolution results, you can change the `resolution: [512, 768]` in `configs/UniAnimate_infer.yaml` to `resolution: [768, 1216]`. And execute the following command to generate video clips: +``` +python inference.py --cfg configs/UniAnimate_infer.yaml +``` +After this, 32-frame video clips with 1216x768 resolution will be generated: + + + + +
+
+ + + + + + + + +
+ +
+ +
+

Click HERE to view the generated video.

+
+

Click HERE to view the generated video.

+
+ + + + + +**✔ Some tips**: + +- > To run the model, **~21G** ~~36G~~ GPU memory will be used. Even though our model was trained on 512x768 resolution, we observed that direct inference on 768x1216 is usually allowed and produces satisfactory results. If this results in inconsistent apparence, you can try a different seed or adjust the resolution to 512x768. + +- > Although our model was not trained on 48 or 64 frames, we found that the model generalizes well to synthesis of these lengths. + + + +In the `configs/UniAnimate_infer.yaml` configuration file, you can specify the data, adjust the video length using `max_frames`, and validate your ideas with different Diffusion settings, and so on. + + + +#### (4.3) Generating long videos + + + +If you want to synthesize videos as long as the target pose sequence, you can execute the following command to generate long videos: +``` +python inference.py --cfg configs/UniAnimate_infer_long.yaml +``` +After this, long videos with 1216x768 resolution will be generated: + + + + +
+
+ + + + + + + + +
+ +
+ +
+

Click HERE to view the generated video.

+
+

Click HERE to view the generated video.

+
+ + + + + + + + +
+
+ + + + + + + + +
+ +
+ +
+

Click HERE to view the generated video.

+
+

Click HERE to view the generated video.

+
+ + + + +In the `configs/UniAnimate_infer_long.yaml` configuration file, `test_list_path` should in the format of `[frame_interval, reference image, driving pose sequence]`, where `frame_interval=1` means that all frames in the target pose sequence will be used to generate the video, and `frame_interval=2` means that one frame is sampled every two frames. `reference image` is the location where the reference image is saved, and `driving pose sequence` is the location where the driving pose sequence is saved. + + + +**✔ Some tips**: + +- > If you find inconsistent appearance, you can change the resolution from 768x1216 to 512x768, or change the `context_overlap` from 8 to 16. +- > In the default setting of `configs/UniAnimate_infer_long.yaml`, the strategy of sliding window with temporal overlap is used. You can also generate a satisfactory video segment first, and then input the last frame of this segment into the model to generate the next segment to continue the video. + + + +## Citation + +If you find this codebase useful for your research, please cite the following paper: + +``` +@article{wang2024unianimate, + title={UniAnimate: Taming Unified Video Diffusion Models for Consistent Human Image Animation}, + author={Wang, Xiang and Zhang, Shiwei and Gao, Changxin and Wang, Jiayu and Zhou, Xiaoqiang and Zhang, Yingya and Yan, Luxin and Sang, Nong}, + journal={arXiv preprint arXiv:2406.01188}, + year={2024} +} +``` + + + +## Disclaimer + + +This open-source model is intended for RESEARCH/NON-COMMERCIAL USE ONLY. +We explicitly disclaim any responsibility for user-generated content. Users are solely liable for their actions while using the generative model. The project contributors have no legal affiliation with, nor accountability for, users' behaviors. It is imperative to use the generative model responsibly, adhering to both ethical and legal standards. diff --git a/animation/UniAnimate/assets/1.gif b/animation/UniAnimate/assets/1.gif new file mode 100644 index 0000000..30e9173 Binary files /dev/null and b/animation/UniAnimate/assets/1.gif differ diff --git a/animation/UniAnimate/assets/2.gif b/animation/UniAnimate/assets/2.gif new file mode 100644 index 0000000..1a76d8b Binary files /dev/null and b/animation/UniAnimate/assets/2.gif differ diff --git a/animation/UniAnimate/assets/3.gif b/animation/UniAnimate/assets/3.gif new file mode 100644 index 0000000..f1505d9 Binary files /dev/null and b/animation/UniAnimate/assets/3.gif differ diff --git a/animation/UniAnimate/assets/4.gif b/animation/UniAnimate/assets/4.gif new file mode 100644 index 0000000..3efab84 Binary files /dev/null and b/animation/UniAnimate/assets/4.gif differ diff --git a/animation/UniAnimate/assets/5.gif b/animation/UniAnimate/assets/5.gif new file mode 100644 index 0000000..372673f Binary files /dev/null and b/animation/UniAnimate/assets/5.gif differ diff --git a/animation/UniAnimate/assets/6.gif b/animation/UniAnimate/assets/6.gif new file mode 100644 index 0000000..d0d42d7 Binary files /dev/null and b/animation/UniAnimate/assets/6.gif differ diff --git a/animation/UniAnimate/assets/7.gif b/animation/UniAnimate/assets/7.gif new file mode 100644 index 0000000..c1fd67a Binary files /dev/null and b/animation/UniAnimate/assets/7.gif differ diff --git a/animation/UniAnimate/assets/8.gif b/animation/UniAnimate/assets/8.gif new file mode 100644 index 0000000..8bf9cfe Binary files /dev/null and b/animation/UniAnimate/assets/8.gif differ diff --git a/animation/UniAnimate/assets/network.png b/animation/UniAnimate/assets/network.png new file mode 100644 index 0000000..eaa05e1 Binary files /dev/null and b/animation/UniAnimate/assets/network.png differ diff --git a/animation/UniAnimate/assets/teaser.png b/animation/UniAnimate/assets/teaser.png new file mode 100644 index 0000000..bb8c6c8 Binary files /dev/null and b/animation/UniAnimate/assets/teaser.png differ diff --git a/animation/UniAnimate/configs/UniAnimate_infer.yaml b/animation/UniAnimate/configs/UniAnimate_infer.yaml new file mode 100644 index 0000000..0003d62 --- /dev/null +++ b/animation/UniAnimate/configs/UniAnimate_infer.yaml @@ -0,0 +1,98 @@ +# manual setting +max_frames: 32 +resolution: [512, 768] # or resolution: [768, 1216] +# resolution: [768, 1216] +round: 1 +ddim_timesteps: 30 # among 25-50 +seed: 11 # 7 +test_list_path: [ + # Format: [frame_interval, reference image, driving pose sequence] + [2, "data/images/WOMEN-Blouses_Shirts-id_00004955-01_4_full.jpg", "data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full"], + [2, "data/images/musk.jpg", "data/saved_pose/musk"], + [2, "data/images/WOMEN-Blouses_Shirts-id_00005125-03_4_full.jpg", "data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full"], + [2, "data/images/IMG_20240514_104337.jpg", "data/saved_pose/IMG_20240514_104337"] +] +partial_keys: [ + ['image','local_image', "dwpose"], # reference image as the first frame of the generated video (optional) + ['image', 'randomref', "dwpose"], + ] + + + +# default settings +TASK_TYPE: inference_unianimate_entrance +guide_scale: 2.5 +vit_resolution: [224, 224] +use_fp16: True +batch_size: 1 +latent_random_ref: True +chunk_size: 2 +decoder_bs: 2 +scale: 8 +use_fps_condition: False +test_model: checkpoints/unianimate_16f_32f_non_ema_223000.pth +embedder: { + 'type': 'FrozenOpenCLIPTextVisualEmbedder', + 'layer': 'penultimate', + 'pretrained': 'checkpoints/open_clip_pytorch_model.bin' +} + + +auto_encoder: { + 'type': 'AutoencoderKL', + 'ddconfig': { + 'double_z': True, + 'z_channels': 4, + 'resolution': 256, + 'in_channels': 3, + 'out_ch': 3, + 'ch': 128, + 'ch_mult': [1, 2, 4, 4], + 'num_res_blocks': 2, + 'attn_resolutions': [], + 'dropout': 0.0, + 'video_kernel_size': [3, 1, 1] + }, + 'embed_dim': 4, + 'pretrained': 'checkpoints/v2-1_512-ema-pruned.ckpt' +} + +UNet: { + 'type': 'UNetSD_UniAnimate', + 'config': None, + 'in_dim': 4, + 'dim': 320, + 'y_dim': 1024, + 'context_dim': 1024, + 'out_dim': 4, + 'dim_mult': [1, 2, 4, 4], + 'num_heads': 8, + 'head_dim': 64, + 'num_res_blocks': 2, + 'dropout': 0.1, + 'temporal_attention': True, + 'num_tokens': 4, + 'temporal_attn_times': 1, + 'use_checkpoint': True, + 'use_fps_condition': False, + 'use_sim_mask': False +} +video_compositions: ['image', 'local_image', 'dwpose', 'randomref', 'randomref_pose'] +Diffusion: { + 'type': 'DiffusionDDIM', + 'schedule': 'linear_sd', + 'schedule_param': { + 'num_timesteps': 1000, + "init_beta": 0.00085, + "last_beta": 0.0120, + 'zero_terminal_snr': True, + }, + 'mean_type': 'v', + 'loss_type': 'mse', + 'var_type': 'fixed_small', # 'fixed_large', + 'rescale_timesteps': False, + 'noise_strength': 0.1 +} +use_DiffusionDPM: False +CPU_CLIP_VAE: True +noise_prior_value: 949 # or 999, 949 \ No newline at end of file diff --git a/animation/UniAnimate/configs/UniAnimate_infer_long.yaml b/animation/UniAnimate/configs/UniAnimate_infer_long.yaml new file mode 100644 index 0000000..dbdd361 --- /dev/null +++ b/animation/UniAnimate/configs/UniAnimate_infer_long.yaml @@ -0,0 +1,101 @@ +# manual setting +# resolution: [512, 768] # or [768, 1216] +resolution: [768, 1216] +round: 1 +ddim_timesteps: 30 # among 25-50 +context_size: 32 +context_stride: 1 +context_overlap: 8 +seed: 7 +max_frames: "None" # 64, 96, "None" mean the length of original pose sequence +test_list_path: [ + # Format: [frame_interval, reference image, driving pose sequence] + [2, "data/images/WOMEN-Blouses_Shirts-id_00004955-01_4_full.jpg", "data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full"], + [2, "data/images/musk.jpg", "data/saved_pose/musk"], + [2, "data/images/WOMEN-Blouses_Shirts-id_00005125-03_4_full.jpg", "data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full"], + [2, "data/images/IMG_20240514_104337.jpg", "data/saved_pose/IMG_20240514_104337"], + [2, "data/images/IMG_20240514_104337.jpg", "data/saved_pose/IMG_20240514_104337_dance"], + [2, "data/images/WOMEN-Blouses_Shirts-id_00005125-03_4_full.jpg", "data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance"] +] + + +# default settings +TASK_TYPE: inference_unianimate_long_entrance +guide_scale: 2.5 +vit_resolution: [224, 224] +use_fp16: True +batch_size: 1 +latent_random_ref: True +chunk_size: 2 +decoder_bs: 2 +scale: 8 +use_fps_condition: False +test_model: checkpoints/unianimate_16f_32f_non_ema_223000.pth +partial_keys: [ + ['image', 'randomref', "dwpose"], + ] +embedder: { + 'type': 'FrozenOpenCLIPTextVisualEmbedder', + 'layer': 'penultimate', + 'pretrained': 'checkpoints/open_clip_pytorch_model.bin' +} + + +auto_encoder: { + 'type': 'AutoencoderKL', + 'ddconfig': { + 'double_z': True, + 'z_channels': 4, + 'resolution': 256, + 'in_channels': 3, + 'out_ch': 3, + 'ch': 128, + 'ch_mult': [1, 2, 4, 4], + 'num_res_blocks': 2, + 'attn_resolutions': [], + 'dropout': 0.0, + 'video_kernel_size': [3, 1, 1] + }, + 'embed_dim': 4, + 'pretrained': 'checkpoints/v2-1_512-ema-pruned.ckpt' +} + +UNet: { + 'type': 'UNetSD_UniAnimate', + 'config': None, + 'in_dim': 4, + 'dim': 320, + 'y_dim': 1024, + 'context_dim': 1024, + 'out_dim': 4, + 'dim_mult': [1, 2, 4, 4], + 'num_heads': 8, + 'head_dim': 64, + 'num_res_blocks': 2, + 'dropout': 0.1, + 'temporal_attention': True, + 'num_tokens': 4, + 'temporal_attn_times': 1, + 'use_checkpoint': True, + 'use_fps_condition': False, + 'use_sim_mask': False +} +video_compositions: ['image', 'local_image', 'dwpose', 'randomref', 'randomref_pose'] +Diffusion: { + 'type': 'DiffusionDDIMLong', + 'schedule': 'linear_sd', + 'schedule_param': { + 'num_timesteps': 1000, + "init_beta": 0.00085, + "last_beta": 0.0120, + 'zero_terminal_snr': True, + }, + 'mean_type': 'v', + 'loss_type': 'mse', + 'var_type': 'fixed_small', + 'rescale_timesteps': False, + 'noise_strength': 0.1 +} +CPU_CLIP_VAE: True +context_batch_size: 1 +noise_prior_value: 939 # or 999, 949 diff --git a/animation/UniAnimate/data/images/IMG_20240514_104337.jpg b/animation/UniAnimate/data/images/IMG_20240514_104337.jpg new file mode 100644 index 0000000..3203dd9 Binary files /dev/null and b/animation/UniAnimate/data/images/IMG_20240514_104337.jpg differ diff --git a/animation/UniAnimate/data/images/WOMEN-Blouses_Shirts-id_00004955-01_4_full.jpg b/animation/UniAnimate/data/images/WOMEN-Blouses_Shirts-id_00004955-01_4_full.jpg new file mode 100644 index 0000000..c144af0 Binary files /dev/null and b/animation/UniAnimate/data/images/WOMEN-Blouses_Shirts-id_00004955-01_4_full.jpg differ diff --git a/animation/UniAnimate/data/images/WOMEN-Blouses_Shirts-id_00005125-03_4_full.jpg b/animation/UniAnimate/data/images/WOMEN-Blouses_Shirts-id_00005125-03_4_full.jpg new file mode 100644 index 0000000..1a01d8c Binary files /dev/null and b/animation/UniAnimate/data/images/WOMEN-Blouses_Shirts-id_00005125-03_4_full.jpg differ diff --git a/animation/UniAnimate/data/images/musk.jpg b/animation/UniAnimate/data/images/musk.jpg new file mode 100644 index 0000000..29a4ac7 Binary files /dev/null and b/animation/UniAnimate/data/images/musk.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0000.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0000.jpg new file mode 100644 index 0000000..5b9cd3c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0000.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0001.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0001.jpg new file mode 100644 index 0000000..8107df0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0001.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0002.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0002.jpg new file mode 100644 index 0000000..27dfc20 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0002.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0003.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0003.jpg new file mode 100644 index 0000000..034d264 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0003.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0004.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0004.jpg new file mode 100644 index 0000000..cb7760f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0004.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0005.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0005.jpg new file mode 100644 index 0000000..93a94c1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0005.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0006.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0006.jpg new file mode 100644 index 0000000..a004d6d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0006.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0007.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0007.jpg new file mode 100644 index 0000000..b16bcd7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0007.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0008.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0008.jpg new file mode 100644 index 0000000..ed169ce Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0008.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0009.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0009.jpg new file mode 100644 index 0000000..4d1e6db Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0009.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0010.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0010.jpg new file mode 100644 index 0000000..9e881f5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0010.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0011.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0011.jpg new file mode 100644 index 0000000..e683ef8 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0011.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0012.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0012.jpg new file mode 100644 index 0000000..ddf8a03 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0012.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0013.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0013.jpg new file mode 100644 index 0000000..fc6bed5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0013.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0014.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0014.jpg new file mode 100644 index 0000000..c5d6974 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0014.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0015.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0015.jpg new file mode 100644 index 0000000..6994a3c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0015.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0016.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0016.jpg new file mode 100644 index 0000000..921c57d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0016.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0017.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0017.jpg new file mode 100644 index 0000000..aeb6c5a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0017.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0018.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0018.jpg new file mode 100644 index 0000000..225b903 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0018.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0019.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0019.jpg new file mode 100644 index 0000000..880a089 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0019.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0020.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0020.jpg new file mode 100644 index 0000000..cc2091f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0020.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0021.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0021.jpg new file mode 100644 index 0000000..838ae06 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0021.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0022.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0022.jpg new file mode 100644 index 0000000..d495728 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0022.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0023.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0023.jpg new file mode 100644 index 0000000..3c53d75 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0023.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0024.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0024.jpg new file mode 100644 index 0000000..d917e09 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0024.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0025.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0025.jpg new file mode 100644 index 0000000..8163b24 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0025.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0026.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0026.jpg new file mode 100644 index 0000000..2713696 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0026.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0027.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0027.jpg new file mode 100644 index 0000000..7db8c0f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0027.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0028.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0028.jpg new file mode 100644 index 0000000..011270a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0028.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0029.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0029.jpg new file mode 100644 index 0000000..0ebfcc9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0029.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0030.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0030.jpg new file mode 100644 index 0000000..eb34b75 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0030.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0031.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0031.jpg new file mode 100644 index 0000000..12c3f8b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0031.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0032.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0032.jpg new file mode 100644 index 0000000..e2edc54 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0032.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0033.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0033.jpg new file mode 100644 index 0000000..edb52e4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0033.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0034.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0034.jpg new file mode 100644 index 0000000..f07446e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0034.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0035.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0035.jpg new file mode 100644 index 0000000..3cc2995 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0035.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0036.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0036.jpg new file mode 100644 index 0000000..cffae24 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0036.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0037.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0037.jpg new file mode 100644 index 0000000..2b45344 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0037.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0038.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0038.jpg new file mode 100644 index 0000000..3ffd325 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0038.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0039.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0039.jpg new file mode 100644 index 0000000..11cb599 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0039.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0040.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0040.jpg new file mode 100644 index 0000000..db47b1f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0040.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0041.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0041.jpg new file mode 100644 index 0000000..7d1cfbc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0041.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0042.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0042.jpg new file mode 100644 index 0000000..db17827 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0042.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0043.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0043.jpg new file mode 100644 index 0000000..76d147f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0043.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0044.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0044.jpg new file mode 100644 index 0000000..a9a8e7f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0044.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0045.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0045.jpg new file mode 100644 index 0000000..73cb5b1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0045.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0046.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0046.jpg new file mode 100644 index 0000000..5837a22 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0046.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0047.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0047.jpg new file mode 100644 index 0000000..f466f89 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0047.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0048.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0048.jpg new file mode 100644 index 0000000..9bb5597 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0048.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0049.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0049.jpg new file mode 100644 index 0000000..fb5ece9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0049.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0050.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0050.jpg new file mode 100644 index 0000000..9642b87 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0050.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0051.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0051.jpg new file mode 100644 index 0000000..91e2a25 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0051.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0052.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0052.jpg new file mode 100644 index 0000000..e119355 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0052.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0053.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0053.jpg new file mode 100644 index 0000000..977d1fa Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0053.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0054.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0054.jpg new file mode 100644 index 0000000..a538382 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0054.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0055.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0055.jpg new file mode 100644 index 0000000..1c68506 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0055.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0056.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0056.jpg new file mode 100644 index 0000000..38e8a21 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0056.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0057.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0057.jpg new file mode 100644 index 0000000..1035cce Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0057.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0058.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0058.jpg new file mode 100644 index 0000000..05797d1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0058.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0059.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0059.jpg new file mode 100644 index 0000000..e36f195 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0059.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0060.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0060.jpg new file mode 100644 index 0000000..69ee443 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0060.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0061.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0061.jpg new file mode 100644 index 0000000..029d5f5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0061.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0062.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0062.jpg new file mode 100644 index 0000000..6ca7010 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0062.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0063.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0063.jpg new file mode 100644 index 0000000..bd52473 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0063.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0064.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0064.jpg new file mode 100644 index 0000000..10ae71c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0064.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0065.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0065.jpg new file mode 100644 index 0000000..c8fca6c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0065.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0066.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0066.jpg new file mode 100644 index 0000000..80bcf79 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0066.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0067.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0067.jpg new file mode 100644 index 0000000..0e102bd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0067.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0068.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0068.jpg new file mode 100644 index 0000000..43fdb12 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0068.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0069.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0069.jpg new file mode 100644 index 0000000..570c5f7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0069.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0070.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0070.jpg new file mode 100644 index 0000000..24d296f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0070.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0071.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0071.jpg new file mode 100644 index 0000000..29ed6bf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0071.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0072.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0072.jpg new file mode 100644 index 0000000..5380ba4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0072.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0073.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0073.jpg new file mode 100644 index 0000000..1282fbd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0073.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0074.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0074.jpg new file mode 100644 index 0000000..a57ff3a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0074.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0075.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0075.jpg new file mode 100644 index 0000000..b4963ef Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0075.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0076.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0076.jpg new file mode 100644 index 0000000..7914881 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0076.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0077.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0077.jpg new file mode 100644 index 0000000..ee729cc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0077.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0078.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0078.jpg new file mode 100644 index 0000000..e53d34b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0078.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0079.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0079.jpg new file mode 100644 index 0000000..d9f644c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0079.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0080.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0080.jpg new file mode 100644 index 0000000..df1802f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0080.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0081.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0081.jpg new file mode 100644 index 0000000..2c9a9f8 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0081.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0082.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0082.jpg new file mode 100644 index 0000000..9844a98 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0082.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0083.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0083.jpg new file mode 100644 index 0000000..e908c4f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0083.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0084.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0084.jpg new file mode 100644 index 0000000..c1a1bec Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0084.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0085.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0085.jpg new file mode 100644 index 0000000..546f219 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0085.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0086.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0086.jpg new file mode 100644 index 0000000..7d980fe Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0086.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0087.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0087.jpg new file mode 100644 index 0000000..bbd42a1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0087.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0088.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0088.jpg new file mode 100644 index 0000000..f41ba1b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0088.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0089.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0089.jpg new file mode 100644 index 0000000..7f96b14 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0089.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0090.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0090.jpg new file mode 100644 index 0000000..8f391ac Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0090.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0091.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0091.jpg new file mode 100644 index 0000000..bcef82d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0091.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0092.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0092.jpg new file mode 100644 index 0000000..9d87418 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0092.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0093.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0093.jpg new file mode 100644 index 0000000..b20ad16 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0093.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0094.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0094.jpg new file mode 100644 index 0000000..157937e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0094.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0095.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0095.jpg new file mode 100644 index 0000000..4994b5e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0095.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0096.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0096.jpg new file mode 100644 index 0000000..831afea Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0096.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0097.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0097.jpg new file mode 100644 index 0000000..5ddd265 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0097.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0098.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0098.jpg new file mode 100644 index 0000000..22602b4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0098.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0099.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0099.jpg new file mode 100644 index 0000000..396ecc4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0099.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0100.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0100.jpg new file mode 100644 index 0000000..81db242 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0100.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0101.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0101.jpg new file mode 100644 index 0000000..45dbb3f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0101.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0102.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0102.jpg new file mode 100644 index 0000000..6ca4a4e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0102.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0103.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0103.jpg new file mode 100644 index 0000000..8b90764 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0103.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0104.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0104.jpg new file mode 100644 index 0000000..363f75d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0104.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0105.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0105.jpg new file mode 100644 index 0000000..28fac25 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0105.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0106.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0106.jpg new file mode 100644 index 0000000..04dcd4b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0106.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0107.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0107.jpg new file mode 100644 index 0000000..a8818ea Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0107.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0108.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0108.jpg new file mode 100644 index 0000000..e1ff1de Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0108.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0109.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0109.jpg new file mode 100644 index 0000000..29780ff Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0109.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0110.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0110.jpg new file mode 100644 index 0000000..b6c4b9c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0110.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0111.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0111.jpg new file mode 100644 index 0000000..9bcd845 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0111.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0112.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0112.jpg new file mode 100644 index 0000000..14c2974 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0112.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0113.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0113.jpg new file mode 100644 index 0000000..b83888c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0113.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0114.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0114.jpg new file mode 100644 index 0000000..91a6344 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0114.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0115.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0115.jpg new file mode 100644 index 0000000..6f2ffa5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0115.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0116.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0116.jpg new file mode 100644 index 0000000..d7ac119 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0116.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0117.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0117.jpg new file mode 100644 index 0000000..072e2ca Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0117.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0118.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0118.jpg new file mode 100644 index 0000000..857342d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0118.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0119.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0119.jpg new file mode 100644 index 0000000..c885911 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0119.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0120.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0120.jpg new file mode 100644 index 0000000..1fb2726 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0120.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0121.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0121.jpg new file mode 100644 index 0000000..f3eb3db Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0121.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0122.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0122.jpg new file mode 100644 index 0000000..7c81c20 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0122.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0123.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0123.jpg new file mode 100644 index 0000000..ba9c83e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0123.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0124.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0124.jpg new file mode 100644 index 0000000..00aee06 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0124.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0125.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0125.jpg new file mode 100644 index 0000000..2ee0e0f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0125.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0126.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0126.jpg new file mode 100644 index 0000000..fdc47f9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0126.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0127.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0127.jpg new file mode 100644 index 0000000..46e7b70 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0127.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0128.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0128.jpg new file mode 100644 index 0000000..48e6d01 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0128.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0129.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0129.jpg new file mode 100644 index 0000000..19e23df Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0129.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0130.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0130.jpg new file mode 100644 index 0000000..56224a5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0130.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0131.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0131.jpg new file mode 100644 index 0000000..f8b90fc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0131.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0132.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0132.jpg new file mode 100644 index 0000000..d324a41 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0132.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0133.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0133.jpg new file mode 100644 index 0000000..a53266f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0133.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0134.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0134.jpg new file mode 100644 index 0000000..e1e2a29 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0134.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0135.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0135.jpg new file mode 100644 index 0000000..c8632fb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0135.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0136.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0136.jpg new file mode 100644 index 0000000..4b6328e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0136.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0137.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0137.jpg new file mode 100644 index 0000000..8b893db Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0137.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0138.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0138.jpg new file mode 100644 index 0000000..75b8c1a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0138.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0139.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0139.jpg new file mode 100644 index 0000000..0a665e6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0139.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0140.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0140.jpg new file mode 100644 index 0000000..4cfb691 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0140.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0141.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0141.jpg new file mode 100644 index 0000000..72b73e2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0141.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0142.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0142.jpg new file mode 100644 index 0000000..cc51839 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0142.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0143.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0143.jpg new file mode 100644 index 0000000..d98d12a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0143.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0144.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0144.jpg new file mode 100644 index 0000000..63c8da3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0144.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0145.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0145.jpg new file mode 100644 index 0000000..fa6434c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0145.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0146.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0146.jpg new file mode 100644 index 0000000..e6ce906 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0146.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0147.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0147.jpg new file mode 100644 index 0000000..79f3fe9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0147.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0148.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0148.jpg new file mode 100644 index 0000000..bd3df32 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0148.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0149.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0149.jpg new file mode 100644 index 0000000..38610e7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/0149.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/ref_pose.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/ref_pose.jpg new file mode 100644 index 0000000..e0a9f7a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337/ref_pose.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0000.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0000.jpg new file mode 100644 index 0000000..5ded0ca Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0000.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0001.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0001.jpg new file mode 100644 index 0000000..68f9e9f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0001.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0002.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0002.jpg new file mode 100644 index 0000000..0d31106 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0002.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0003.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0003.jpg new file mode 100644 index 0000000..eb9f420 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0003.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0004.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0004.jpg new file mode 100644 index 0000000..be62c16 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0004.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0005.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0005.jpg new file mode 100644 index 0000000..65f6b6c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0005.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0006.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0006.jpg new file mode 100644 index 0000000..41b00a1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0006.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0007.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0007.jpg new file mode 100644 index 0000000..167e1b2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0007.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0008.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0008.jpg new file mode 100644 index 0000000..2c7ecd7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0008.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0009.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0009.jpg new file mode 100644 index 0000000..e18147d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0009.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0010.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0010.jpg new file mode 100644 index 0000000..155726f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0010.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0011.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0011.jpg new file mode 100644 index 0000000..87d3fb3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0011.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0012.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0012.jpg new file mode 100644 index 0000000..73911b2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0012.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0013.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0013.jpg new file mode 100644 index 0000000..f4dd581 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0013.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0014.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0014.jpg new file mode 100644 index 0000000..8af758f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0014.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0015.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0015.jpg new file mode 100644 index 0000000..dcaf9c8 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0015.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0016.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0016.jpg new file mode 100644 index 0000000..2155794 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0016.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0017.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0017.jpg new file mode 100644 index 0000000..972a609 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0017.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0018.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0018.jpg new file mode 100644 index 0000000..0043dd3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0018.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0019.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0019.jpg new file mode 100644 index 0000000..eddc81b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0019.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0020.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0020.jpg new file mode 100644 index 0000000..c401d19 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0020.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0021.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0021.jpg new file mode 100644 index 0000000..5f4ebbf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0021.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0022.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0022.jpg new file mode 100644 index 0000000..69a0087 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0022.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0023.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0023.jpg new file mode 100644 index 0000000..eb1ffed Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0023.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0024.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0024.jpg new file mode 100644 index 0000000..fe71e8b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0024.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0025.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0025.jpg new file mode 100644 index 0000000..7bd68e1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0025.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0026.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0026.jpg new file mode 100644 index 0000000..90a5c79 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0026.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0027.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0027.jpg new file mode 100644 index 0000000..7e5613d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0027.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0028.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0028.jpg new file mode 100644 index 0000000..3969ae8 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0028.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0029.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0029.jpg new file mode 100644 index 0000000..b181edb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0029.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0030.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0030.jpg new file mode 100644 index 0000000..2f8e791 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0030.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0031.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0031.jpg new file mode 100644 index 0000000..186ac84 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0031.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0032.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0032.jpg new file mode 100644 index 0000000..074b8a4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0032.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0033.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0033.jpg new file mode 100644 index 0000000..95531dc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0033.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0034.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0034.jpg new file mode 100644 index 0000000..4463d1b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0034.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0035.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0035.jpg new file mode 100644 index 0000000..f921d66 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0035.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0036.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0036.jpg new file mode 100644 index 0000000..bd28aaf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0036.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0037.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0037.jpg new file mode 100644 index 0000000..83b5099 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0037.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0038.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0038.jpg new file mode 100644 index 0000000..ffb153e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0038.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0039.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0039.jpg new file mode 100644 index 0000000..4f22fe0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0039.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0040.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0040.jpg new file mode 100644 index 0000000..4e57bc9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0040.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0041.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0041.jpg new file mode 100644 index 0000000..47d51db Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0041.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0042.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0042.jpg new file mode 100644 index 0000000..7fbdc88 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0042.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0043.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0043.jpg new file mode 100644 index 0000000..8040849 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0043.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0044.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0044.jpg new file mode 100644 index 0000000..6e0799e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0044.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0045.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0045.jpg new file mode 100644 index 0000000..f0ee09d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0045.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0046.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0046.jpg new file mode 100644 index 0000000..ea5a3b5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0046.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0047.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0047.jpg new file mode 100644 index 0000000..9d92ba8 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0047.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0048.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0048.jpg new file mode 100644 index 0000000..2077f60 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0048.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0049.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0049.jpg new file mode 100644 index 0000000..8ec3bb7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0049.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0050.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0050.jpg new file mode 100644 index 0000000..cfc6d8f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0050.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0051.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0051.jpg new file mode 100644 index 0000000..c2414db Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0051.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0052.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0052.jpg new file mode 100644 index 0000000..84ee9b5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0052.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0053.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0053.jpg new file mode 100644 index 0000000..32d39ee Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0053.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0054.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0054.jpg new file mode 100644 index 0000000..226ba7f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0054.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0055.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0055.jpg new file mode 100644 index 0000000..517435b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0055.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0056.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0056.jpg new file mode 100644 index 0000000..f09a849 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0056.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0057.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0057.jpg new file mode 100644 index 0000000..5ab3792 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0057.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0058.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0058.jpg new file mode 100644 index 0000000..ef1c03e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0058.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0059.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0059.jpg new file mode 100644 index 0000000..5680f79 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0059.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0060.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0060.jpg new file mode 100644 index 0000000..8fa3156 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0060.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0061.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0061.jpg new file mode 100644 index 0000000..058c12b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0061.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0062.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0062.jpg new file mode 100644 index 0000000..738f579 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0062.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0063.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0063.jpg new file mode 100644 index 0000000..fafc9c2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0063.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0064.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0064.jpg new file mode 100644 index 0000000..43a9ebd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0064.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0065.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0065.jpg new file mode 100644 index 0000000..3808f8b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0065.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0066.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0066.jpg new file mode 100644 index 0000000..67778cb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0066.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0067.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0067.jpg new file mode 100644 index 0000000..7c1ccaa Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0067.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0068.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0068.jpg new file mode 100644 index 0000000..3001a85 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0068.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0069.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0069.jpg new file mode 100644 index 0000000..30b0fbc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0069.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0070.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0070.jpg new file mode 100644 index 0000000..51701cd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0070.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0071.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0071.jpg new file mode 100644 index 0000000..32621e1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0071.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0072.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0072.jpg new file mode 100644 index 0000000..3d91062 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0072.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0073.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0073.jpg new file mode 100644 index 0000000..e587b82 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0073.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0074.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0074.jpg new file mode 100644 index 0000000..beacd15 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0074.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0075.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0075.jpg new file mode 100644 index 0000000..25eb379 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0075.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0076.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0076.jpg new file mode 100644 index 0000000..4303ddc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0076.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0077.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0077.jpg new file mode 100644 index 0000000..a607543 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0077.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0078.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0078.jpg new file mode 100644 index 0000000..e5cc842 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0078.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0079.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0079.jpg new file mode 100644 index 0000000..3b2cdbe Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0079.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0080.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0080.jpg new file mode 100644 index 0000000..b68ea38 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0080.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0081.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0081.jpg new file mode 100644 index 0000000..8bdcbbe Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0081.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0082.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0082.jpg new file mode 100644 index 0000000..d43c3fc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0082.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0083.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0083.jpg new file mode 100644 index 0000000..29cc787 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0083.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0084.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0084.jpg new file mode 100644 index 0000000..56c8f8e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0084.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0085.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0085.jpg new file mode 100644 index 0000000..efdac38 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0085.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0086.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0086.jpg new file mode 100644 index 0000000..7f196a1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0086.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0087.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0087.jpg new file mode 100644 index 0000000..c916715 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0087.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0088.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0088.jpg new file mode 100644 index 0000000..37a08e6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0088.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0089.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0089.jpg new file mode 100644 index 0000000..1b7b58a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0089.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0090.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0090.jpg new file mode 100644 index 0000000..0a4902f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0090.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0091.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0091.jpg new file mode 100644 index 0000000..aeb741b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0091.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0092.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0092.jpg new file mode 100644 index 0000000..7a0c58b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0092.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0093.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0093.jpg new file mode 100644 index 0000000..182e167 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0093.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0094.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0094.jpg new file mode 100644 index 0000000..b419556 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0094.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0095.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0095.jpg new file mode 100644 index 0000000..12c8cc4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0095.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0096.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0096.jpg new file mode 100644 index 0000000..45d2e3b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0096.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0097.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0097.jpg new file mode 100644 index 0000000..e19c2bb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0097.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0098.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0098.jpg new file mode 100644 index 0000000..cda6014 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0098.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0099.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0099.jpg new file mode 100644 index 0000000..c26af76 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0099.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0100.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0100.jpg new file mode 100644 index 0000000..f322ffc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0100.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0101.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0101.jpg new file mode 100644 index 0000000..1207983 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0101.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0102.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0102.jpg new file mode 100644 index 0000000..cc46875 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0102.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0103.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0103.jpg new file mode 100644 index 0000000..b388f49 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0103.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0104.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0104.jpg new file mode 100644 index 0000000..8245c81 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0104.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0105.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0105.jpg new file mode 100644 index 0000000..fa826f3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0105.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0106.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0106.jpg new file mode 100644 index 0000000..e9e7dd3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0106.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0107.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0107.jpg new file mode 100644 index 0000000..123f938 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0107.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0108.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0108.jpg new file mode 100644 index 0000000..97a3a51 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0108.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0109.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0109.jpg new file mode 100644 index 0000000..981b479 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0109.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0110.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0110.jpg new file mode 100644 index 0000000..493b003 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0110.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0111.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0111.jpg new file mode 100644 index 0000000..96e6e7a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0111.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0112.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0112.jpg new file mode 100644 index 0000000..502343d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0112.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0113.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0113.jpg new file mode 100644 index 0000000..9bdf669 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0113.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0114.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0114.jpg new file mode 100644 index 0000000..4daa6b4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0114.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0115.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0115.jpg new file mode 100644 index 0000000..28f23ec Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0115.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0116.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0116.jpg new file mode 100644 index 0000000..f4c3d4a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0116.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0117.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0117.jpg new file mode 100644 index 0000000..bc2800c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0117.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0118.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0118.jpg new file mode 100644 index 0000000..7c533b4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0118.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0119.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0119.jpg new file mode 100644 index 0000000..a86dc07 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0119.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0120.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0120.jpg new file mode 100644 index 0000000..0c3d312 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0120.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0121.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0121.jpg new file mode 100644 index 0000000..8fe2767 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0121.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0122.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0122.jpg new file mode 100644 index 0000000..780e91c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0122.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0123.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0123.jpg new file mode 100644 index 0000000..0a1e10a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0123.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0124.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0124.jpg new file mode 100644 index 0000000..18ac12d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0124.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0125.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0125.jpg new file mode 100644 index 0000000..948b861 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0125.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0126.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0126.jpg new file mode 100644 index 0000000..9f7fbc3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0126.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0127.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0127.jpg new file mode 100644 index 0000000..02be6ff Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0127.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0128.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0128.jpg new file mode 100644 index 0000000..f00c4ab Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0128.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0129.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0129.jpg new file mode 100644 index 0000000..6663713 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0129.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0130.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0130.jpg new file mode 100644 index 0000000..67711d7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0130.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0131.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0131.jpg new file mode 100644 index 0000000..49f9b4d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0131.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0132.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0132.jpg new file mode 100644 index 0000000..e9111cd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0132.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0133.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0133.jpg new file mode 100644 index 0000000..fffffb8 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0133.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0134.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0134.jpg new file mode 100644 index 0000000..e41b78d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0134.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0135.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0135.jpg new file mode 100644 index 0000000..017eb6a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0135.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0136.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0136.jpg new file mode 100644 index 0000000..d44ead7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0136.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0137.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0137.jpg new file mode 100644 index 0000000..4403a7d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0137.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0138.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0138.jpg new file mode 100644 index 0000000..ac83cda Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0138.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0139.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0139.jpg new file mode 100644 index 0000000..bafef32 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0139.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0140.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0140.jpg new file mode 100644 index 0000000..f87814b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0140.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0141.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0141.jpg new file mode 100644 index 0000000..4d2e1f5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0141.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0142.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0142.jpg new file mode 100644 index 0000000..e99ec0e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0142.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0143.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0143.jpg new file mode 100644 index 0000000..f7077c7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0143.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0144.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0144.jpg new file mode 100644 index 0000000..427d7c5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0144.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0145.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0145.jpg new file mode 100644 index 0000000..0d61674 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0145.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0146.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0146.jpg new file mode 100644 index 0000000..c6facfe Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0146.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0147.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0147.jpg new file mode 100644 index 0000000..0060f1c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0147.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0148.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0148.jpg new file mode 100644 index 0000000..c58b0eb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0148.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0149.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0149.jpg new file mode 100644 index 0000000..dca6d36 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0149.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0150.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0150.jpg new file mode 100644 index 0000000..cf819df Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0150.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0151.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0151.jpg new file mode 100644 index 0000000..094fe5e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0151.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0152.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0152.jpg new file mode 100644 index 0000000..71f9d05 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0152.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0153.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0153.jpg new file mode 100644 index 0000000..795eb31 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0153.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0154.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0154.jpg new file mode 100644 index 0000000..23b1b94 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0154.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0155.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0155.jpg new file mode 100644 index 0000000..e15a3d2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0155.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0156.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0156.jpg new file mode 100644 index 0000000..8a7d086 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0156.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0157.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0157.jpg new file mode 100644 index 0000000..ba4090e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0157.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0158.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0158.jpg new file mode 100644 index 0000000..099173c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0158.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0159.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0159.jpg new file mode 100644 index 0000000..3037509 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0159.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0160.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0160.jpg new file mode 100644 index 0000000..20792cb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0160.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0161.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0161.jpg new file mode 100644 index 0000000..1626596 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0161.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0162.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0162.jpg new file mode 100644 index 0000000..6bb5f1a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0162.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0163.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0163.jpg new file mode 100644 index 0000000..0a1c0f0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0163.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0164.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0164.jpg new file mode 100644 index 0000000..db54b5e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0164.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0165.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0165.jpg new file mode 100644 index 0000000..023bf0c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0165.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0166.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0166.jpg new file mode 100644 index 0000000..ee3655f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0166.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0167.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0167.jpg new file mode 100644 index 0000000..02798a9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0167.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0168.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0168.jpg new file mode 100644 index 0000000..8032b81 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0168.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0169.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0169.jpg new file mode 100644 index 0000000..4b1f446 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0169.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0170.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0170.jpg new file mode 100644 index 0000000..87fd0f4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0170.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0171.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0171.jpg new file mode 100644 index 0000000..f2c7c6d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0171.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0172.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0172.jpg new file mode 100644 index 0000000..9c393d7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0172.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0173.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0173.jpg new file mode 100644 index 0000000..825811a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0173.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0174.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0174.jpg new file mode 100644 index 0000000..9fad7f1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0174.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0175.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0175.jpg new file mode 100644 index 0000000..7aeb1e2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0175.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0176.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0176.jpg new file mode 100644 index 0000000..8c4b751 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0176.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0177.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0177.jpg new file mode 100644 index 0000000..76971ea Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0177.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0178.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0178.jpg new file mode 100644 index 0000000..2fb817d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0178.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0179.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0179.jpg new file mode 100644 index 0000000..bb0356f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0179.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0180.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0180.jpg new file mode 100644 index 0000000..944e55b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0180.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0181.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0181.jpg new file mode 100644 index 0000000..f86adc0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0181.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0182.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0182.jpg new file mode 100644 index 0000000..59b62af Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0182.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0183.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0183.jpg new file mode 100644 index 0000000..dfd1cc4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0183.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0184.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0184.jpg new file mode 100644 index 0000000..0df0f05 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0184.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0185.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0185.jpg new file mode 100644 index 0000000..c31fb1b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0185.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0186.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0186.jpg new file mode 100644 index 0000000..904e3ba Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0186.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0187.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0187.jpg new file mode 100644 index 0000000..7f7b146 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0187.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0188.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0188.jpg new file mode 100644 index 0000000..8072987 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0188.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0189.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0189.jpg new file mode 100644 index 0000000..31d8c8c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0189.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0190.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0190.jpg new file mode 100644 index 0000000..7961d31 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0190.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0191.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0191.jpg new file mode 100644 index 0000000..9117f90 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0191.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0192.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0192.jpg new file mode 100644 index 0000000..29bc3cc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0192.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0193.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0193.jpg new file mode 100644 index 0000000..7edf57f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0193.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0194.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0194.jpg new file mode 100644 index 0000000..68f831e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0194.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0195.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0195.jpg new file mode 100644 index 0000000..44682fa Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0195.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0196.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0196.jpg new file mode 100644 index 0000000..c96d96d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0196.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0197.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0197.jpg new file mode 100644 index 0000000..4293bae Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0197.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0198.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0198.jpg new file mode 100644 index 0000000..df2b6c0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0198.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0199.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0199.jpg new file mode 100644 index 0000000..f7ba61e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0199.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0200.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0200.jpg new file mode 100644 index 0000000..e61383a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0200.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0201.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0201.jpg new file mode 100644 index 0000000..5dc291c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0201.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0202.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0202.jpg new file mode 100644 index 0000000..3918374 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0202.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0203.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0203.jpg new file mode 100644 index 0000000..3f6dde4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0203.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0204.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0204.jpg new file mode 100644 index 0000000..ecbf213 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0204.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0205.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0205.jpg new file mode 100644 index 0000000..e4505f8 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0205.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0206.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0206.jpg new file mode 100644 index 0000000..13adade Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0206.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0207.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0207.jpg new file mode 100644 index 0000000..4d95fb3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0207.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0208.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0208.jpg new file mode 100644 index 0000000..9a0d3fb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0208.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0209.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0209.jpg new file mode 100644 index 0000000..1a4f495 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0209.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0210.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0210.jpg new file mode 100644 index 0000000..918c321 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0210.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0211.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0211.jpg new file mode 100644 index 0000000..6417769 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0211.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0212.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0212.jpg new file mode 100644 index 0000000..f65265b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0212.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0213.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0213.jpg new file mode 100644 index 0000000..32566ca Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0213.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0214.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0214.jpg new file mode 100644 index 0000000..86b1dd4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0214.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0215.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0215.jpg new file mode 100644 index 0000000..75ed397 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0215.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0216.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0216.jpg new file mode 100644 index 0000000..296dadb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0216.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0217.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0217.jpg new file mode 100644 index 0000000..fb9c4f3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0217.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0218.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0218.jpg new file mode 100644 index 0000000..bbcc05e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0218.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0219.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0219.jpg new file mode 100644 index 0000000..907a162 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0219.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0220.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0220.jpg new file mode 100644 index 0000000..d0aa04f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0220.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0221.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0221.jpg new file mode 100644 index 0000000..e3be22d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0221.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0222.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0222.jpg new file mode 100644 index 0000000..7098dad Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0222.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0223.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0223.jpg new file mode 100644 index 0000000..0b8de1e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0223.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0224.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0224.jpg new file mode 100644 index 0000000..053b95e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0224.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0225.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0225.jpg new file mode 100644 index 0000000..593f457 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0225.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0226.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0226.jpg new file mode 100644 index 0000000..f9a42de Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0226.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0227.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0227.jpg new file mode 100644 index 0000000..2fa7014 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0227.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0228.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0228.jpg new file mode 100644 index 0000000..4ebee94 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0228.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0229.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0229.jpg new file mode 100644 index 0000000..fbdbe19 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0229.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0230.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0230.jpg new file mode 100644 index 0000000..02bed50 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0230.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0231.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0231.jpg new file mode 100644 index 0000000..11fc59d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0231.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0232.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0232.jpg new file mode 100644 index 0000000..b945785 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0232.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0233.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0233.jpg new file mode 100644 index 0000000..977afb2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0233.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0234.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0234.jpg new file mode 100644 index 0000000..c4a60e0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0234.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0235.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0235.jpg new file mode 100644 index 0000000..ae090ab Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0235.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0236.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0236.jpg new file mode 100644 index 0000000..d1d5bb2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0236.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0237.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0237.jpg new file mode 100644 index 0000000..6d85a94 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0237.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0238.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0238.jpg new file mode 100644 index 0000000..0ea7223 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0238.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0239.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0239.jpg new file mode 100644 index 0000000..6a7f973 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0239.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0240.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0240.jpg new file mode 100644 index 0000000..99a3bd4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0240.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0241.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0241.jpg new file mode 100644 index 0000000..24505f2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0241.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0242.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0242.jpg new file mode 100644 index 0000000..8eab235 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0242.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0243.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0243.jpg new file mode 100644 index 0000000..29e6d48 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0243.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0244.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0244.jpg new file mode 100644 index 0000000..00da99e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0244.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0245.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0245.jpg new file mode 100644 index 0000000..1560b8f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0245.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0246.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0246.jpg new file mode 100644 index 0000000..401beff Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0246.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0247.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0247.jpg new file mode 100644 index 0000000..ca5a046 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0247.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0248.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0248.jpg new file mode 100644 index 0000000..e11df3c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0248.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0249.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0249.jpg new file mode 100644 index 0000000..76e61f4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0249.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0250.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0250.jpg new file mode 100644 index 0000000..f5521c1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0250.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0251.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0251.jpg new file mode 100644 index 0000000..01bd85c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0251.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0252.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0252.jpg new file mode 100644 index 0000000..ce56383 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0252.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0253.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0253.jpg new file mode 100644 index 0000000..3bc100d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0253.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0254.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0254.jpg new file mode 100644 index 0000000..aad0a72 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0254.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0255.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0255.jpg new file mode 100644 index 0000000..f072618 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0255.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0256.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0256.jpg new file mode 100644 index 0000000..da65124 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0256.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0257.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0257.jpg new file mode 100644 index 0000000..2f0b0f9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0257.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0258.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0258.jpg new file mode 100644 index 0000000..9a2bbe7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0258.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0259.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0259.jpg new file mode 100644 index 0000000..0eacfb0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0259.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0260.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0260.jpg new file mode 100644 index 0000000..82a1f98 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0260.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0261.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0261.jpg new file mode 100644 index 0000000..6a69a04 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0261.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0262.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0262.jpg new file mode 100644 index 0000000..053017d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0262.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0263.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0263.jpg new file mode 100644 index 0000000..3876938 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0263.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0264.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0264.jpg new file mode 100644 index 0000000..c128145 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0264.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0265.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0265.jpg new file mode 100644 index 0000000..96b5c0c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0265.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0266.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0266.jpg new file mode 100644 index 0000000..384f3f9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0266.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0267.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0267.jpg new file mode 100644 index 0000000..6d0ea3a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0267.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0268.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0268.jpg new file mode 100644 index 0000000..fbffd49 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0268.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0269.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0269.jpg new file mode 100644 index 0000000..9512613 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0269.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0270.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0270.jpg new file mode 100644 index 0000000..990d657 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0270.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0271.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0271.jpg new file mode 100644 index 0000000..2d8e125 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0271.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0272.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0272.jpg new file mode 100644 index 0000000..bdffe42 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0272.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0273.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0273.jpg new file mode 100644 index 0000000..1141637 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0273.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0274.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0274.jpg new file mode 100644 index 0000000..be6e5da Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0274.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0275.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0275.jpg new file mode 100644 index 0000000..3947dd9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0275.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0276.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0276.jpg new file mode 100644 index 0000000..d7d6f89 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0276.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0277.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0277.jpg new file mode 100644 index 0000000..0974c9e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0277.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0278.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0278.jpg new file mode 100644 index 0000000..32b9488 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0278.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0279.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0279.jpg new file mode 100644 index 0000000..6fe8dc1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/0279.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/ref_pose.jpg b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/ref_pose.jpg new file mode 100644 index 0000000..e0a9f7a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/IMG_20240514_104337_dance/ref_pose.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0000.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0000.jpg new file mode 100644 index 0000000..e06a18a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0000.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0001.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0001.jpg new file mode 100644 index 0000000..40e4b1e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0001.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0002.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0002.jpg new file mode 100644 index 0000000..be885e1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0002.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0003.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0003.jpg new file mode 100644 index 0000000..9f88b98 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0003.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0004.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0004.jpg new file mode 100644 index 0000000..1adbb1d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0004.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0005.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0005.jpg new file mode 100644 index 0000000..b0ec425 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0005.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0006.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0006.jpg new file mode 100644 index 0000000..a9b2b75 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0006.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0007.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0007.jpg new file mode 100644 index 0000000..1bb1e73 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0007.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0008.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0008.jpg new file mode 100644 index 0000000..ccfe147 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0008.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0009.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0009.jpg new file mode 100644 index 0000000..5a60b62 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0009.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0010.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0010.jpg new file mode 100644 index 0000000..c5dfd9d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0010.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0011.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0011.jpg new file mode 100644 index 0000000..81b5dcd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0011.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0012.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0012.jpg new file mode 100644 index 0000000..bfddae7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0012.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0013.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0013.jpg new file mode 100644 index 0000000..98f8cb3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0013.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0014.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0014.jpg new file mode 100644 index 0000000..b99a3b0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0014.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0015.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0015.jpg new file mode 100644 index 0000000..ed90d53 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0015.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0016.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0016.jpg new file mode 100644 index 0000000..e2428e6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0016.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0017.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0017.jpg new file mode 100644 index 0000000..4ff9dc0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0017.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0018.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0018.jpg new file mode 100644 index 0000000..bf4fc27 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0018.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0019.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0019.jpg new file mode 100644 index 0000000..54fc7f9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0019.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0020.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0020.jpg new file mode 100644 index 0000000..712511a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0020.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0021.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0021.jpg new file mode 100644 index 0000000..7137aa2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0021.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0022.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0022.jpg new file mode 100644 index 0000000..709b573 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0022.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0023.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0023.jpg new file mode 100644 index 0000000..91bbb80 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0023.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0024.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0024.jpg new file mode 100644 index 0000000..ebc8246 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0024.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0025.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0025.jpg new file mode 100644 index 0000000..0c22d59 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0025.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0026.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0026.jpg new file mode 100644 index 0000000..30f7ea2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0026.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0027.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0027.jpg new file mode 100644 index 0000000..e05cf74 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0027.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0028.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0028.jpg new file mode 100644 index 0000000..afa06d1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0028.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0029.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0029.jpg new file mode 100644 index 0000000..1fb374d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0029.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0030.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0030.jpg new file mode 100644 index 0000000..84bb942 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0030.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0031.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0031.jpg new file mode 100644 index 0000000..d575900 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0031.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0032.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0032.jpg new file mode 100644 index 0000000..0cb53aa Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0032.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0033.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0033.jpg new file mode 100644 index 0000000..a3dec33 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0033.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0034.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0034.jpg new file mode 100644 index 0000000..a2b6d8e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0034.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0035.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0035.jpg new file mode 100644 index 0000000..da01372 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0035.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0036.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0036.jpg new file mode 100644 index 0000000..5cb0060 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0036.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0037.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0037.jpg new file mode 100644 index 0000000..763296a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0037.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0038.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0038.jpg new file mode 100644 index 0000000..77eb80c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0038.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0039.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0039.jpg new file mode 100644 index 0000000..6821462 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0039.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0040.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0040.jpg new file mode 100644 index 0000000..14446de Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0040.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0041.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0041.jpg new file mode 100644 index 0000000..4ddbe42 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0041.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0042.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0042.jpg new file mode 100644 index 0000000..aa1d826 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0042.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0043.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0043.jpg new file mode 100644 index 0000000..d66200d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0043.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0044.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0044.jpg new file mode 100644 index 0000000..32996f3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0044.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0045.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0045.jpg new file mode 100644 index 0000000..e89a391 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0045.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0046.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0046.jpg new file mode 100644 index 0000000..f4dd427 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0046.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0047.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0047.jpg new file mode 100644 index 0000000..6832111 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0047.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0048.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0048.jpg new file mode 100644 index 0000000..ae6a58d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0048.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0049.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0049.jpg new file mode 100644 index 0000000..b87b97f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0049.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0050.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0050.jpg new file mode 100644 index 0000000..9356943 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0050.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0051.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0051.jpg new file mode 100644 index 0000000..4fa1a7e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0051.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0052.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0052.jpg new file mode 100644 index 0000000..98d7d8f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0052.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0053.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0053.jpg new file mode 100644 index 0000000..0585e25 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0053.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0054.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0054.jpg new file mode 100644 index 0000000..b764e66 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0054.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0055.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0055.jpg new file mode 100644 index 0000000..cfabcba Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0055.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0056.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0056.jpg new file mode 100644 index 0000000..330d00d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0056.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0057.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0057.jpg new file mode 100644 index 0000000..69b389a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0057.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0058.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0058.jpg new file mode 100644 index 0000000..2d80cc6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0058.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0059.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0059.jpg new file mode 100644 index 0000000..c391a29 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0059.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0060.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0060.jpg new file mode 100644 index 0000000..27fd339 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0060.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0061.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0061.jpg new file mode 100644 index 0000000..f4873ad Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0061.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0062.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0062.jpg new file mode 100644 index 0000000..e258402 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0062.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0063.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0063.jpg new file mode 100644 index 0000000..49399e2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0063.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0064.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0064.jpg new file mode 100644 index 0000000..e932a6c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0064.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0065.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0065.jpg new file mode 100644 index 0000000..bd5bcfb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0065.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0066.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0066.jpg new file mode 100644 index 0000000..e7d4b39 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0066.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0067.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0067.jpg new file mode 100644 index 0000000..2c51e5c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0067.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0068.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0068.jpg new file mode 100644 index 0000000..0de194f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0068.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0069.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0069.jpg new file mode 100644 index 0000000..ec83754 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0069.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0070.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0070.jpg new file mode 100644 index 0000000..d9b8793 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0070.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0071.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0071.jpg new file mode 100644 index 0000000..5989caf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0071.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0072.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0072.jpg new file mode 100644 index 0000000..075e2db Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0072.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0073.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0073.jpg new file mode 100644 index 0000000..5ea11e3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0073.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0074.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0074.jpg new file mode 100644 index 0000000..fe0649e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0074.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0075.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0075.jpg new file mode 100644 index 0000000..aec5ef2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0075.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0076.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0076.jpg new file mode 100644 index 0000000..38aa139 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0076.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0077.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0077.jpg new file mode 100644 index 0000000..380969e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0077.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0078.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0078.jpg new file mode 100644 index 0000000..64c8f7f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0078.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0079.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0079.jpg new file mode 100644 index 0000000..300d800 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0079.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0080.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0080.jpg new file mode 100644 index 0000000..f389c2a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0080.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0081.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0081.jpg new file mode 100644 index 0000000..c3bc764 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0081.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0082.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0082.jpg new file mode 100644 index 0000000..6046ba9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0082.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0083.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0083.jpg new file mode 100644 index 0000000..3609f55 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0083.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0084.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0084.jpg new file mode 100644 index 0000000..8e10965 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0084.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0085.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0085.jpg new file mode 100644 index 0000000..0751cb6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0085.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0086.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0086.jpg new file mode 100644 index 0000000..36f3582 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0086.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0087.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0087.jpg new file mode 100644 index 0000000..0e51229 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0087.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0088.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0088.jpg new file mode 100644 index 0000000..11bc624 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0088.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0089.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0089.jpg new file mode 100644 index 0000000..44cb0e6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0089.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0090.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0090.jpg new file mode 100644 index 0000000..f2bd2a8 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0090.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0091.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0091.jpg new file mode 100644 index 0000000..f3f7e7c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0091.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0092.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0092.jpg new file mode 100644 index 0000000..4cc031b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0092.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0093.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0093.jpg new file mode 100644 index 0000000..d01edb9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0093.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0094.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0094.jpg new file mode 100644 index 0000000..89869cb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0094.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0095.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0095.jpg new file mode 100644 index 0000000..0a2b9ac Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0095.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0096.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0096.jpg new file mode 100644 index 0000000..53f8ac7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0096.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0097.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0097.jpg new file mode 100644 index 0000000..2baaf93 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0097.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0098.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0098.jpg new file mode 100644 index 0000000..c4b86ed Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0098.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0099.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0099.jpg new file mode 100644 index 0000000..79d94ad Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0099.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0100.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0100.jpg new file mode 100644 index 0000000..2710561 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0100.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0101.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0101.jpg new file mode 100644 index 0000000..87d3b4d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0101.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0102.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0102.jpg new file mode 100644 index 0000000..8a76a51 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0102.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0103.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0103.jpg new file mode 100644 index 0000000..587bbd6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0103.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0104.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0104.jpg new file mode 100644 index 0000000..5809b03 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0104.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0105.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0105.jpg new file mode 100644 index 0000000..24b993e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0105.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0106.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0106.jpg new file mode 100644 index 0000000..7f13841 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0106.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0107.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0107.jpg new file mode 100644 index 0000000..6dd1c6d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0107.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0108.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0108.jpg new file mode 100644 index 0000000..805ec45 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0108.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0109.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0109.jpg new file mode 100644 index 0000000..62c7cbc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0109.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0110.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0110.jpg new file mode 100644 index 0000000..f87a5af Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0110.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0111.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0111.jpg new file mode 100644 index 0000000..7897a6c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0111.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0112.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0112.jpg new file mode 100644 index 0000000..7a2f7b2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0112.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0113.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0113.jpg new file mode 100644 index 0000000..dc395fe Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0113.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0114.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0114.jpg new file mode 100644 index 0000000..47c6cf1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0114.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0115.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0115.jpg new file mode 100644 index 0000000..82b650a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0115.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0116.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0116.jpg new file mode 100644 index 0000000..9387ec6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0116.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0117.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0117.jpg new file mode 100644 index 0000000..59b085c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0117.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0118.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0118.jpg new file mode 100644 index 0000000..d65d293 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0118.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0119.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0119.jpg new file mode 100644 index 0000000..6483709 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0119.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0120.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0120.jpg new file mode 100644 index 0000000..6797984 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0120.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0121.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0121.jpg new file mode 100644 index 0000000..a313e38 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0121.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0122.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0122.jpg new file mode 100644 index 0000000..7394f36 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0122.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0123.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0123.jpg new file mode 100644 index 0000000..8fa441b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0123.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0124.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0124.jpg new file mode 100644 index 0000000..137728c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0124.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0125.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0125.jpg new file mode 100644 index 0000000..13c3ca9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0125.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0126.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0126.jpg new file mode 100644 index 0000000..56755a7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0126.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0127.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0127.jpg new file mode 100644 index 0000000..fe6c303 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0127.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0128.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0128.jpg new file mode 100644 index 0000000..eb2cfe9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0128.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0129.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0129.jpg new file mode 100644 index 0000000..af6a22b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0129.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0130.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0130.jpg new file mode 100644 index 0000000..d2b94d7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0130.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0131.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0131.jpg new file mode 100644 index 0000000..5ab8053 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0131.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0132.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0132.jpg new file mode 100644 index 0000000..0865282 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0132.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0133.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0133.jpg new file mode 100644 index 0000000..6abaad6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0133.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0134.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0134.jpg new file mode 100644 index 0000000..16858e8 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0134.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0135.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0135.jpg new file mode 100644 index 0000000..ed9fc69 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0135.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0136.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0136.jpg new file mode 100644 index 0000000..85bba9e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0136.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0137.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0137.jpg new file mode 100644 index 0000000..ccfa42f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0137.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0138.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0138.jpg new file mode 100644 index 0000000..cf464d7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0138.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0139.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0139.jpg new file mode 100644 index 0000000..255c97d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0139.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0140.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0140.jpg new file mode 100644 index 0000000..97f8650 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0140.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0141.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0141.jpg new file mode 100644 index 0000000..6c3a0fd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0141.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0142.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0142.jpg new file mode 100644 index 0000000..af0f3cf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0142.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0143.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0143.jpg new file mode 100644 index 0000000..70ac68c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0143.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0144.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0144.jpg new file mode 100644 index 0000000..9168260 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0144.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0145.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0145.jpg new file mode 100644 index 0000000..c673889 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0145.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0146.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0146.jpg new file mode 100644 index 0000000..87fa698 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0146.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0147.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0147.jpg new file mode 100644 index 0000000..d3b47a3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0147.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0148.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0148.jpg new file mode 100644 index 0000000..bd7fdbd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0148.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0149.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0149.jpg new file mode 100644 index 0000000..2561cfd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/0149.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/ref_pose.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/ref_pose.jpg new file mode 100644 index 0000000..c8ac420 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00004955-01_4_full/ref_pose.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0000.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0000.jpg new file mode 100644 index 0000000..103ee05 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0000.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0001.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0001.jpg new file mode 100644 index 0000000..a21b69d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0001.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0002.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0002.jpg new file mode 100644 index 0000000..3ed464f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0002.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0003.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0003.jpg new file mode 100644 index 0000000..d051f1a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0003.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0004.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0004.jpg new file mode 100644 index 0000000..c73de04 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0004.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0005.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0005.jpg new file mode 100644 index 0000000..cacbe7e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0005.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0006.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0006.jpg new file mode 100644 index 0000000..ddc22a6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0006.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0007.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0007.jpg new file mode 100644 index 0000000..14bff84 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0007.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0008.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0008.jpg new file mode 100644 index 0000000..b16aff3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0008.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0009.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0009.jpg new file mode 100644 index 0000000..6efce60 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0009.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0010.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0010.jpg new file mode 100644 index 0000000..0fd80c0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0010.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0011.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0011.jpg new file mode 100644 index 0000000..91c10c7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0011.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0012.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0012.jpg new file mode 100644 index 0000000..a8fb37d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0012.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0013.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0013.jpg new file mode 100644 index 0000000..0e485f7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0013.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0014.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0014.jpg new file mode 100644 index 0000000..8b70173 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0014.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0015.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0015.jpg new file mode 100644 index 0000000..a06cbe9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0015.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0016.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0016.jpg new file mode 100644 index 0000000..73ea414 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0016.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0017.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0017.jpg new file mode 100644 index 0000000..a5fe1ed Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0017.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0018.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0018.jpg new file mode 100644 index 0000000..9e0c67f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0018.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0019.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0019.jpg new file mode 100644 index 0000000..e217d25 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0019.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0020.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0020.jpg new file mode 100644 index 0000000..bad77d6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0020.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0021.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0021.jpg new file mode 100644 index 0000000..e9819f0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0021.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0022.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0022.jpg new file mode 100644 index 0000000..3f19300 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0022.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0023.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0023.jpg new file mode 100644 index 0000000..08507e8 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0023.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0024.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0024.jpg new file mode 100644 index 0000000..68005ba Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0024.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0025.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0025.jpg new file mode 100644 index 0000000..891dd2b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0025.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0026.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0026.jpg new file mode 100644 index 0000000..f299883 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0026.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0027.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0027.jpg new file mode 100644 index 0000000..a8a3337 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0027.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0028.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0028.jpg new file mode 100644 index 0000000..4f29eaf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0028.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0029.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0029.jpg new file mode 100644 index 0000000..95a864a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0029.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0030.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0030.jpg new file mode 100644 index 0000000..3fec689 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0030.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0031.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0031.jpg new file mode 100644 index 0000000..480912f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0031.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0032.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0032.jpg new file mode 100644 index 0000000..cab5668 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0032.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0033.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0033.jpg new file mode 100644 index 0000000..c82a077 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0033.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0034.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0034.jpg new file mode 100644 index 0000000..57587ed Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0034.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0035.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0035.jpg new file mode 100644 index 0000000..8f86ae7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0035.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0036.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0036.jpg new file mode 100644 index 0000000..913190a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0036.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0037.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0037.jpg new file mode 100644 index 0000000..a854a30 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0037.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0038.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0038.jpg new file mode 100644 index 0000000..386d664 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0038.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0039.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0039.jpg new file mode 100644 index 0000000..68f66cf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0039.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0040.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0040.jpg new file mode 100644 index 0000000..a419578 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0040.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0041.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0041.jpg new file mode 100644 index 0000000..fce64f7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0041.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0042.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0042.jpg new file mode 100644 index 0000000..d2e6e0e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0042.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0043.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0043.jpg new file mode 100644 index 0000000..80b4427 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0043.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0044.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0044.jpg new file mode 100644 index 0000000..dcdefff Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0044.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0045.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0045.jpg new file mode 100644 index 0000000..34f66fc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0045.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0046.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0046.jpg new file mode 100644 index 0000000..ad4bb2b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0046.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0047.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0047.jpg new file mode 100644 index 0000000..9005a33 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0047.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0048.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0048.jpg new file mode 100644 index 0000000..f560b46 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0048.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0049.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0049.jpg new file mode 100644 index 0000000..9f2d50e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0049.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0050.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0050.jpg new file mode 100644 index 0000000..a47b60a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0050.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0051.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0051.jpg new file mode 100644 index 0000000..555ef43 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0051.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0052.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0052.jpg new file mode 100644 index 0000000..6d9a578 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0052.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0053.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0053.jpg new file mode 100644 index 0000000..8bc1fc4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0053.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0054.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0054.jpg new file mode 100644 index 0000000..526381d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0054.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0055.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0055.jpg new file mode 100644 index 0000000..16b93d3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0055.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0056.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0056.jpg new file mode 100644 index 0000000..f264799 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0056.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0057.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0057.jpg new file mode 100644 index 0000000..e939ac2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0057.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0058.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0058.jpg new file mode 100644 index 0000000..953c01d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0058.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0059.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0059.jpg new file mode 100644 index 0000000..bada993 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0059.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0060.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0060.jpg new file mode 100644 index 0000000..25519e1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0060.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0061.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0061.jpg new file mode 100644 index 0000000..f74eaf4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0061.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0062.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0062.jpg new file mode 100644 index 0000000..8e0f807 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0062.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0063.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0063.jpg new file mode 100644 index 0000000..4fb4c2b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0063.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0064.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0064.jpg new file mode 100644 index 0000000..3c507bb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0064.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0065.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0065.jpg new file mode 100644 index 0000000..70994c1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0065.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0066.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0066.jpg new file mode 100644 index 0000000..ca968e0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0066.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0067.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0067.jpg new file mode 100644 index 0000000..045ded5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0067.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0068.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0068.jpg new file mode 100644 index 0000000..3fe4235 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0068.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0069.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0069.jpg new file mode 100644 index 0000000..fbd845b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0069.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0070.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0070.jpg new file mode 100644 index 0000000..a96e5c5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0070.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0071.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0071.jpg new file mode 100644 index 0000000..981f959 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0071.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0072.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0072.jpg new file mode 100644 index 0000000..e2bc30c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0072.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0073.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0073.jpg new file mode 100644 index 0000000..37f7ac4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0073.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0074.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0074.jpg new file mode 100644 index 0000000..91cdc4d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0074.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0075.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0075.jpg new file mode 100644 index 0000000..0d60d29 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0075.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0076.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0076.jpg new file mode 100644 index 0000000..6a3ce2b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0076.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0077.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0077.jpg new file mode 100644 index 0000000..4d32feb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0077.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0078.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0078.jpg new file mode 100644 index 0000000..a8ab5fe Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0078.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0079.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0079.jpg new file mode 100644 index 0000000..3e291ef Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0079.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0080.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0080.jpg new file mode 100644 index 0000000..97423cd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0080.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0081.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0081.jpg new file mode 100644 index 0000000..b31f794 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0081.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0082.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0082.jpg new file mode 100644 index 0000000..2547ea4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0082.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0083.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0083.jpg new file mode 100644 index 0000000..891f655 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0083.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0084.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0084.jpg new file mode 100644 index 0000000..24803d8 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0084.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0085.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0085.jpg new file mode 100644 index 0000000..b5742f1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0085.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0086.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0086.jpg new file mode 100644 index 0000000..a7d9520 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0086.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0087.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0087.jpg new file mode 100644 index 0000000..163c7c5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0087.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0088.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0088.jpg new file mode 100644 index 0000000..c8e0392 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0088.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0089.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0089.jpg new file mode 100644 index 0000000..b6b4426 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0089.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0090.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0090.jpg new file mode 100644 index 0000000..7c5d887 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0090.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0091.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0091.jpg new file mode 100644 index 0000000..ff3606a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0091.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0092.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0092.jpg new file mode 100644 index 0000000..dc004a5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0092.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0093.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0093.jpg new file mode 100644 index 0000000..ba99682 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0093.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0094.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0094.jpg new file mode 100644 index 0000000..e6b63e0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0094.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0095.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0095.jpg new file mode 100644 index 0000000..56bb985 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0095.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0096.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0096.jpg new file mode 100644 index 0000000..ea73473 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0096.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0097.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0097.jpg new file mode 100644 index 0000000..c2eec17 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0097.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0098.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0098.jpg new file mode 100644 index 0000000..d3131ae Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0098.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0099.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0099.jpg new file mode 100644 index 0000000..d36094b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0099.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0100.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0100.jpg new file mode 100644 index 0000000..38ed0f3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0100.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0101.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0101.jpg new file mode 100644 index 0000000..70d01cf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0101.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0102.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0102.jpg new file mode 100644 index 0000000..2890426 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0102.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0103.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0103.jpg new file mode 100644 index 0000000..01a1d6b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0103.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0104.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0104.jpg new file mode 100644 index 0000000..f405626 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0104.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0105.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0105.jpg new file mode 100644 index 0000000..16208b6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0105.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0106.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0106.jpg new file mode 100644 index 0000000..af88bc7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0106.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0107.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0107.jpg new file mode 100644 index 0000000..7f7cbeb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0107.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0108.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0108.jpg new file mode 100644 index 0000000..d80b921 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0108.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0109.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0109.jpg new file mode 100644 index 0000000..a592b5f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0109.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0110.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0110.jpg new file mode 100644 index 0000000..eca6510 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0110.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0111.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0111.jpg new file mode 100644 index 0000000..725efc7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0111.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0112.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0112.jpg new file mode 100644 index 0000000..5ed32a4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0112.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0113.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0113.jpg new file mode 100644 index 0000000..4a60020 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0113.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0114.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0114.jpg new file mode 100644 index 0000000..1b6b89a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0114.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0115.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0115.jpg new file mode 100644 index 0000000..a2d8ad7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0115.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0116.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0116.jpg new file mode 100644 index 0000000..8095af4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0116.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0117.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0117.jpg new file mode 100644 index 0000000..e4c4583 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0117.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0118.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0118.jpg new file mode 100644 index 0000000..dcc9047 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0118.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0119.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0119.jpg new file mode 100644 index 0000000..c229a54 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0119.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0120.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0120.jpg new file mode 100644 index 0000000..646bf7e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0120.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0121.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0121.jpg new file mode 100644 index 0000000..4fa4206 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0121.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0122.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0122.jpg new file mode 100644 index 0000000..17029ff Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0122.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0123.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0123.jpg new file mode 100644 index 0000000..b05bb38 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0123.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0124.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0124.jpg new file mode 100644 index 0000000..6fd696f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0124.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0125.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0125.jpg new file mode 100644 index 0000000..082e85b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0125.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0126.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0126.jpg new file mode 100644 index 0000000..1644de0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0126.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0127.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0127.jpg new file mode 100644 index 0000000..52343e0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0127.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0128.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0128.jpg new file mode 100644 index 0000000..bee7a5a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0128.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0129.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0129.jpg new file mode 100644 index 0000000..7c8e32f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0129.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0130.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0130.jpg new file mode 100644 index 0000000..166b58f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0130.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0131.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0131.jpg new file mode 100644 index 0000000..c3ee065 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0131.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0132.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0132.jpg new file mode 100644 index 0000000..f4cc0c3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0132.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0133.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0133.jpg new file mode 100644 index 0000000..4812dde Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0133.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0134.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0134.jpg new file mode 100644 index 0000000..3d5a381 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0134.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0135.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0135.jpg new file mode 100644 index 0000000..8293775 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0135.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0136.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0136.jpg new file mode 100644 index 0000000..84d5b34 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0136.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0137.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0137.jpg new file mode 100644 index 0000000..b4ef736 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0137.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0138.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0138.jpg new file mode 100644 index 0000000..5024cf3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0138.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0139.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0139.jpg new file mode 100644 index 0000000..6305984 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0139.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0140.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0140.jpg new file mode 100644 index 0000000..26d4cec Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0140.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0141.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0141.jpg new file mode 100644 index 0000000..48c3de1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0141.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0142.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0142.jpg new file mode 100644 index 0000000..53317f3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0142.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0143.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0143.jpg new file mode 100644 index 0000000..b82343b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0143.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0144.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0144.jpg new file mode 100644 index 0000000..de259c9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0144.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0145.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0145.jpg new file mode 100644 index 0000000..2c1a412 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0145.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0146.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0146.jpg new file mode 100644 index 0000000..ee2a133 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0146.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0147.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0147.jpg new file mode 100644 index 0000000..5753835 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0147.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0148.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0148.jpg new file mode 100644 index 0000000..a532947 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0148.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0149.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0149.jpg new file mode 100644 index 0000000..72142c6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/0149.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/ref_pose.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/ref_pose.jpg new file mode 100644 index 0000000..c3a354b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full/ref_pose.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0000.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0000.jpg new file mode 100644 index 0000000..2fce139 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0000.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0001.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0001.jpg new file mode 100644 index 0000000..95a764c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0001.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0002.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0002.jpg new file mode 100644 index 0000000..d0cb1bd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0002.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0003.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0003.jpg new file mode 100644 index 0000000..0968256 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0003.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0004.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0004.jpg new file mode 100644 index 0000000..09e06e9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0004.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0005.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0005.jpg new file mode 100644 index 0000000..0c17791 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0005.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0006.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0006.jpg new file mode 100644 index 0000000..709f2bb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0006.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0007.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0007.jpg new file mode 100644 index 0000000..7f45314 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0007.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0008.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0008.jpg new file mode 100644 index 0000000..47e4f93 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0008.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0009.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0009.jpg new file mode 100644 index 0000000..67fb5c9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0009.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0010.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0010.jpg new file mode 100644 index 0000000..a1e511f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0010.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0011.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0011.jpg new file mode 100644 index 0000000..f60a72a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0011.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0012.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0012.jpg new file mode 100644 index 0000000..dd5dee0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0012.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0013.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0013.jpg new file mode 100644 index 0000000..98d393b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0013.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0014.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0014.jpg new file mode 100644 index 0000000..eca137f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0014.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0015.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0015.jpg new file mode 100644 index 0000000..0c665c3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0015.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0016.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0016.jpg new file mode 100644 index 0000000..81ed1fb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0016.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0017.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0017.jpg new file mode 100644 index 0000000..b2b10d3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0017.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0018.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0018.jpg new file mode 100644 index 0000000..1029e39 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0018.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0019.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0019.jpg new file mode 100644 index 0000000..ddc4574 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0019.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0020.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0020.jpg new file mode 100644 index 0000000..d2a4a34 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0020.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0021.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0021.jpg new file mode 100644 index 0000000..1504a7e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0021.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0022.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0022.jpg new file mode 100644 index 0000000..6e75d01 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0022.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0023.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0023.jpg new file mode 100644 index 0000000..5a6eb45 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0023.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0024.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0024.jpg new file mode 100644 index 0000000..8366b50 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0024.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0025.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0025.jpg new file mode 100644 index 0000000..5950557 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0025.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0026.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0026.jpg new file mode 100644 index 0000000..2b5fdfa Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0026.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0027.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0027.jpg new file mode 100644 index 0000000..0adcb67 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0027.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0028.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0028.jpg new file mode 100644 index 0000000..3d6a480 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0028.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0029.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0029.jpg new file mode 100644 index 0000000..4b3dc6a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0029.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0030.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0030.jpg new file mode 100644 index 0000000..501d3e6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0030.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0031.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0031.jpg new file mode 100644 index 0000000..7f110f9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0031.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0032.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0032.jpg new file mode 100644 index 0000000..ca07ce5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0032.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0033.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0033.jpg new file mode 100644 index 0000000..be205af Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0033.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0034.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0034.jpg new file mode 100644 index 0000000..d6f8fe6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0034.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0035.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0035.jpg new file mode 100644 index 0000000..88fa5b2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0035.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0036.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0036.jpg new file mode 100644 index 0000000..d4cdf09 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0036.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0037.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0037.jpg new file mode 100644 index 0000000..39c0794 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0037.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0038.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0038.jpg new file mode 100644 index 0000000..5831b70 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0038.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0039.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0039.jpg new file mode 100644 index 0000000..af812fa Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0039.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0040.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0040.jpg new file mode 100644 index 0000000..5bbd300 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0040.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0041.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0041.jpg new file mode 100644 index 0000000..de9231f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0041.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0042.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0042.jpg new file mode 100644 index 0000000..0065ce3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0042.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0043.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0043.jpg new file mode 100644 index 0000000..373f5e9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0043.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0044.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0044.jpg new file mode 100644 index 0000000..e921b65 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0044.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0045.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0045.jpg new file mode 100644 index 0000000..4277ee2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0045.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0046.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0046.jpg new file mode 100644 index 0000000..2b1dd93 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0046.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0047.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0047.jpg new file mode 100644 index 0000000..b90b528 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0047.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0048.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0048.jpg new file mode 100644 index 0000000..f1f2d8f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0048.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0049.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0049.jpg new file mode 100644 index 0000000..f6c4d3c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0049.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0050.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0050.jpg new file mode 100644 index 0000000..d466211 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0050.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0051.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0051.jpg new file mode 100644 index 0000000..3ce77a3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0051.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0052.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0052.jpg new file mode 100644 index 0000000..9d53fee Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0052.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0053.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0053.jpg new file mode 100644 index 0000000..19dd0ba Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0053.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0054.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0054.jpg new file mode 100644 index 0000000..49f6b44 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0054.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0055.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0055.jpg new file mode 100644 index 0000000..d1939d7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0055.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0056.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0056.jpg new file mode 100644 index 0000000..b5ee59a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0056.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0057.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0057.jpg new file mode 100644 index 0000000..1978049 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0057.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0058.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0058.jpg new file mode 100644 index 0000000..39ddc6a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0058.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0059.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0059.jpg new file mode 100644 index 0000000..ad147c1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0059.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0060.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0060.jpg new file mode 100644 index 0000000..6dd5081 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0060.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0061.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0061.jpg new file mode 100644 index 0000000..5a7a62c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0061.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0062.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0062.jpg new file mode 100644 index 0000000..80baaaf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0062.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0063.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0063.jpg new file mode 100644 index 0000000..cc42fe3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0063.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0064.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0064.jpg new file mode 100644 index 0000000..8baa64d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0064.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0065.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0065.jpg new file mode 100644 index 0000000..397a3d3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0065.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0066.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0066.jpg new file mode 100644 index 0000000..d1b3a2f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0066.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0067.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0067.jpg new file mode 100644 index 0000000..b829a52 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0067.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0068.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0068.jpg new file mode 100644 index 0000000..caf9c21 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0068.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0069.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0069.jpg new file mode 100644 index 0000000..9f1f762 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0069.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0070.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0070.jpg new file mode 100644 index 0000000..abbc489 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0070.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0071.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0071.jpg new file mode 100644 index 0000000..57d8836 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0071.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0072.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0072.jpg new file mode 100644 index 0000000..c24dce0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0072.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0073.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0073.jpg new file mode 100644 index 0000000..810077d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0073.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0074.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0074.jpg new file mode 100644 index 0000000..d4d0997 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0074.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0075.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0075.jpg new file mode 100644 index 0000000..844b913 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0075.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0076.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0076.jpg new file mode 100644 index 0000000..2d99f11 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0076.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0077.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0077.jpg new file mode 100644 index 0000000..7fc905b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0077.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0078.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0078.jpg new file mode 100644 index 0000000..4fa70c0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0078.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0079.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0079.jpg new file mode 100644 index 0000000..4afe09b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0079.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0080.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0080.jpg new file mode 100644 index 0000000..4429cb7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0080.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0081.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0081.jpg new file mode 100644 index 0000000..031bd47 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0081.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0082.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0082.jpg new file mode 100644 index 0000000..1fd48e9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0082.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0083.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0083.jpg new file mode 100644 index 0000000..c822abf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0083.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0084.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0084.jpg new file mode 100644 index 0000000..90e19dd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0084.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0085.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0085.jpg new file mode 100644 index 0000000..92bb951 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0085.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0086.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0086.jpg new file mode 100644 index 0000000..70337bd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0086.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0087.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0087.jpg new file mode 100644 index 0000000..e5a30d7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0087.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0088.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0088.jpg new file mode 100644 index 0000000..588c176 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0088.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0089.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0089.jpg new file mode 100644 index 0000000..d2ed2a5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0089.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0090.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0090.jpg new file mode 100644 index 0000000..2edd350 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0090.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0091.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0091.jpg new file mode 100644 index 0000000..a52ab8f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0091.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0092.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0092.jpg new file mode 100644 index 0000000..2912277 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0092.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0093.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0093.jpg new file mode 100644 index 0000000..6fd4d45 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0093.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0094.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0094.jpg new file mode 100644 index 0000000..188ecd0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0094.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0095.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0095.jpg new file mode 100644 index 0000000..8f89ac4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0095.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0096.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0096.jpg new file mode 100644 index 0000000..4e58dbe Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0096.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0097.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0097.jpg new file mode 100644 index 0000000..a75bf69 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0097.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0098.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0098.jpg new file mode 100644 index 0000000..75e3b0d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0098.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0099.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0099.jpg new file mode 100644 index 0000000..3de3a47 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0099.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0100.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0100.jpg new file mode 100644 index 0000000..d6f297e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0100.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0101.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0101.jpg new file mode 100644 index 0000000..315bad8 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0101.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0102.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0102.jpg new file mode 100644 index 0000000..0df76af Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0102.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0103.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0103.jpg new file mode 100644 index 0000000..d97c8ae Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0103.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0104.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0104.jpg new file mode 100644 index 0000000..f28bfc2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0104.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0105.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0105.jpg new file mode 100644 index 0000000..bb303b3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0105.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0106.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0106.jpg new file mode 100644 index 0000000..6754ce7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0106.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0107.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0107.jpg new file mode 100644 index 0000000..2306d54 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0107.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0108.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0108.jpg new file mode 100644 index 0000000..c79e575 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0108.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0109.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0109.jpg new file mode 100644 index 0000000..777bd4e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0109.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0110.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0110.jpg new file mode 100644 index 0000000..6001629 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0110.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0111.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0111.jpg new file mode 100644 index 0000000..368f4a5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0111.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0112.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0112.jpg new file mode 100644 index 0000000..cc10382 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0112.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0113.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0113.jpg new file mode 100644 index 0000000..0688043 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0113.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0114.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0114.jpg new file mode 100644 index 0000000..c94c5b3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0114.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0115.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0115.jpg new file mode 100644 index 0000000..14f6f1b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0115.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0116.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0116.jpg new file mode 100644 index 0000000..8ffa4fb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0116.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0117.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0117.jpg new file mode 100644 index 0000000..99f6f6d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0117.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0118.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0118.jpg new file mode 100644 index 0000000..a677475 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0118.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0119.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0119.jpg new file mode 100644 index 0000000..29fac17 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0119.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0120.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0120.jpg new file mode 100644 index 0000000..f4d68a9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0120.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0121.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0121.jpg new file mode 100644 index 0000000..b71fb95 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0121.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0122.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0122.jpg new file mode 100644 index 0000000..5f1d9bc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0122.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0123.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0123.jpg new file mode 100644 index 0000000..a3e79a3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0123.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0124.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0124.jpg new file mode 100644 index 0000000..54de373 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0124.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0125.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0125.jpg new file mode 100644 index 0000000..f224b03 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0125.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0126.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0126.jpg new file mode 100644 index 0000000..5896ffa Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0126.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0127.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0127.jpg new file mode 100644 index 0000000..4dd8553 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0127.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0128.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0128.jpg new file mode 100644 index 0000000..1ff3ae2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0128.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0129.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0129.jpg new file mode 100644 index 0000000..0c6e023 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0129.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0130.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0130.jpg new file mode 100644 index 0000000..07fde8a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0130.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0131.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0131.jpg new file mode 100644 index 0000000..fc7d81d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0131.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0132.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0132.jpg new file mode 100644 index 0000000..0b3b1cd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0132.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0133.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0133.jpg new file mode 100644 index 0000000..fc08a5f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0133.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0134.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0134.jpg new file mode 100644 index 0000000..9a116a4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0134.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0135.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0135.jpg new file mode 100644 index 0000000..60a8a91 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0135.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0136.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0136.jpg new file mode 100644 index 0000000..e229ad0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0136.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0137.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0137.jpg new file mode 100644 index 0000000..ff34080 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0137.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0138.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0138.jpg new file mode 100644 index 0000000..04134a0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0138.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0139.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0139.jpg new file mode 100644 index 0000000..f641534 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0139.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0140.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0140.jpg new file mode 100644 index 0000000..ecf31bb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0140.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0141.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0141.jpg new file mode 100644 index 0000000..a000e02 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0141.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0142.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0142.jpg new file mode 100644 index 0000000..bf3056d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0142.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0143.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0143.jpg new file mode 100644 index 0000000..5f5259b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0143.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0144.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0144.jpg new file mode 100644 index 0000000..4818a8a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0144.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0145.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0145.jpg new file mode 100644 index 0000000..35203a5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0145.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0146.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0146.jpg new file mode 100644 index 0000000..98c641a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0146.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0147.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0147.jpg new file mode 100644 index 0000000..6e9762b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0147.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0148.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0148.jpg new file mode 100644 index 0000000..8bd769a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0148.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0149.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0149.jpg new file mode 100644 index 0000000..43049b2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0149.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0150.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0150.jpg new file mode 100644 index 0000000..4ad68a5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0150.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0151.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0151.jpg new file mode 100644 index 0000000..f5506d5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0151.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0152.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0152.jpg new file mode 100644 index 0000000..6588189 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0152.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0153.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0153.jpg new file mode 100644 index 0000000..d647695 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0153.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0154.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0154.jpg new file mode 100644 index 0000000..8622ae7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0154.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0155.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0155.jpg new file mode 100644 index 0000000..c90d9f9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0155.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0156.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0156.jpg new file mode 100644 index 0000000..a0b348f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0156.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0157.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0157.jpg new file mode 100644 index 0000000..790d36d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0157.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0158.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0158.jpg new file mode 100644 index 0000000..a7e149d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0158.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0159.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0159.jpg new file mode 100644 index 0000000..dbbfee1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0159.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0160.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0160.jpg new file mode 100644 index 0000000..7a112d2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0160.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0161.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0161.jpg new file mode 100644 index 0000000..0bf1697 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0161.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0162.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0162.jpg new file mode 100644 index 0000000..b9b34d9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0162.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0163.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0163.jpg new file mode 100644 index 0000000..13a662d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0163.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0164.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0164.jpg new file mode 100644 index 0000000..45aaad0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0164.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0165.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0165.jpg new file mode 100644 index 0000000..7ff4b6c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0165.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0166.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0166.jpg new file mode 100644 index 0000000..0081089 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0166.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0167.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0167.jpg new file mode 100644 index 0000000..590d306 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0167.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0168.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0168.jpg new file mode 100644 index 0000000..79abc3b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0168.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0169.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0169.jpg new file mode 100644 index 0000000..3df06f6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0169.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0170.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0170.jpg new file mode 100644 index 0000000..ac636dd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0170.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0171.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0171.jpg new file mode 100644 index 0000000..933fae6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0171.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0172.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0172.jpg new file mode 100644 index 0000000..5f075fb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0172.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0173.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0173.jpg new file mode 100644 index 0000000..bf60058 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0173.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0174.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0174.jpg new file mode 100644 index 0000000..d3f9350 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0174.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0175.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0175.jpg new file mode 100644 index 0000000..3b02efa Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0175.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0176.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0176.jpg new file mode 100644 index 0000000..4ef4528 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0176.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0177.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0177.jpg new file mode 100644 index 0000000..4c94d06 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0177.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0178.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0178.jpg new file mode 100644 index 0000000..a07c673 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0178.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0179.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0179.jpg new file mode 100644 index 0000000..15b0a2c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0179.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0180.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0180.jpg new file mode 100644 index 0000000..207931a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0180.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0181.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0181.jpg new file mode 100644 index 0000000..76f203b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0181.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0182.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0182.jpg new file mode 100644 index 0000000..d69d091 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0182.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0183.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0183.jpg new file mode 100644 index 0000000..11c872b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0183.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0184.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0184.jpg new file mode 100644 index 0000000..4cb243e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0184.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0185.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0185.jpg new file mode 100644 index 0000000..9ef3a24 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0185.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0186.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0186.jpg new file mode 100644 index 0000000..886ddcf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0186.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0187.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0187.jpg new file mode 100644 index 0000000..46e535f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0187.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0188.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0188.jpg new file mode 100644 index 0000000..71260ae Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0188.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0189.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0189.jpg new file mode 100644 index 0000000..4b7ba6a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0189.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0190.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0190.jpg new file mode 100644 index 0000000..6518447 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0190.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0191.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0191.jpg new file mode 100644 index 0000000..70826b8 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0191.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0192.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0192.jpg new file mode 100644 index 0000000..d8fc017 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0192.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0193.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0193.jpg new file mode 100644 index 0000000..e15fac1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0193.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0194.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0194.jpg new file mode 100644 index 0000000..3e48c50 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0194.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0195.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0195.jpg new file mode 100644 index 0000000..71a2515 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0195.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0196.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0196.jpg new file mode 100644 index 0000000..a28a654 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0196.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0197.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0197.jpg new file mode 100644 index 0000000..b75c454 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0197.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0198.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0198.jpg new file mode 100644 index 0000000..82478c3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0198.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0199.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0199.jpg new file mode 100644 index 0000000..69e4edf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0199.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0200.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0200.jpg new file mode 100644 index 0000000..bfff95b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0200.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0201.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0201.jpg new file mode 100644 index 0000000..c639f4f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0201.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0202.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0202.jpg new file mode 100644 index 0000000..385aad0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0202.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0203.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0203.jpg new file mode 100644 index 0000000..73866cf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0203.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0204.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0204.jpg new file mode 100644 index 0000000..e9ca5bc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0204.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0205.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0205.jpg new file mode 100644 index 0000000..48ac8ed Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0205.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0206.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0206.jpg new file mode 100644 index 0000000..68be3dd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0206.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0207.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0207.jpg new file mode 100644 index 0000000..aad6187 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0207.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0208.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0208.jpg new file mode 100644 index 0000000..8860f2a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0208.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0209.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0209.jpg new file mode 100644 index 0000000..12cac0b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0209.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0210.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0210.jpg new file mode 100644 index 0000000..1eb3f49 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0210.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0211.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0211.jpg new file mode 100644 index 0000000..4a3bec2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0211.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0212.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0212.jpg new file mode 100644 index 0000000..5e0c361 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0212.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0213.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0213.jpg new file mode 100644 index 0000000..52becf8 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0213.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0214.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0214.jpg new file mode 100644 index 0000000..3459d96 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0214.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0215.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0215.jpg new file mode 100644 index 0000000..6eea8d0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0215.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0216.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0216.jpg new file mode 100644 index 0000000..a29678d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0216.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0217.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0217.jpg new file mode 100644 index 0000000..9ff15fe Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0217.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0218.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0218.jpg new file mode 100644 index 0000000..22bcd3a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0218.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0219.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0219.jpg new file mode 100644 index 0000000..e23cf71 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0219.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0220.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0220.jpg new file mode 100644 index 0000000..4e4ff2d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0220.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0221.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0221.jpg new file mode 100644 index 0000000..584d8d5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0221.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0222.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0222.jpg new file mode 100644 index 0000000..7513482 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0222.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0223.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0223.jpg new file mode 100644 index 0000000..37c219b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0223.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0224.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0224.jpg new file mode 100644 index 0000000..8875351 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0224.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0225.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0225.jpg new file mode 100644 index 0000000..7f00085 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0225.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0226.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0226.jpg new file mode 100644 index 0000000..cfc1dd4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0226.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0227.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0227.jpg new file mode 100644 index 0000000..1989203 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0227.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0228.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0228.jpg new file mode 100644 index 0000000..0547cda Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0228.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0229.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0229.jpg new file mode 100644 index 0000000..1eadf2f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0229.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0230.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0230.jpg new file mode 100644 index 0000000..288fe3f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0230.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0231.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0231.jpg new file mode 100644 index 0000000..8139080 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0231.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0232.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0232.jpg new file mode 100644 index 0000000..5286303 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0232.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0233.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0233.jpg new file mode 100644 index 0000000..8d62f9b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0233.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0234.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0234.jpg new file mode 100644 index 0000000..4aa3e6f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0234.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0235.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0235.jpg new file mode 100644 index 0000000..625a81b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0235.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0236.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0236.jpg new file mode 100644 index 0000000..afac149 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0236.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0237.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0237.jpg new file mode 100644 index 0000000..107a8fb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0237.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0238.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0238.jpg new file mode 100644 index 0000000..1837394 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0238.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0239.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0239.jpg new file mode 100644 index 0000000..4a2afe7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0239.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0240.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0240.jpg new file mode 100644 index 0000000..76d5f0a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0240.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0241.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0241.jpg new file mode 100644 index 0000000..abc8074 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0241.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0242.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0242.jpg new file mode 100644 index 0000000..b3a882c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0242.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0243.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0243.jpg new file mode 100644 index 0000000..e110682 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0243.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0244.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0244.jpg new file mode 100644 index 0000000..fb56704 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0244.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0245.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0245.jpg new file mode 100644 index 0000000..794ccce Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0245.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0246.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0246.jpg new file mode 100644 index 0000000..e5a2e4c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0246.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0247.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0247.jpg new file mode 100644 index 0000000..83ca0ac Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0247.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0248.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0248.jpg new file mode 100644 index 0000000..7d19ea4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0248.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0249.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0249.jpg new file mode 100644 index 0000000..6acb9a3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0249.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0250.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0250.jpg new file mode 100644 index 0000000..3d2628f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0250.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0251.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0251.jpg new file mode 100644 index 0000000..5ec5f34 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0251.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0252.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0252.jpg new file mode 100644 index 0000000..a6a40cf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0252.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0253.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0253.jpg new file mode 100644 index 0000000..d5755eb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0253.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0254.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0254.jpg new file mode 100644 index 0000000..0d28d04 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0254.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0255.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0255.jpg new file mode 100644 index 0000000..17fc583 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0255.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0256.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0256.jpg new file mode 100644 index 0000000..d49f3ad Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0256.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0257.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0257.jpg new file mode 100644 index 0000000..103eab8 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0257.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0258.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0258.jpg new file mode 100644 index 0000000..7b5605a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0258.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0259.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0259.jpg new file mode 100644 index 0000000..639593e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0259.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0260.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0260.jpg new file mode 100644 index 0000000..8cac60d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0260.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0261.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0261.jpg new file mode 100644 index 0000000..1ae25db Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0261.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0262.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0262.jpg new file mode 100644 index 0000000..47ec64a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0262.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0263.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0263.jpg new file mode 100644 index 0000000..f746ad7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0263.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0264.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0264.jpg new file mode 100644 index 0000000..bdaba6b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0264.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0265.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0265.jpg new file mode 100644 index 0000000..113e576 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0265.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0266.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0266.jpg new file mode 100644 index 0000000..f2abaa7 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0266.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0267.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0267.jpg new file mode 100644 index 0000000..fd2c811 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0267.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0268.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0268.jpg new file mode 100644 index 0000000..34837b5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0268.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0269.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0269.jpg new file mode 100644 index 0000000..7906208 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0269.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0270.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0270.jpg new file mode 100644 index 0000000..08b18b4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0270.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0271.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0271.jpg new file mode 100644 index 0000000..f7eea7a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0271.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0272.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0272.jpg new file mode 100644 index 0000000..8aafada Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0272.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0273.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0273.jpg new file mode 100644 index 0000000..93e5424 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0273.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0274.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0274.jpg new file mode 100644 index 0000000..6889db9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0274.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0275.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0275.jpg new file mode 100644 index 0000000..a971ac4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0275.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0276.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0276.jpg new file mode 100644 index 0000000..a98e89e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0276.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0277.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0277.jpg new file mode 100644 index 0000000..06755a0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0277.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0278.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0278.jpg new file mode 100644 index 0000000..e83d84e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0278.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0279.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0279.jpg new file mode 100644 index 0000000..912660c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/0279.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/ref_pose.jpg b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/ref_pose.jpg new file mode 100644 index 0000000..c3a354b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/WOMEN-Blouses_Shirts-id_00005125-03_4_full_dance/ref_pose.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0000.jpg b/animation/UniAnimate/data/saved_pose/musk/0000.jpg new file mode 100644 index 0000000..0c84f3d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0000.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0001.jpg b/animation/UniAnimate/data/saved_pose/musk/0001.jpg new file mode 100644 index 0000000..9a8db23 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0001.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0002.jpg b/animation/UniAnimate/data/saved_pose/musk/0002.jpg new file mode 100644 index 0000000..a0e3125 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0002.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0003.jpg b/animation/UniAnimate/data/saved_pose/musk/0003.jpg new file mode 100644 index 0000000..250da17 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0003.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0004.jpg b/animation/UniAnimate/data/saved_pose/musk/0004.jpg new file mode 100644 index 0000000..1fa6767 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0004.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0005.jpg b/animation/UniAnimate/data/saved_pose/musk/0005.jpg new file mode 100644 index 0000000..42c9fbc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0005.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0006.jpg b/animation/UniAnimate/data/saved_pose/musk/0006.jpg new file mode 100644 index 0000000..631af06 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0006.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0007.jpg b/animation/UniAnimate/data/saved_pose/musk/0007.jpg new file mode 100644 index 0000000..95e9d66 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0007.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0008.jpg b/animation/UniAnimate/data/saved_pose/musk/0008.jpg new file mode 100644 index 0000000..98315af Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0008.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0009.jpg b/animation/UniAnimate/data/saved_pose/musk/0009.jpg new file mode 100644 index 0000000..c8848de Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0009.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0010.jpg b/animation/UniAnimate/data/saved_pose/musk/0010.jpg new file mode 100644 index 0000000..703f7a3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0010.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0011.jpg b/animation/UniAnimate/data/saved_pose/musk/0011.jpg new file mode 100644 index 0000000..546654d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0011.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0012.jpg b/animation/UniAnimate/data/saved_pose/musk/0012.jpg new file mode 100644 index 0000000..629ee10 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0012.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0013.jpg b/animation/UniAnimate/data/saved_pose/musk/0013.jpg new file mode 100644 index 0000000..2448c2b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0013.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0014.jpg b/animation/UniAnimate/data/saved_pose/musk/0014.jpg new file mode 100644 index 0000000..23a9f89 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0014.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0015.jpg b/animation/UniAnimate/data/saved_pose/musk/0015.jpg new file mode 100644 index 0000000..8f50935 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0015.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0016.jpg b/animation/UniAnimate/data/saved_pose/musk/0016.jpg new file mode 100644 index 0000000..716a2ac Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0016.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0017.jpg b/animation/UniAnimate/data/saved_pose/musk/0017.jpg new file mode 100644 index 0000000..49ba023 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0017.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0018.jpg b/animation/UniAnimate/data/saved_pose/musk/0018.jpg new file mode 100644 index 0000000..f3796b5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0018.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0019.jpg b/animation/UniAnimate/data/saved_pose/musk/0019.jpg new file mode 100644 index 0000000..6c32d06 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0019.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0020.jpg b/animation/UniAnimate/data/saved_pose/musk/0020.jpg new file mode 100644 index 0000000..8aa444e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0020.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0021.jpg b/animation/UniAnimate/data/saved_pose/musk/0021.jpg new file mode 100644 index 0000000..854a3bf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0021.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0022.jpg b/animation/UniAnimate/data/saved_pose/musk/0022.jpg new file mode 100644 index 0000000..ef09d13 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0022.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0023.jpg b/animation/UniAnimate/data/saved_pose/musk/0023.jpg new file mode 100644 index 0000000..47ee521 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0023.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0024.jpg b/animation/UniAnimate/data/saved_pose/musk/0024.jpg new file mode 100644 index 0000000..873f9f9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0024.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0025.jpg b/animation/UniAnimate/data/saved_pose/musk/0025.jpg new file mode 100644 index 0000000..a57a196 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0025.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0026.jpg b/animation/UniAnimate/data/saved_pose/musk/0026.jpg new file mode 100644 index 0000000..2951f99 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0026.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0027.jpg b/animation/UniAnimate/data/saved_pose/musk/0027.jpg new file mode 100644 index 0000000..6016262 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0027.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0028.jpg b/animation/UniAnimate/data/saved_pose/musk/0028.jpg new file mode 100644 index 0000000..58fdcd2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0028.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0029.jpg b/animation/UniAnimate/data/saved_pose/musk/0029.jpg new file mode 100644 index 0000000..7ade3f5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0029.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0030.jpg b/animation/UniAnimate/data/saved_pose/musk/0030.jpg new file mode 100644 index 0000000..b2518bf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0030.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0031.jpg b/animation/UniAnimate/data/saved_pose/musk/0031.jpg new file mode 100644 index 0000000..ddc42fc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0031.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0032.jpg b/animation/UniAnimate/data/saved_pose/musk/0032.jpg new file mode 100644 index 0000000..07da1bb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0032.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0033.jpg b/animation/UniAnimate/data/saved_pose/musk/0033.jpg new file mode 100644 index 0000000..0302d76 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0033.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0034.jpg b/animation/UniAnimate/data/saved_pose/musk/0034.jpg new file mode 100644 index 0000000..ac1d5c4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0034.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0035.jpg b/animation/UniAnimate/data/saved_pose/musk/0035.jpg new file mode 100644 index 0000000..84ff590 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0035.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0036.jpg b/animation/UniAnimate/data/saved_pose/musk/0036.jpg new file mode 100644 index 0000000..4e09e39 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0036.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0037.jpg b/animation/UniAnimate/data/saved_pose/musk/0037.jpg new file mode 100644 index 0000000..00e6775 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0037.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0038.jpg b/animation/UniAnimate/data/saved_pose/musk/0038.jpg new file mode 100644 index 0000000..c6b9be6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0038.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0039.jpg b/animation/UniAnimate/data/saved_pose/musk/0039.jpg new file mode 100644 index 0000000..f87f866 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0039.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0040.jpg b/animation/UniAnimate/data/saved_pose/musk/0040.jpg new file mode 100644 index 0000000..d04ecfd Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0040.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0041.jpg b/animation/UniAnimate/data/saved_pose/musk/0041.jpg new file mode 100644 index 0000000..33bc53e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0041.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0042.jpg b/animation/UniAnimate/data/saved_pose/musk/0042.jpg new file mode 100644 index 0000000..4d6f825 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0042.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0043.jpg b/animation/UniAnimate/data/saved_pose/musk/0043.jpg new file mode 100644 index 0000000..f3f83b9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0043.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0044.jpg b/animation/UniAnimate/data/saved_pose/musk/0044.jpg new file mode 100644 index 0000000..3552c91 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0044.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0045.jpg b/animation/UniAnimate/data/saved_pose/musk/0045.jpg new file mode 100644 index 0000000..78104bb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0045.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0046.jpg b/animation/UniAnimate/data/saved_pose/musk/0046.jpg new file mode 100644 index 0000000..af42223 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0046.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0047.jpg b/animation/UniAnimate/data/saved_pose/musk/0047.jpg new file mode 100644 index 0000000..ab26644 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0047.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0048.jpg b/animation/UniAnimate/data/saved_pose/musk/0048.jpg new file mode 100644 index 0000000..2778396 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0048.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0049.jpg b/animation/UniAnimate/data/saved_pose/musk/0049.jpg new file mode 100644 index 0000000..465c87c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0049.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0050.jpg b/animation/UniAnimate/data/saved_pose/musk/0050.jpg new file mode 100644 index 0000000..dd6d741 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0050.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0051.jpg b/animation/UniAnimate/data/saved_pose/musk/0051.jpg new file mode 100644 index 0000000..dd28dbb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0051.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0052.jpg b/animation/UniAnimate/data/saved_pose/musk/0052.jpg new file mode 100644 index 0000000..3341247 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0052.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0053.jpg b/animation/UniAnimate/data/saved_pose/musk/0053.jpg new file mode 100644 index 0000000..1f6d128 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0053.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0054.jpg b/animation/UniAnimate/data/saved_pose/musk/0054.jpg new file mode 100644 index 0000000..e966917 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0054.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0055.jpg b/animation/UniAnimate/data/saved_pose/musk/0055.jpg new file mode 100644 index 0000000..bc74263 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0055.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0056.jpg b/animation/UniAnimate/data/saved_pose/musk/0056.jpg new file mode 100644 index 0000000..27ea90e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0056.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0057.jpg b/animation/UniAnimate/data/saved_pose/musk/0057.jpg new file mode 100644 index 0000000..0d1837c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0057.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0058.jpg b/animation/UniAnimate/data/saved_pose/musk/0058.jpg new file mode 100644 index 0000000..0043133 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0058.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0059.jpg b/animation/UniAnimate/data/saved_pose/musk/0059.jpg new file mode 100644 index 0000000..1fdcea0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0059.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0060.jpg b/animation/UniAnimate/data/saved_pose/musk/0060.jpg new file mode 100644 index 0000000..279fdc0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0060.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0061.jpg b/animation/UniAnimate/data/saved_pose/musk/0061.jpg new file mode 100644 index 0000000..9628240 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0061.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0062.jpg b/animation/UniAnimate/data/saved_pose/musk/0062.jpg new file mode 100644 index 0000000..14c2826 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0062.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0063.jpg b/animation/UniAnimate/data/saved_pose/musk/0063.jpg new file mode 100644 index 0000000..26fb2cb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0063.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0064.jpg b/animation/UniAnimate/data/saved_pose/musk/0064.jpg new file mode 100644 index 0000000..13522ee Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0064.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0065.jpg b/animation/UniAnimate/data/saved_pose/musk/0065.jpg new file mode 100644 index 0000000..7b4f3c4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0065.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0066.jpg b/animation/UniAnimate/data/saved_pose/musk/0066.jpg new file mode 100644 index 0000000..4b7125c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0066.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0067.jpg b/animation/UniAnimate/data/saved_pose/musk/0067.jpg new file mode 100644 index 0000000..4e0c7c6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0067.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0068.jpg b/animation/UniAnimate/data/saved_pose/musk/0068.jpg new file mode 100644 index 0000000..42ca9f6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0068.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0069.jpg b/animation/UniAnimate/data/saved_pose/musk/0069.jpg new file mode 100644 index 0000000..3763a53 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0069.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0070.jpg b/animation/UniAnimate/data/saved_pose/musk/0070.jpg new file mode 100644 index 0000000..ba4ba6c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0070.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0071.jpg b/animation/UniAnimate/data/saved_pose/musk/0071.jpg new file mode 100644 index 0000000..c32ce52 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0071.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0072.jpg b/animation/UniAnimate/data/saved_pose/musk/0072.jpg new file mode 100644 index 0000000..cab89c4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0072.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0073.jpg b/animation/UniAnimate/data/saved_pose/musk/0073.jpg new file mode 100644 index 0000000..9b9cfc4 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0073.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0074.jpg b/animation/UniAnimate/data/saved_pose/musk/0074.jpg new file mode 100644 index 0000000..087ca2d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0074.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0075.jpg b/animation/UniAnimate/data/saved_pose/musk/0075.jpg new file mode 100644 index 0000000..b972676 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0075.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0076.jpg b/animation/UniAnimate/data/saved_pose/musk/0076.jpg new file mode 100644 index 0000000..47cb2db Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0076.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0077.jpg b/animation/UniAnimate/data/saved_pose/musk/0077.jpg new file mode 100644 index 0000000..3b0ad17 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0077.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0078.jpg b/animation/UniAnimate/data/saved_pose/musk/0078.jpg new file mode 100644 index 0000000..f8ae81f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0078.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0079.jpg b/animation/UniAnimate/data/saved_pose/musk/0079.jpg new file mode 100644 index 0000000..73e2eea Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0079.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0080.jpg b/animation/UniAnimate/data/saved_pose/musk/0080.jpg new file mode 100644 index 0000000..85ccf41 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0080.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0081.jpg b/animation/UniAnimate/data/saved_pose/musk/0081.jpg new file mode 100644 index 0000000..f68c7f0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0081.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0082.jpg b/animation/UniAnimate/data/saved_pose/musk/0082.jpg new file mode 100644 index 0000000..c7853f0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0082.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0083.jpg b/animation/UniAnimate/data/saved_pose/musk/0083.jpg new file mode 100644 index 0000000..5d943df Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0083.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0084.jpg b/animation/UniAnimate/data/saved_pose/musk/0084.jpg new file mode 100644 index 0000000..fd0c28d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0084.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0085.jpg b/animation/UniAnimate/data/saved_pose/musk/0085.jpg new file mode 100644 index 0000000..9510b42 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0085.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0086.jpg b/animation/UniAnimate/data/saved_pose/musk/0086.jpg new file mode 100644 index 0000000..55007da Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0086.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0087.jpg b/animation/UniAnimate/data/saved_pose/musk/0087.jpg new file mode 100644 index 0000000..5a40f2d Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0087.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0088.jpg b/animation/UniAnimate/data/saved_pose/musk/0088.jpg new file mode 100644 index 0000000..4c53596 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0088.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0089.jpg b/animation/UniAnimate/data/saved_pose/musk/0089.jpg new file mode 100644 index 0000000..d284ed2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0089.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0090.jpg b/animation/UniAnimate/data/saved_pose/musk/0090.jpg new file mode 100644 index 0000000..96a5b6c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0090.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0091.jpg b/animation/UniAnimate/data/saved_pose/musk/0091.jpg new file mode 100644 index 0000000..7b773dc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0091.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0092.jpg b/animation/UniAnimate/data/saved_pose/musk/0092.jpg new file mode 100644 index 0000000..2858997 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0092.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0093.jpg b/animation/UniAnimate/data/saved_pose/musk/0093.jpg new file mode 100644 index 0000000..bf66f93 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0093.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0094.jpg b/animation/UniAnimate/data/saved_pose/musk/0094.jpg new file mode 100644 index 0000000..737379a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0094.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0095.jpg b/animation/UniAnimate/data/saved_pose/musk/0095.jpg new file mode 100644 index 0000000..14b5891 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0095.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0096.jpg b/animation/UniAnimate/data/saved_pose/musk/0096.jpg new file mode 100644 index 0000000..be8f7ae Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0096.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0097.jpg b/animation/UniAnimate/data/saved_pose/musk/0097.jpg new file mode 100644 index 0000000..f507613 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0097.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0098.jpg b/animation/UniAnimate/data/saved_pose/musk/0098.jpg new file mode 100644 index 0000000..72a51f6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0098.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0099.jpg b/animation/UniAnimate/data/saved_pose/musk/0099.jpg new file mode 100644 index 0000000..60b0bb2 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0099.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0100.jpg b/animation/UniAnimate/data/saved_pose/musk/0100.jpg new file mode 100644 index 0000000..1d6b6ba Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0100.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0101.jpg b/animation/UniAnimate/data/saved_pose/musk/0101.jpg new file mode 100644 index 0000000..604f9d9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0101.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0102.jpg b/animation/UniAnimate/data/saved_pose/musk/0102.jpg new file mode 100644 index 0000000..605d232 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0102.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0103.jpg b/animation/UniAnimate/data/saved_pose/musk/0103.jpg new file mode 100644 index 0000000..ee53b06 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0103.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0104.jpg b/animation/UniAnimate/data/saved_pose/musk/0104.jpg new file mode 100644 index 0000000..3b5e0e3 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0104.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0105.jpg b/animation/UniAnimate/data/saved_pose/musk/0105.jpg new file mode 100644 index 0000000..22105fc Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0105.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0106.jpg b/animation/UniAnimate/data/saved_pose/musk/0106.jpg new file mode 100644 index 0000000..f1686c9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0106.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0107.jpg b/animation/UniAnimate/data/saved_pose/musk/0107.jpg new file mode 100644 index 0000000..32324e1 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0107.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0108.jpg b/animation/UniAnimate/data/saved_pose/musk/0108.jpg new file mode 100644 index 0000000..2271277 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0108.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0109.jpg b/animation/UniAnimate/data/saved_pose/musk/0109.jpg new file mode 100644 index 0000000..0d11215 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0109.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0110.jpg b/animation/UniAnimate/data/saved_pose/musk/0110.jpg new file mode 100644 index 0000000..e832e5f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0110.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0111.jpg b/animation/UniAnimate/data/saved_pose/musk/0111.jpg new file mode 100644 index 0000000..8babf81 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0111.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0112.jpg b/animation/UniAnimate/data/saved_pose/musk/0112.jpg new file mode 100644 index 0000000..2935159 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0112.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0113.jpg b/animation/UniAnimate/data/saved_pose/musk/0113.jpg new file mode 100644 index 0000000..aaaf080 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0113.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0114.jpg b/animation/UniAnimate/data/saved_pose/musk/0114.jpg new file mode 100644 index 0000000..2916619 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0114.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0115.jpg b/animation/UniAnimate/data/saved_pose/musk/0115.jpg new file mode 100644 index 0000000..56b9e29 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0115.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0116.jpg b/animation/UniAnimate/data/saved_pose/musk/0116.jpg new file mode 100644 index 0000000..97c45f0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0116.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0117.jpg b/animation/UniAnimate/data/saved_pose/musk/0117.jpg new file mode 100644 index 0000000..0ff4116 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0117.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0118.jpg b/animation/UniAnimate/data/saved_pose/musk/0118.jpg new file mode 100644 index 0000000..e523566 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0118.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0119.jpg b/animation/UniAnimate/data/saved_pose/musk/0119.jpg new file mode 100644 index 0000000..bd5605c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0119.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0120.jpg b/animation/UniAnimate/data/saved_pose/musk/0120.jpg new file mode 100644 index 0000000..c356480 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0120.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0121.jpg b/animation/UniAnimate/data/saved_pose/musk/0121.jpg new file mode 100644 index 0000000..1c5c538 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0121.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0122.jpg b/animation/UniAnimate/data/saved_pose/musk/0122.jpg new file mode 100644 index 0000000..f68781a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0122.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0123.jpg b/animation/UniAnimate/data/saved_pose/musk/0123.jpg new file mode 100644 index 0000000..56b9b38 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0123.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0124.jpg b/animation/UniAnimate/data/saved_pose/musk/0124.jpg new file mode 100644 index 0000000..5d81d71 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0124.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0125.jpg b/animation/UniAnimate/data/saved_pose/musk/0125.jpg new file mode 100644 index 0000000..586a3b6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0125.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0126.jpg b/animation/UniAnimate/data/saved_pose/musk/0126.jpg new file mode 100644 index 0000000..05c665a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0126.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0127.jpg b/animation/UniAnimate/data/saved_pose/musk/0127.jpg new file mode 100644 index 0000000..181bc97 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0127.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0128.jpg b/animation/UniAnimate/data/saved_pose/musk/0128.jpg new file mode 100644 index 0000000..db25646 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0128.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0129.jpg b/animation/UniAnimate/data/saved_pose/musk/0129.jpg new file mode 100644 index 0000000..a42e1ae Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0129.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0130.jpg b/animation/UniAnimate/data/saved_pose/musk/0130.jpg new file mode 100644 index 0000000..e03f95e Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0130.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0131.jpg b/animation/UniAnimate/data/saved_pose/musk/0131.jpg new file mode 100644 index 0000000..046038c Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0131.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0132.jpg b/animation/UniAnimate/data/saved_pose/musk/0132.jpg new file mode 100644 index 0000000..cd96fb5 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0132.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0133.jpg b/animation/UniAnimate/data/saved_pose/musk/0133.jpg new file mode 100644 index 0000000..3ba0145 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0133.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0134.jpg b/animation/UniAnimate/data/saved_pose/musk/0134.jpg new file mode 100644 index 0000000..8c4cc06 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0134.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0135.jpg b/animation/UniAnimate/data/saved_pose/musk/0135.jpg new file mode 100644 index 0000000..5712f81 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0135.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0136.jpg b/animation/UniAnimate/data/saved_pose/musk/0136.jpg new file mode 100644 index 0000000..d2877c9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0136.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0137.jpg b/animation/UniAnimate/data/saved_pose/musk/0137.jpg new file mode 100644 index 0000000..61cb23f Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0137.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0138.jpg b/animation/UniAnimate/data/saved_pose/musk/0138.jpg new file mode 100644 index 0000000..af81451 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0138.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0139.jpg b/animation/UniAnimate/data/saved_pose/musk/0139.jpg new file mode 100644 index 0000000..26486b0 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0139.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0140.jpg b/animation/UniAnimate/data/saved_pose/musk/0140.jpg new file mode 100644 index 0000000..07818d6 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0140.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0141.jpg b/animation/UniAnimate/data/saved_pose/musk/0141.jpg new file mode 100644 index 0000000..7ccb395 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0141.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0142.jpg b/animation/UniAnimate/data/saved_pose/musk/0142.jpg new file mode 100644 index 0000000..a773aff Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0142.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0143.jpg b/animation/UniAnimate/data/saved_pose/musk/0143.jpg new file mode 100644 index 0000000..4c17465 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0143.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0144.jpg b/animation/UniAnimate/data/saved_pose/musk/0144.jpg new file mode 100644 index 0000000..55d6edf Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0144.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0145.jpg b/animation/UniAnimate/data/saved_pose/musk/0145.jpg new file mode 100644 index 0000000..65d90fb Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0145.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0146.jpg b/animation/UniAnimate/data/saved_pose/musk/0146.jpg new file mode 100644 index 0000000..f69e462 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0146.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0147.jpg b/animation/UniAnimate/data/saved_pose/musk/0147.jpg new file mode 100644 index 0000000..e4ce12a Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0147.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0148.jpg b/animation/UniAnimate/data/saved_pose/musk/0148.jpg new file mode 100644 index 0000000..c05441b Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0148.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/0149.jpg b/animation/UniAnimate/data/saved_pose/musk/0149.jpg new file mode 100644 index 0000000..a75aeed Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/0149.jpg differ diff --git a/animation/UniAnimate/data/saved_pose/musk/ref_pose.jpg b/animation/UniAnimate/data/saved_pose/musk/ref_pose.jpg new file mode 100644 index 0000000..7ba93a9 Binary files /dev/null and b/animation/UniAnimate/data/saved_pose/musk/ref_pose.jpg differ diff --git a/animation/UniAnimate/data/videos/dance.mp4 b/animation/UniAnimate/data/videos/dance.mp4 new file mode 100644 index 0000000..f0ba10b Binary files /dev/null and b/animation/UniAnimate/data/videos/dance.mp4 differ diff --git a/animation/UniAnimate/data/videos/source_video.mp4 b/animation/UniAnimate/data/videos/source_video.mp4 new file mode 100644 index 0000000..ae08d10 Binary files /dev/null and b/animation/UniAnimate/data/videos/source_video.mp4 differ diff --git a/animation/UniAnimate/dwpose/__init__.py b/animation/UniAnimate/dwpose/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/animation/UniAnimate/dwpose/onnxdet.py b/animation/UniAnimate/dwpose/onnxdet.py new file mode 100644 index 0000000..15ae797 --- /dev/null +++ b/animation/UniAnimate/dwpose/onnxdet.py @@ -0,0 +1,127 @@ +import cv2 +import numpy as np + +import onnxruntime + +def nms(boxes, scores, nms_thr): + """Single class NMS implemented in Numpy.""" + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= nms_thr)[0] + order = order[inds + 1] + + return keep + +def multiclass_nms(boxes, scores, nms_thr, score_thr): + """Multiclass NMS implemented in Numpy. Class-aware version.""" + final_dets = [] + num_classes = scores.shape[1] + for cls_ind in range(num_classes): + cls_scores = scores[:, cls_ind] + valid_score_mask = cls_scores > score_thr + if valid_score_mask.sum() == 0: + continue + else: + valid_scores = cls_scores[valid_score_mask] + valid_boxes = boxes[valid_score_mask] + keep = nms(valid_boxes, valid_scores, nms_thr) + if len(keep) > 0: + cls_inds = np.ones((len(keep), 1)) * cls_ind + dets = np.concatenate( + [valid_boxes[keep], valid_scores[keep, None], cls_inds], 1 + ) + final_dets.append(dets) + if len(final_dets) == 0: + return None + return np.concatenate(final_dets, 0) + +def demo_postprocess(outputs, img_size, p6=False): + grids = [] + expanded_strides = [] + strides = [8, 16, 32] if not p6 else [8, 16, 32, 64] + + hsizes = [img_size[0] // stride for stride in strides] + wsizes = [img_size[1] // stride for stride in strides] + + for hsize, wsize, stride in zip(hsizes, wsizes, strides): + xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize)) + grid = np.stack((xv, yv), 2).reshape(1, -1, 2) + grids.append(grid) + shape = grid.shape[:2] + expanded_strides.append(np.full((*shape, 1), stride)) + + grids = np.concatenate(grids, 1) + expanded_strides = np.concatenate(expanded_strides, 1) + outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides + outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides + + return outputs + +def preprocess(img, input_size, swap=(2, 0, 1)): + if len(img.shape) == 3: + padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114 + else: + padded_img = np.ones(input_size, dtype=np.uint8) * 114 + + r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1]) + resized_img = cv2.resize( + img, + (int(img.shape[1] * r), int(img.shape[0] * r)), + interpolation=cv2.INTER_LINEAR, + ).astype(np.uint8) + padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img + + padded_img = padded_img.transpose(swap) + padded_img = np.ascontiguousarray(padded_img, dtype=np.float32) + return padded_img, r + +def inference_detector(session, oriImg): + input_shape = (640,640) + img, ratio = preprocess(oriImg, input_shape) + + ort_inputs = {session.get_inputs()[0].name: img[None, :, :, :]} + + output = session.run(None, ort_inputs) + + predictions = demo_postprocess(output[0], input_shape)[0] + + boxes = predictions[:, :4] + scores = predictions[:, 4:5] * predictions[:, 5:] + + boxes_xyxy = np.ones_like(boxes) + boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2]/2. + boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3]/2. + boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2]/2. + boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3]/2. + boxes_xyxy /= ratio + dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1) + if dets is not None: + final_boxes, final_scores, final_cls_inds = dets[:, :4], dets[:, 4], dets[:, 5] + isscore = final_scores>0.3 + iscat = final_cls_inds == 0 + isbbox = [ i and j for (i, j) in zip(isscore, iscat)] + final_boxes = final_boxes[isbbox] + else: + final_boxes = np.array([]) + + return final_boxes diff --git a/animation/UniAnimate/dwpose/onnxpose.py b/animation/UniAnimate/dwpose/onnxpose.py new file mode 100644 index 0000000..79cd4a0 --- /dev/null +++ b/animation/UniAnimate/dwpose/onnxpose.py @@ -0,0 +1,360 @@ +from typing import List, Tuple + +import cv2 +import numpy as np +import onnxruntime as ort + +def preprocess( + img: np.ndarray, out_bbox, input_size: Tuple[int, int] = (192, 256) +) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """Do preprocessing for RTMPose model inference. + + Args: + img (np.ndarray): Input image in shape. + input_size (tuple): Input image size in shape (w, h). + + Returns: + tuple: + - resized_img (np.ndarray): Preprocessed image. + - center (np.ndarray): Center of image. + - scale (np.ndarray): Scale of image. + """ + # get shape of image + img_shape = img.shape[:2] + out_img, out_center, out_scale = [], [], [] + if len(out_bbox) == 0: + out_bbox = [[0, 0, img_shape[1], img_shape[0]]] + for i in range(len(out_bbox)): + x0 = out_bbox[i][0] + y0 = out_bbox[i][1] + x1 = out_bbox[i][2] + y1 = out_bbox[i][3] + bbox = np.array([x0, y0, x1, y1]) + + # get center and scale + center, scale = bbox_xyxy2cs(bbox, padding=1.25) + + # do affine transformation + resized_img, scale = top_down_affine(input_size, scale, center, img) + + # normalize image + mean = np.array([123.675, 116.28, 103.53]) + std = np.array([58.395, 57.12, 57.375]) + resized_img = (resized_img - mean) / std + + out_img.append(resized_img) + out_center.append(center) + out_scale.append(scale) + + return out_img, out_center, out_scale + + +def inference(sess: ort.InferenceSession, img: np.ndarray) -> np.ndarray: + """Inference RTMPose model. + + Args: + sess (ort.InferenceSession): ONNXRuntime session. + img (np.ndarray): Input image in shape. + + Returns: + outputs (np.ndarray): Output of RTMPose model. + """ + all_out = [] + # build input + for i in range(len(img)): + input = [img[i].transpose(2, 0, 1)] + + # build output + sess_input = {sess.get_inputs()[0].name: input} + sess_output = [] + for out in sess.get_outputs(): + sess_output.append(out.name) + + # run model + outputs = sess.run(sess_output, sess_input) + all_out.append(outputs) + + return all_out + + +def postprocess(outputs: List[np.ndarray], + model_input_size: Tuple[int, int], + center: Tuple[int, int], + scale: Tuple[int, int], + simcc_split_ratio: float = 2.0 + ) -> Tuple[np.ndarray, np.ndarray]: + """Postprocess for RTMPose model output. + + Args: + outputs (np.ndarray): Output of RTMPose model. + model_input_size (tuple): RTMPose model Input image size. + center (tuple): Center of bbox in shape (x, y). + scale (tuple): Scale of bbox in shape (w, h). + simcc_split_ratio (float): Split ratio of simcc. + + Returns: + tuple: + - keypoints (np.ndarray): Rescaled keypoints. + - scores (np.ndarray): Model predict scores. + """ + all_key = [] + all_score = [] + for i in range(len(outputs)): + # use simcc to decode + simcc_x, simcc_y = outputs[i] + keypoints, scores = decode(simcc_x, simcc_y, simcc_split_ratio) + + # rescale keypoints + keypoints = keypoints / model_input_size * scale[i] + center[i] - scale[i] / 2 + all_key.append(keypoints[0]) + all_score.append(scores[0]) + + return np.array(all_key), np.array(all_score) + + +def bbox_xyxy2cs(bbox: np.ndarray, + padding: float = 1.) -> Tuple[np.ndarray, np.ndarray]: + """Transform the bbox format from (x,y,w,h) into (center, scale) + + Args: + bbox (ndarray): Bounding box(es) in shape (4,) or (n, 4), formatted + as (left, top, right, bottom) + padding (float): BBox padding factor that will be multilied to scale. + Default: 1.0 + + Returns: + tuple: A tuple containing center and scale. + - np.ndarray[float32]: Center (x, y) of the bbox in shape (2,) or + (n, 2) + - np.ndarray[float32]: Scale (w, h) of the bbox in shape (2,) or + (n, 2) + """ + # convert single bbox from (4, ) to (1, 4) + dim = bbox.ndim + if dim == 1: + bbox = bbox[None, :] + + # get bbox center and scale + x1, y1, x2, y2 = np.hsplit(bbox, [1, 2, 3]) + center = np.hstack([x1 + x2, y1 + y2]) * 0.5 + scale = np.hstack([x2 - x1, y2 - y1]) * padding + + if dim == 1: + center = center[0] + scale = scale[0] + + return center, scale + + +def _fix_aspect_ratio(bbox_scale: np.ndarray, + aspect_ratio: float) -> np.ndarray: + """Extend the scale to match the given aspect ratio. + + Args: + scale (np.ndarray): The image scale (w, h) in shape (2, ) + aspect_ratio (float): The ratio of ``w/h`` + + Returns: + np.ndarray: The reshaped image scale in (2, ) + """ + w, h = np.hsplit(bbox_scale, [1]) + bbox_scale = np.where(w > h * aspect_ratio, + np.hstack([w, w / aspect_ratio]), + np.hstack([h * aspect_ratio, h])) + return bbox_scale + + +def _rotate_point(pt: np.ndarray, angle_rad: float) -> np.ndarray: + """Rotate a point by an angle. + + Args: + pt (np.ndarray): 2D point coordinates (x, y) in shape (2, ) + angle_rad (float): rotation angle in radian + + Returns: + np.ndarray: Rotated point in shape (2, ) + """ + sn, cs = np.sin(angle_rad), np.cos(angle_rad) + rot_mat = np.array([[cs, -sn], [sn, cs]]) + return rot_mat @ pt + + +def _get_3rd_point(a: np.ndarray, b: np.ndarray) -> np.ndarray: + """To calculate the affine matrix, three pairs of points are required. This + function is used to get the 3rd point, given 2D points a & b. + + The 3rd point is defined by rotating vector `a - b` by 90 degrees + anticlockwise, using b as the rotation center. + + Args: + a (np.ndarray): The 1st point (x,y) in shape (2, ) + b (np.ndarray): The 2nd point (x,y) in shape (2, ) + + Returns: + np.ndarray: The 3rd point. + """ + direction = a - b + c = b + np.r_[-direction[1], direction[0]] + return c + + +def get_warp_matrix(center: np.ndarray, + scale: np.ndarray, + rot: float, + output_size: Tuple[int, int], + shift: Tuple[float, float] = (0., 0.), + inv: bool = False) -> np.ndarray: + """Calculate the affine transformation matrix that can warp the bbox area + in the input image to the output size. + + Args: + center (np.ndarray[2, ]): Center of the bounding box (x, y). + scale (np.ndarray[2, ]): Scale of the bounding box + wrt [width, height]. + rot (float): Rotation angle (degree). + output_size (np.ndarray[2, ] | list(2,)): Size of the + destination heatmaps. + shift (0-100%): Shift translation ratio wrt the width/height. + Default (0., 0.). + inv (bool): Option to inverse the affine transform direction. + (inv=False: src->dst or inv=True: dst->src) + + Returns: + np.ndarray: A 2x3 transformation matrix + """ + shift = np.array(shift) + src_w = scale[0] + dst_w = output_size[0] + dst_h = output_size[1] + + # compute transformation matrix + rot_rad = np.deg2rad(rot) + src_dir = _rotate_point(np.array([0., src_w * -0.5]), rot_rad) + dst_dir = np.array([0., dst_w * -0.5]) + + # get four corners of the src rectangle in the original image + src = np.zeros((3, 2), dtype=np.float32) + src[0, :] = center + scale * shift + src[1, :] = center + src_dir + scale * shift + src[2, :] = _get_3rd_point(src[0, :], src[1, :]) + + # get four corners of the dst rectangle in the input image + dst = np.zeros((3, 2), dtype=np.float32) + dst[0, :] = [dst_w * 0.5, dst_h * 0.5] + dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir + dst[2, :] = _get_3rd_point(dst[0, :], dst[1, :]) + + if inv: + warp_mat = cv2.getAffineTransform(np.float32(dst), np.float32(src)) + else: + warp_mat = cv2.getAffineTransform(np.float32(src), np.float32(dst)) + + return warp_mat + + +def top_down_affine(input_size: dict, bbox_scale: dict, bbox_center: dict, + img: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + """Get the bbox image as the model input by affine transform. + + Args: + input_size (dict): The input size of the model. + bbox_scale (dict): The bbox scale of the img. + bbox_center (dict): The bbox center of the img. + img (np.ndarray): The original image. + + Returns: + tuple: A tuple containing center and scale. + - np.ndarray[float32]: img after affine transform. + - np.ndarray[float32]: bbox scale after affine transform. + """ + w, h = input_size + warp_size = (int(w), int(h)) + + # reshape bbox to fixed aspect ratio + bbox_scale = _fix_aspect_ratio(bbox_scale, aspect_ratio=w / h) + + # get the affine matrix + center = bbox_center + scale = bbox_scale + rot = 0 + warp_mat = get_warp_matrix(center, scale, rot, output_size=(w, h)) + + # do affine transform + img = cv2.warpAffine(img, warp_mat, warp_size, flags=cv2.INTER_LINEAR) + + return img, bbox_scale + + +def get_simcc_maximum(simcc_x: np.ndarray, + simcc_y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + """Get maximum response location and value from simcc representations. + + Note: + instance number: N + num_keypoints: K + heatmap height: H + heatmap width: W + + Args: + simcc_x (np.ndarray): x-axis SimCC in shape (K, Wx) or (N, K, Wx) + simcc_y (np.ndarray): y-axis SimCC in shape (K, Wy) or (N, K, Wy) + + Returns: + tuple: + - locs (np.ndarray): locations of maximum heatmap responses in shape + (K, 2) or (N, K, 2) + - vals (np.ndarray): values of maximum heatmap responses in shape + (K,) or (N, K) + """ + N, K, Wx = simcc_x.shape + simcc_x = simcc_x.reshape(N * K, -1) + simcc_y = simcc_y.reshape(N * K, -1) + + # get maximum value locations + x_locs = np.argmax(simcc_x, axis=1) + y_locs = np.argmax(simcc_y, axis=1) + locs = np.stack((x_locs, y_locs), axis=-1).astype(np.float32) + max_val_x = np.amax(simcc_x, axis=1) + max_val_y = np.amax(simcc_y, axis=1) + + # get maximum value across x and y axis + mask = max_val_x > max_val_y + max_val_x[mask] = max_val_y[mask] + vals = max_val_x + locs[vals <= 0.] = -1 + + # reshape + locs = locs.reshape(N, K, 2) + vals = vals.reshape(N, K) + + return locs, vals + + +def decode(simcc_x: np.ndarray, simcc_y: np.ndarray, + simcc_split_ratio) -> Tuple[np.ndarray, np.ndarray]: + """Modulate simcc distribution with Gaussian. + + Args: + simcc_x (np.ndarray[K, Wx]): model predicted simcc in x. + simcc_y (np.ndarray[K, Wy]): model predicted simcc in y. + simcc_split_ratio (int): The split ratio of simcc. + + Returns: + tuple: A tuple containing center and scale. + - np.ndarray[float32]: keypoints in shape (K, 2) or (n, K, 2) + - np.ndarray[float32]: scores in shape (K,) or (n, K) + """ + keypoints, scores = get_simcc_maximum(simcc_x, simcc_y) + keypoints /= simcc_split_ratio + + return keypoints, scores + + +def inference_pose(session, out_bbox, oriImg): + h, w = session.get_inputs()[0].shape[2:] + model_input_size = (w, h) + resized_img, center, scale = preprocess(oriImg, out_bbox, model_input_size) + outputs = inference(session, resized_img) + keypoints, scores = postprocess(outputs, model_input_size, center, scale) + + return keypoints, scores \ No newline at end of file diff --git a/animation/UniAnimate/dwpose/util.py b/animation/UniAnimate/dwpose/util.py new file mode 100644 index 0000000..2f83229 --- /dev/null +++ b/animation/UniAnimate/dwpose/util.py @@ -0,0 +1,336 @@ +import math +import numpy as np +import matplotlib +import cv2 + + +eps = 0.01 + + +def smart_resize(x, s): + Ht, Wt = s + if x.ndim == 2: + Ho, Wo = x.shape + Co = 1 + else: + Ho, Wo, Co = x.shape + if Co == 3 or Co == 1: + k = float(Ht + Wt) / float(Ho + Wo) + return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4) + else: + return np.stack([smart_resize(x[:, :, i], s) for i in range(Co)], axis=2) + + +def smart_resize_k(x, fx, fy): + if x.ndim == 2: + Ho, Wo = x.shape + Co = 1 + else: + Ho, Wo, Co = x.shape + Ht, Wt = Ho * fy, Wo * fx + if Co == 3 or Co == 1: + k = float(Ht + Wt) / float(Ho + Wo) + return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4) + else: + return np.stack([smart_resize_k(x[:, :, i], fx, fy) for i in range(Co)], axis=2) + + +def padRightDownCorner(img, stride, padValue): + h = img.shape[0] + w = img.shape[1] + + pad = 4 * [None] + pad[0] = 0 # up + pad[1] = 0 # left + pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down + pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right + + img_padded = img + pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1)) + img_padded = np.concatenate((pad_up, img_padded), axis=0) + pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1)) + img_padded = np.concatenate((pad_left, img_padded), axis=1) + pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1)) + img_padded = np.concatenate((img_padded, pad_down), axis=0) + pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1)) + img_padded = np.concatenate((img_padded, pad_right), axis=1) + + return img_padded, pad + + +def transfer(model, model_weights): + transfered_model_weights = {} + for weights_name in model.state_dict().keys(): + transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])] + return transfered_model_weights + + +def draw_bodypose(canvas, candidate, subset): + H, W, C = canvas.shape + candidate = np.array(candidate) + subset = np.array(subset) + + stickwidth = 4 + + limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \ + [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \ + [1, 16], [16, 18], [3, 17], [6, 18]] + + colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \ + [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \ + [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]] + + for i in range(17): + for n in range(len(subset)): + index = subset[n][np.array(limbSeq[i]) - 1] + if -1 in index: + continue + Y = candidate[index.astype(int), 0] * float(W) + X = candidate[index.astype(int), 1] * float(H) + mX = np.mean(X) + mY = np.mean(Y) + length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 + angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) + polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1) + cv2.fillConvexPoly(canvas, polygon, colors[i]) + + canvas = (canvas * 0.6).astype(np.uint8) + + for i in range(18): + for n in range(len(subset)): + index = int(subset[n][i]) + if index == -1: + continue + x, y = candidate[index][0:2] + x = int(x * W) + y = int(y * H) + cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1) + + return canvas + + +def draw_body_and_foot(canvas, candidate, subset): + H, W, C = canvas.shape + candidate = np.array(candidate) + subset = np.array(subset) + + stickwidth = 4 + + limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \ + [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \ + [1, 16], [16, 18], [14,19], [11, 20]] + + colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \ + [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \ + [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85], [170, 255, 255], [255, 255, 0]] + + for i in range(19): + for n in range(len(subset)): + index = subset[n][np.array(limbSeq[i]) - 1] + if -1 in index: + continue + Y = candidate[index.astype(int), 0] * float(W) + X = candidate[index.astype(int), 1] * float(H) + mX = np.mean(X) + mY = np.mean(Y) + length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5 + angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1])) + polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1) + cv2.fillConvexPoly(canvas, polygon, colors[i]) + + canvas = (canvas * 0.6).astype(np.uint8) + + for i in range(20): + for n in range(len(subset)): + index = int(subset[n][i]) + if index == -1: + continue + x, y = candidate[index][0:2] + x = int(x * W) + y = int(y * H) + cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1) + + return canvas + + +def draw_handpose(canvas, all_hand_peaks): + H, W, C = canvas.shape + + edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \ + [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]] + + for peaks in all_hand_peaks: + peaks = np.array(peaks) + + for ie, e in enumerate(edges): + x1, y1 = peaks[e[0]] + x2, y2 = peaks[e[1]] + x1 = int(x1 * W) + y1 = int(y1 * H) + x2 = int(x2 * W) + y2 = int(y2 * H) + if x1 > eps and y1 > eps and x2 > eps and y2 > eps: + cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255, thickness=2) + + for i, keyponit in enumerate(peaks): + x, y = keyponit + x = int(x * W) + y = int(y * H) + if x > eps and y > eps: + cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1) + return canvas + + +def draw_facepose(canvas, all_lmks): + H, W, C = canvas.shape + for lmks in all_lmks: + lmks = np.array(lmks) + for lmk in lmks: + x, y = lmk + x = int(x * W) + y = int(y * H) + if x > eps and y > eps: + cv2.circle(canvas, (x, y), 3, (255, 255, 255), thickness=-1) + return canvas + + +# detect hand according to body pose keypoints +# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp +def handDetect(candidate, subset, oriImg): + # right hand: wrist 4, elbow 3, shoulder 2 + # left hand: wrist 7, elbow 6, shoulder 5 + ratioWristElbow = 0.33 + detect_result = [] + image_height, image_width = oriImg.shape[0:2] + for person in subset.astype(int): + # if any of three not detected + has_left = np.sum(person[[5, 6, 7]] == -1) == 0 + has_right = np.sum(person[[2, 3, 4]] == -1) == 0 + if not (has_left or has_right): + continue + hands = [] + #left hand + if has_left: + left_shoulder_index, left_elbow_index, left_wrist_index = person[[5, 6, 7]] + x1, y1 = candidate[left_shoulder_index][:2] + x2, y2 = candidate[left_elbow_index][:2] + x3, y3 = candidate[left_wrist_index][:2] + hands.append([x1, y1, x2, y2, x3, y3, True]) + # right hand + if has_right: + right_shoulder_index, right_elbow_index, right_wrist_index = person[[2, 3, 4]] + x1, y1 = candidate[right_shoulder_index][:2] + x2, y2 = candidate[right_elbow_index][:2] + x3, y3 = candidate[right_wrist_index][:2] + hands.append([x1, y1, x2, y2, x3, y3, False]) + + for x1, y1, x2, y2, x3, y3, is_left in hands: + + x = x3 + ratioWristElbow * (x3 - x2) + y = y3 + ratioWristElbow * (y3 - y2) + distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2) + distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2) + width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder) + # x-y refers to the center --> offset to topLeft point + # handRectangle.x -= handRectangle.width / 2.f; + # handRectangle.y -= handRectangle.height / 2.f; + x -= width / 2 + y -= width / 2 # width = height + # overflow the image + if x < 0: x = 0 + if y < 0: y = 0 + width1 = width + width2 = width + if x + width > image_width: width1 = image_width - x + if y + width > image_height: width2 = image_height - y + width = min(width1, width2) + # the max hand box value is 20 pixels + if width >= 20: + detect_result.append([int(x), int(y), int(width), is_left]) + + ''' + return value: [[x, y, w, True if left hand else False]]. + width=height since the network require squared input. + x, y is the coordinate of top left + ''' + return detect_result + + +# Written by Lvmin +def faceDetect(candidate, subset, oriImg): + # left right eye ear 14 15 16 17 + detect_result = [] + image_height, image_width = oriImg.shape[0:2] + for person in subset.astype(int): + has_head = person[0] > -1 + if not has_head: + continue + + has_left_eye = person[14] > -1 + has_right_eye = person[15] > -1 + has_left_ear = person[16] > -1 + has_right_ear = person[17] > -1 + + if not (has_left_eye or has_right_eye or has_left_ear or has_right_ear): + continue + + head, left_eye, right_eye, left_ear, right_ear = person[[0, 14, 15, 16, 17]] + + width = 0.0 + x0, y0 = candidate[head][:2] + + if has_left_eye: + x1, y1 = candidate[left_eye][:2] + d = max(abs(x0 - x1), abs(y0 - y1)) + width = max(width, d * 3.0) + + if has_right_eye: + x1, y1 = candidate[right_eye][:2] + d = max(abs(x0 - x1), abs(y0 - y1)) + width = max(width, d * 3.0) + + if has_left_ear: + x1, y1 = candidate[left_ear][:2] + d = max(abs(x0 - x1), abs(y0 - y1)) + width = max(width, d * 1.5) + + if has_right_ear: + x1, y1 = candidate[right_ear][:2] + d = max(abs(x0 - x1), abs(y0 - y1)) + width = max(width, d * 1.5) + + x, y = x0, y0 + + x -= width + y -= width + + if x < 0: + x = 0 + + if y < 0: + y = 0 + + width1 = width * 2 + width2 = width * 2 + + if x + width > image_width: + width1 = image_width - x + + if y + width > image_height: + width2 = image_height - y + + width = min(width1, width2) + + if width >= 20: + detect_result.append([int(x), int(y), int(width)]) + + return detect_result + + +# get max index of 2d array +def npmax(array): + arrayindex = array.argmax(1) + arrayvalue = array.max(1) + i = arrayvalue.argmax() + j = arrayindex[i] + return i, j diff --git a/animation/UniAnimate/dwpose/wholebody.py b/animation/UniAnimate/dwpose/wholebody.py new file mode 100644 index 0000000..05bbe0b --- /dev/null +++ b/animation/UniAnimate/dwpose/wholebody.py @@ -0,0 +1,48 @@ +import cv2 +import numpy as np + +import onnxruntime as ort +from dwpose.onnxdet import inference_detector +from dwpose.onnxpose import inference_pose + +class Wholebody: + def __init__(self): + device = 'cuda' # 'cpu' # + providers = ['CPUExecutionProvider' + ] if device == 'cpu' else ['CUDAExecutionProvider'] + onnx_det = 'checkpoints/yolox_l.onnx' + onnx_pose = 'checkpoints/dw-ll_ucoco_384.onnx' + + self.session_det = ort.InferenceSession(path_or_bytes=onnx_det, providers=providers) + self.session_pose = ort.InferenceSession(path_or_bytes=onnx_pose, providers=providers) + + def __call__(self, oriImg): + det_result = inference_detector(self.session_det, oriImg) + keypoints, scores = inference_pose(self.session_pose, det_result, oriImg) + + keypoints_info = np.concatenate( + (keypoints, scores[..., None]), axis=-1) + # compute neck joint + neck = np.mean(keypoints_info[:, [5, 6]], axis=1) + # neck score when visualizing pred + neck[:, 2:4] = np.logical_and( + keypoints_info[:, 5, 2:4] > 0.3, + keypoints_info[:, 6, 2:4] > 0.3).astype(int) + new_keypoints_info = np.insert( + keypoints_info, 17, neck, axis=1) + mmpose_idx = [ + 17, 6, 8, 10, 7, 9, 12, 14, 16, 13, 15, 2, 1, 4, 3 + ] + openpose_idx = [ + 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17 + ] + new_keypoints_info[:, openpose_idx] = \ + new_keypoints_info[:, mmpose_idx] + keypoints_info = new_keypoints_info + + keypoints, scores = keypoints_info[ + ..., :2], keypoints_info[..., 2] + + return keypoints, scores + + diff --git a/animation/UniAnimate/environment.yaml b/animation/UniAnimate/environment.yaml new file mode 100644 index 0000000..7f5569d --- /dev/null +++ b/animation/UniAnimate/environment.yaml @@ -0,0 +1,236 @@ +name: /mnt/user/miniconda3/envs/dtrans +channels: + - http://mirrors.aliyun.com/anaconda/pkgs/main + - defaults +dependencies: + - _libgcc_mutex=0.1=main + - _openmp_mutex=5.1=1_gnu + - ca-certificates=2023.12.12=h06a4308_0 + - ld_impl_linux-64=2.38=h1181459_1 + - libffi=3.4.4=h6a678d5_0 + - libgcc-ng=11.2.0=h1234567_1 + - libgomp=11.2.0=h1234567_1 + - libstdcxx-ng=11.2.0=h1234567_1 + - ncurses=6.4=h6a678d5_0 + - openssl=3.0.12=h7f8727e_0 + - pip=23.3.1=py39h06a4308_0 + - python=3.9.18=h955ad1f_0 + - readline=8.2=h5eee18b_0 + - setuptools=68.2.2=py39h06a4308_0 + - sqlite=3.41.2=h5eee18b_0 + - tk=8.6.12=h1ccaba5_0 + - wheel=0.41.2=py39h06a4308_0 + - xz=5.4.5=h5eee18b_0 + - zlib=1.2.13=h5eee18b_0 + - pip: + - aiofiles==23.2.1 + - aiohttp==3.9.1 + - aiosignal==1.3.1 + - aliyun-python-sdk-core==2.14.0 + - aliyun-python-sdk-kms==2.16.2 + - altair==5.2.0 + - annotated-types==0.6.0 + - antlr4-python3-runtime==4.9.3 + - anyio==4.2.0 + - argparse==1.4.0 + - asttokens==2.4.1 + - async-timeout==4.0.3 + - attrs==23.2.0 + - automat==22.10.0 + - beartype==0.16.4 + - blessed==1.20.0 + - buildtools==1.0.6 + - causal-conv1d==1.1.3.post1 + - certifi==2023.11.17 + - cffi==1.16.0 + - chardet==5.2.0 + - charset-normalizer==3.3.2 + - clean-fid==0.1.35 + - click==8.1.7 + - clip==1.0 + - cmake==3.28.1 + - colorama==0.4.6 + - coloredlogs==15.0.1 + - constantly==23.10.4 + - contourpy==1.2.0 + - crcmod==1.7 + - cryptography==41.0.7 + - cycler==0.12.1 + - decorator==5.1.1 + - decord==0.6.0 + - diffusers==0.26.3 + - docopt==0.6.2 + - easydict==1.11 + - einops==0.7.0 + - exceptiongroup==1.2.0 + - executing==2.0.1 + - fairscale==0.4.13 + - fastapi==0.109.0 + - ffmpeg==1.4 + - ffmpy==0.3.1 + - filelock==3.13.1 + - flatbuffers==24.3.25 + - fonttools==4.47.2 + - frozenlist==1.4.1 + - fsspec==2023.12.2 + - ftfy==6.1.3 + - furl==2.1.3 + - gpustat==1.1.1 + - gradio==4.14.0 + - gradio-client==0.8.0 + - greenlet==3.0.3 + - h11==0.14.0 + - httpcore==1.0.2 + - httpx==0.26.0 + - huggingface-hub==0.20.2 + - humanfriendly==10.0 + - hyperlink==21.0.0 + - idna==3.6 + - imageio==2.33.1 + - imageio-ffmpeg==0.4.9 + - importlib-metadata==7.0.1 + - importlib-resources==6.1.1 + - incremental==22.10.0 + - ipdb==0.13.13 + - ipython==8.18.1 + - jedi==0.19.1 + - jinja2==3.1.3 + - jmespath==0.10.0 + - joblib==1.3.2 + - jsonschema==4.21.0 + - jsonschema-specifications==2023.12.1 + - kiwisolver==1.4.5 + - kornia==0.7.1 + - lazy-loader==0.3 + - lightning-utilities==0.10.0 + - lit==17.0.6 + - lpips==0.1.4 + - mamba-ssm==1.1.4 + - markdown-it-py==3.0.0 + - markupsafe==2.1.3 + - matplotlib==3.8.2 + - matplotlib-inline==0.1.6 + - mdurl==0.1.2 + - motion-vector-extractor==1.0.6 + - mpmath==1.3.0 + - multidict==6.0.4 + - mypy-extensions==1.0.0 + - networkx==3.2.1 + - ninja==1.11.1.1 + - numpy==1.26.3 + - nvidia-cublas-cu11==11.10.3.66 + - nvidia-cublas-cu12==12.1.3.1 + - nvidia-cuda-cupti-cu11==11.7.101 + - nvidia-cuda-cupti-cu12==12.1.105 + - nvidia-cuda-nvrtc-cu11==11.7.99 + - nvidia-cuda-nvrtc-cu12==12.1.105 + - nvidia-cuda-runtime-cu11==11.7.99 + - nvidia-cuda-runtime-cu12==12.1.105 + - nvidia-cudnn-cu11==8.5.0.96 + - nvidia-cudnn-cu12==8.9.2.26 + - nvidia-cufft-cu11==10.9.0.58 + - nvidia-cufft-cu12==11.0.2.54 + - nvidia-curand-cu11==10.2.10.91 + - nvidia-curand-cu12==10.3.2.106 + - nvidia-cusolver-cu11==11.4.0.1 + - nvidia-cusolver-cu12==11.4.5.107 + - nvidia-cusparse-cu11==11.7.4.91 + - nvidia-cusparse-cu12==12.1.0.106 + - nvidia-ml-py==12.535.133 + - nvidia-nccl-cu11==2.14.3 + - nvidia-nccl-cu12==2.19.3 + - nvidia-nvjitlink-cu12==12.3.101 + - nvidia-nvtx-cu11==11.7.91 + - nvidia-nvtx-cu12==12.1.105 + - omegaconf==2.3.0 + - onnxruntime==1.18.0 + - open-clip-torch==2.24.0 + - opencv-python==4.5.3.56 + - opencv-python-headless==4.9.0.80 + - orderedmultidict==1.0.1 + - orjson==3.9.10 + - oss2==2.18.4 + - packaging==23.2 + - pandas==2.1.4 + - parso==0.8.3 + - pexpect==4.9.0 + - pillow==10.2.0 + - piq==0.8.0 + - pkgconfig==1.5.5 + - prompt-toolkit==3.0.43 + - protobuf==4.25.2 + - psutil==5.9.8 + - ptflops==0.7.2.2 + - ptyprocess==0.7.0 + - pure-eval==0.2.2 + - pycparser==2.21 + - pycryptodome==3.20.0 + - pydantic==2.5.3 + - pydantic-core==2.14.6 + - pydub==0.25.1 + - pygments==2.17.2 + - pynvml==11.5.0 + - pyparsing==3.1.1 + - pyre-extensions==0.0.29 + - python-dateutil==2.8.2 + - python-multipart==0.0.6 + - pytorch-lightning==2.1.3 + - pytz==2023.3.post1 + - pyyaml==6.0.1 + - redo==2.0.4 + - referencing==0.32.1 + - regex==2023.12.25 + - requests==2.31.0 + - rich==13.7.0 + - rotary-embedding-torch==0.5.3 + - rpds-py==0.17.1 + - ruff==0.2.0 + - safetensors==0.4.1 + - scikit-image==0.22.0 + - scikit-learn==1.4.0 + - scipy==1.11.4 + - semantic-version==2.10.0 + - sentencepiece==0.1.99 + - shellingham==1.5.4 + - simplejson==3.19.2 + - six==1.16.0 + - sk-video==1.1.10 + - sniffio==1.3.0 + - sqlalchemy==2.0.27 + - stack-data==0.6.3 + - starlette==0.35.1 + - sympy==1.12 + - thop==0.1.1-2209072238 + - threadpoolctl==3.2.0 + - tifffile==2023.12.9 + - timm==0.9.12 + - tokenizers==0.15.0 + - tomli==2.0.1 + - tomlkit==0.12.0 + - toolz==0.12.0 + - torch==2.0.1+cu118 + - torchaudio==2.0.2+cu118 + - torchdiffeq==0.2.3 + - torchmetrics==1.3.0.post0 + - torchsde==0.2.6 + - torchvision==0.15.2+cu118 + - tqdm==4.66.1 + - traitlets==5.14.1 + - trampoline==0.1.2 + - transformers==4.36.2 + - triton==2.0.0 + - twisted==23.10.0 + - typer==0.9.0 + - typing-extensions==4.9.0 + - typing-inspect==0.9.0 + - tzdata==2023.4 + - urllib3==2.1.0 + - uvicorn==0.26.0 + - wcwidth==0.2.13 + - websockets==11.0.3 + - xformers==0.0.20 + - yarl==1.9.4 + - zipp==3.17.0 + - zope-interface==6.2 + - onnxruntime-gpu==1.13.1 +prefix: /mnt/user/miniconda3/envs/dtrans diff --git a/animation/UniAnimate/inference.py b/animation/UniAnimate/inference.py new file mode 100644 index 0000000..05defb2 --- /dev/null +++ b/animation/UniAnimate/inference.py @@ -0,0 +1,18 @@ +import os +import sys +import copy +import json +import math +import random +import logging +import itertools +import numpy as np + +from utils.config import Config +from utils.registry_class import INFER_ENGINE + +from tools import * + +if __name__ == '__main__': + cfg_update = Config(load=True) + INFER_ENGINE.build(dict(type=cfg_update.TASK_TYPE), cfg_update=cfg_update.cfg_dict) diff --git a/animation/UniAnimate/requirements.txt b/animation/UniAnimate/requirements.txt new file mode 100644 index 0000000..25db1db --- /dev/null +++ b/animation/UniAnimate/requirements.txt @@ -0,0 +1,208 @@ +aiofiles==23.2.1 +aiohttp==3.9.1 +aiosignal==1.3.1 +aliyun-python-sdk-core==2.14.0 +aliyun-python-sdk-kms==2.16.2 +altair==5.2.0 +annotated-types==0.6.0 +antlr4-python3-runtime==4.9.3 +anyio==4.2.0 +asttokens==2.4.1 +async-timeout==4.0.3 +attrs==23.2.0 +Automat==22.10.0 +beartype==0.16.4 +blessed==1.20.0 +buildtools==1.0.6 +# causal-conv1d==1.1.3.post1 +certifi==2023.11.17 +cffi==1.16.0 +chardet==5.2.0 +charset-normalizer==3.3.2 +clean-fid==0.1.35 +click==8.1.7 +# clip==1.0 +cmake==3.28.1 +colorama==0.4.6 +coloredlogs==15.0.1 +constantly==23.10.4 +contourpy==1.2.0 +crcmod==1.7 +cryptography==41.0.7 +cycler==0.12.1 +decorator==5.1.1 +decord==0.6.0 +diffusers==0.26.3 +docopt==0.6.2 +easydict==1.11 +einops==0.7.0 +exceptiongroup==1.2.0 +executing==2.0.1 +fairscale==0.4.13 +fastapi==0.109.0 +ffmpeg==1.4 +ffmpy==0.3.1 +filelock==3.13.1 +flatbuffers==24.3.25 +fonttools==4.47.2 +frozenlist==1.4.1 +fsspec==2023.12.2 +ftfy==6.1.3 +furl==2.1.3 +gpustat==1.1.1 +gradio==4.14.0 +gradio_client==0.8.0 +greenlet==3.0.3 +h11==0.14.0 +httpcore==1.0.2 +httpx==0.26.0 +huggingface-hub==0.20.2 +humanfriendly==10.0 +hyperlink==21.0.0 +idna==3.6 +imageio==2.33.1 +imageio-ffmpeg==0.4.9 +importlib-metadata==7.0.1 +importlib-resources==6.1.1 +incremental==22.10.0 +ipdb==0.13.13 +ipython==8.18.1 +jedi==0.19.1 +Jinja2==3.1.3 +jmespath==0.10.0 +joblib==1.3.2 +jsonschema==4.21.0 +jsonschema-specifications==2023.12.1 +kiwisolver==1.4.5 +kornia==0.7.1 +lazy_loader==0.3 +lightning-utilities==0.10.0 +lit==17.0.6 +lpips==0.1.4 +markdown-it-py==3.0.0 +MarkupSafe==2.1.3 +matplotlib==3.8.2 +matplotlib-inline==0.1.6 +mdurl==0.1.2 +# motion-vector-extractor==1.0.6 +mpmath==1.3.0 +multidict==6.0.4 +mypy-extensions==1.0.0 +networkx==3.2.1 +ninja==1.11.1.1 +numpy==1.26.3 +nvidia-cublas-cu11==11.10.3.66 +nvidia-cublas-cu12==12.1.3.1 +nvidia-cuda-cupti-cu11==11.7.101 +nvidia-cuda-cupti-cu12==12.1.105 +nvidia-cuda-nvrtc-cu11==11.7.99 +nvidia-cuda-nvrtc-cu12==12.1.105 +nvidia-cuda-runtime-cu11==11.7.99 +nvidia-cuda-runtime-cu12==12.1.105 +nvidia-cudnn-cu11==8.5.0.96 +nvidia-cudnn-cu12==8.9.2.26 +nvidia-cufft-cu11==10.9.0.58 +nvidia-cufft-cu12==11.0.2.54 +nvidia-curand-cu11==10.2.10.91 +nvidia-curand-cu12==10.3.2.106 +nvidia-cusolver-cu11==11.4.0.1 +nvidia-cusolver-cu12==11.4.5.107 +nvidia-cusparse-cu11==11.7.4.91 +nvidia-cusparse-cu12==12.1.0.106 +nvidia-ml-py==12.535.133 +nvidia-nccl-cu11==2.14.3 +nvidia-nccl-cu12==2.19.3 +nvidia-nvjitlink-cu12==12.3.101 +nvidia-nvtx-cu11==11.7.91 +nvidia-nvtx-cu12==12.1.105 +omegaconf==2.3.0 +onnxruntime==1.18.0 +open-clip-torch==2.24.0 +opencv-python==4.5.3.56 +opencv-python-headless==4.9.0.80 +orderedmultidict==1.0.1 +orjson==3.9.10 +oss2==2.18.4 +# packaging==23.2 +pandas==2.1.4 +parso==0.8.3 +pexpect==4.9.0 +pillow==10.2.0 +piq==0.8.0 +pkgconfig==1.5.5 +prompt-toolkit==3.0.43 +protobuf==4.25.2 +psutil==5.9.8 +ptflops==0.7.2.2 +ptyprocess==0.7.0 +pure-eval==0.2.2 +pycparser==2.21 +pycryptodome==3.20.0 +pydantic==2.5.3 +pydantic_core==2.14.6 +pydub==0.25.1 +Pygments==2.17.2 +pynvml==11.5.0 +pyparsing==3.1.1 +pyre-extensions==0.0.29 +python-dateutil==2.8.2 +python-multipart==0.0.6 +pytorch-lightning==2.1.3 +pytz==2023.3.post1 +PyYAML==6.0.1 +redo==2.0.4 +referencing==0.32.1 +regex==2023.12.25 +requests==2.31.0 +rich==13.7.0 +rotary-embedding-torch==0.5.3 +rpds-py==0.17.1 +ruff==0.2.0 +safetensors==0.4.1 +scikit-image==0.22.0 +scikit-learn==1.4.0 +scipy==1.11.4 +semantic-version==2.10.0 +sentencepiece==0.1.99 +shellingham==1.5.4 +simplejson==3.19.2 +six==1.16.0 +sk-video==1.1.10 +sniffio==1.3.0 +SQLAlchemy==2.0.27 +stack-data==0.6.3 +starlette==0.35.1 +sympy==1.12 +thop==0.1.1.post2209072238 +threadpoolctl==3.2.0 +tifffile==2023.12.9 +timm==0.9.12 +tokenizers==0.15.0 +tomli==2.0.1 +tomlkit==0.12.0 +toolz==0.12.0 +# torch==2.0.1+cu118 +# torchaudio==2.0.2+cu118 +torchdiffeq==0.2.3 +torchmetrics==1.3.0.post0 +torchsde==0.2.6 +# torchvision==0.15.2+cu118 +tqdm==4.66.1 +traitlets==5.14.1 +trampoline==0.1.2 +transformers==4.36.2 +triton==2.0.0 +Twisted==23.10.0 +typer==0.9.0 +typing-inspect==0.9.0 +typing_extensions==4.9.0 +tzdata==2023.4 +urllib3==2.1.0 +uvicorn==0.26.0 +wcwidth==0.2.13 +websockets==11.0.3 +xformers==0.0.20 +yarl==1.9.4 +zipp==3.17.0 +zope.interface==6.2 +onnxruntime-gpu==1.13.1 diff --git a/animation/UniAnimate/run_align_pose.py b/animation/UniAnimate/run_align_pose.py new file mode 100644 index 0000000..a7d641d --- /dev/null +++ b/animation/UniAnimate/run_align_pose.py @@ -0,0 +1,712 @@ +# Openpose +# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose +# 2nd Edited by https://github.com/Hzzone/pytorch-openpose +# 3rd Edited by ControlNet +# 4th Edited by ControlNet (added face and correct hands) + +import os +os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" +import cv2 +import torch +import numpy as np +import json +import copy +import torch +import random +import argparse +import shutil +import tempfile +import subprocess +import numpy as np +import math + +import torch.multiprocessing as mp +import torch.distributed as dist +import pickle +import logging +from io import BytesIO +import oss2 as oss +import os.path as osp + +import sys +import dwpose.util as util +from dwpose.wholebody import Wholebody + + +def smoothing_factor(t_e, cutoff): + r = 2 * math.pi * cutoff * t_e + return r / (r + 1) + + +def exponential_smoothing(a, x, x_prev): + return a * x + (1 - a) * x_prev + + +class OneEuroFilter: + def __init__(self, t0, x0, dx0=0.0, min_cutoff=1.0, beta=0.0, + d_cutoff=1.0): + """Initialize the one euro filter.""" + # The parameters. + self.min_cutoff = float(min_cutoff) + self.beta = float(beta) + self.d_cutoff = float(d_cutoff) + # Previous values. + self.x_prev = x0 + self.dx_prev = float(dx0) + self.t_prev = float(t0) + + def __call__(self, t, x): + """Compute the filtered signal.""" + t_e = t - self.t_prev + + # The filtered derivative of the signal. + a_d = smoothing_factor(t_e, self.d_cutoff) + dx = (x - self.x_prev) / t_e + dx_hat = exponential_smoothing(a_d, dx, self.dx_prev) + + # The filtered signal. + cutoff = self.min_cutoff + self.beta * abs(dx_hat) + a = smoothing_factor(t_e, cutoff) + x_hat = exponential_smoothing(a, x, self.x_prev) + + # Memorize the previous values. + self.x_prev = x_hat + self.dx_prev = dx_hat + self.t_prev = t + + return x_hat + + +def get_logger(name="essmc2"): + logger = logging.getLogger(name) + logger.propagate = False + if len(logger.handlers) == 0: + std_handler = logging.StreamHandler(sys.stdout) + formatter = logging.Formatter( + '%(asctime)s - %(name)s - %(levelname)s - %(message)s') + std_handler.setFormatter(formatter) + std_handler.setLevel(logging.INFO) + logger.setLevel(logging.INFO) + logger.addHandler(std_handler) + return logger + +class DWposeDetector: + def __init__(self): + + self.pose_estimation = Wholebody() + + def __call__(self, oriImg): + oriImg = oriImg.copy() + H, W, C = oriImg.shape + with torch.no_grad(): + candidate, subset = self.pose_estimation(oriImg) + candidate = candidate[0][np.newaxis, :, :] + subset = subset[0][np.newaxis, :] + nums, keys, locs = candidate.shape + candidate[..., 0] /= float(W) + candidate[..., 1] /= float(H) + body = candidate[:,:18].copy() + body = body.reshape(nums*18, locs) + score = subset[:,:18].copy() + + for i in range(len(score)): + for j in range(len(score[i])): + if score[i][j] > 0.3: + score[i][j] = int(18*i+j) + else: + score[i][j] = -1 + + un_visible = subset<0.3 + candidate[un_visible] = -1 + + bodyfoot_score = subset[:,:24].copy() + for i in range(len(bodyfoot_score)): + for j in range(len(bodyfoot_score[i])): + if bodyfoot_score[i][j] > 0.3: + bodyfoot_score[i][j] = int(18*i+j) + else: + bodyfoot_score[i][j] = -1 + if -1 not in bodyfoot_score[:,18] and -1 not in bodyfoot_score[:,19]: + bodyfoot_score[:,18] = np.array([18.]) + else: + bodyfoot_score[:,18] = np.array([-1.]) + if -1 not in bodyfoot_score[:,21] and -1 not in bodyfoot_score[:,22]: + bodyfoot_score[:,19] = np.array([19.]) + else: + bodyfoot_score[:,19] = np.array([-1.]) + bodyfoot_score = bodyfoot_score[:, :20] + + bodyfoot = candidate[:,:24].copy() + + for i in range(nums): + if -1 not in bodyfoot[i][18] and -1 not in bodyfoot[i][19]: + bodyfoot[i][18] = (bodyfoot[i][18]+bodyfoot[i][19])/2 + else: + bodyfoot[i][18] = np.array([-1., -1.]) + if -1 not in bodyfoot[i][21] and -1 not in bodyfoot[i][22]: + bodyfoot[i][19] = (bodyfoot[i][21]+bodyfoot[i][22])/2 + else: + bodyfoot[i][19] = np.array([-1., -1.]) + + bodyfoot = bodyfoot[:,:20,:] + bodyfoot = bodyfoot.reshape(nums*20, locs) + + foot = candidate[:,18:24] + + faces = candidate[:,24:92] + + hands = candidate[:,92:113] + hands = np.vstack([hands, candidate[:,113:]]) + + # bodies = dict(candidate=body, subset=score) + bodies = dict(candidate=bodyfoot, subset=bodyfoot_score) + pose = dict(bodies=bodies, hands=hands, faces=faces) + + # return draw_pose(pose, H, W) + return pose + +def draw_pose(pose, H, W): + bodies = pose['bodies'] + faces = pose['faces'] + hands = pose['hands'] + candidate = bodies['candidate'] + subset = bodies['subset'] + canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8) + + canvas = util.draw_body_and_foot(canvas, candidate, subset) + + canvas = util.draw_handpose(canvas, hands) + + canvas_without_face = copy.deepcopy(canvas) + + canvas = util.draw_facepose(canvas, faces) + + return canvas_without_face, canvas + +def dw_func(_id, frame, dwpose_model, dwpose_woface_folder='tmp_dwpose_wo_face', dwpose_withface_folder='tmp_dwpose_with_face'): + + # frame = cv2.imread(frame_name, cv2.IMREAD_COLOR) + pose = dwpose_model(frame) + + return pose + + +def mp_main(args): + + if args.source_video_paths.endswith('mp4'): + video_paths = [args.source_video_paths] + else: + # video list + video_paths = [os.path.join(args.source_video_paths, frame_name) for frame_name in os.listdir(args.source_video_paths)] + + + logger.info("There are {} videos for extracting poses".format(len(video_paths))) + + logger.info('LOAD: DW Pose Model') + dwpose_model = DWposeDetector() + + results_vis = [] + for i, file_path in enumerate(video_paths): + logger.info(f"{i}/{len(video_paths)}, {file_path}") + videoCapture = cv2.VideoCapture(file_path) + while videoCapture.isOpened(): + # get a frame + ret, frame = videoCapture.read() + if ret: + pose = dw_func(i, frame, dwpose_model) + results_vis.append(pose) + else: + break + logger.info(f'all frames in {file_path} have been read.') + videoCapture.release() + + # added + # results_vis = results_vis[8:] + print(len(results_vis)) + + ref_name = args.ref_name + save_motion = args.saved_pose_dir + os.system(f'rm -rf {save_motion}'); + os.makedirs(save_motion, exist_ok=True) + save_warp = args.saved_pose_dir + # os.makedirs(save_warp, exist_ok=True) + + ref_frame = cv2.imread(ref_name, cv2.IMREAD_COLOR) + pose_ref = dw_func(i, ref_frame, dwpose_model) + + bodies = results_vis[0]['bodies'] + faces = results_vis[0]['faces'] + hands = results_vis[0]['hands'] + candidate = bodies['candidate'] + + ref_bodies = pose_ref['bodies'] + ref_faces = pose_ref['faces'] + ref_hands = pose_ref['hands'] + ref_candidate = ref_bodies['candidate'] + + + ref_2_x = ref_candidate[2][0] + ref_2_y = ref_candidate[2][1] + ref_5_x = ref_candidate[5][0] + ref_5_y = ref_candidate[5][1] + ref_8_x = ref_candidate[8][0] + ref_8_y = ref_candidate[8][1] + ref_11_x = ref_candidate[11][0] + ref_11_y = ref_candidate[11][1] + ref_center1 = 0.5*(ref_candidate[2]+ref_candidate[5]) + ref_center2 = 0.5*(ref_candidate[8]+ref_candidate[11]) + + zero_2_x = candidate[2][0] + zero_2_y = candidate[2][1] + zero_5_x = candidate[5][0] + zero_5_y = candidate[5][1] + zero_8_x = candidate[8][0] + zero_8_y = candidate[8][1] + zero_11_x = candidate[11][0] + zero_11_y = candidate[11][1] + zero_center1 = 0.5*(candidate[2]+candidate[5]) + zero_center2 = 0.5*(candidate[8]+candidate[11]) + + x_ratio = (ref_5_x-ref_2_x)/(zero_5_x-zero_2_x) + y_ratio = (ref_center2[1]-ref_center1[1])/(zero_center2[1]-zero_center1[1]) + + results_vis[0]['bodies']['candidate'][:,0] *= x_ratio + results_vis[0]['bodies']['candidate'][:,1] *= y_ratio + results_vis[0]['faces'][:,:,0] *= x_ratio + results_vis[0]['faces'][:,:,1] *= y_ratio + results_vis[0]['hands'][:,:,0] *= x_ratio + results_vis[0]['hands'][:,:,1] *= y_ratio + + ########neck######## + l_neck_ref = ((ref_candidate[0][0] - ref_candidate[1][0]) ** 2 + (ref_candidate[0][1] - ref_candidate[1][1]) ** 2) ** 0.5 + l_neck_0 = ((candidate[0][0] - candidate[1][0]) ** 2 + (candidate[0][1] - candidate[1][1]) ** 2) ** 0.5 + neck_ratio = l_neck_ref / l_neck_0 + + x_offset_neck = (candidate[1][0]-candidate[0][0])*(1.-neck_ratio) + y_offset_neck = (candidate[1][1]-candidate[0][1])*(1.-neck_ratio) + + results_vis[0]['bodies']['candidate'][0,0] += x_offset_neck + results_vis[0]['bodies']['candidate'][0,1] += y_offset_neck + results_vis[0]['bodies']['candidate'][14,0] += x_offset_neck + results_vis[0]['bodies']['candidate'][14,1] += y_offset_neck + results_vis[0]['bodies']['candidate'][15,0] += x_offset_neck + results_vis[0]['bodies']['candidate'][15,1] += y_offset_neck + results_vis[0]['bodies']['candidate'][16,0] += x_offset_neck + results_vis[0]['bodies']['candidate'][16,1] += y_offset_neck + results_vis[0]['bodies']['candidate'][17,0] += x_offset_neck + results_vis[0]['bodies']['candidate'][17,1] += y_offset_neck + + ########shoulder2######## + l_shoulder2_ref = ((ref_candidate[2][0] - ref_candidate[1][0]) ** 2 + (ref_candidate[2][1] - ref_candidate[1][1]) ** 2) ** 0.5 + l_shoulder2_0 = ((candidate[2][0] - candidate[1][0]) ** 2 + (candidate[2][1] - candidate[1][1]) ** 2) ** 0.5 + + shoulder2_ratio = l_shoulder2_ref / l_shoulder2_0 + + x_offset_shoulder2 = (candidate[1][0]-candidate[2][0])*(1.-shoulder2_ratio) + y_offset_shoulder2 = (candidate[1][1]-candidate[2][1])*(1.-shoulder2_ratio) + + results_vis[0]['bodies']['candidate'][2,0] += x_offset_shoulder2 + results_vis[0]['bodies']['candidate'][2,1] += y_offset_shoulder2 + results_vis[0]['bodies']['candidate'][3,0] += x_offset_shoulder2 + results_vis[0]['bodies']['candidate'][3,1] += y_offset_shoulder2 + results_vis[0]['bodies']['candidate'][4,0] += x_offset_shoulder2 + results_vis[0]['bodies']['candidate'][4,1] += y_offset_shoulder2 + results_vis[0]['hands'][1,:,0] += x_offset_shoulder2 + results_vis[0]['hands'][1,:,1] += y_offset_shoulder2 + + ########shoulder5######## + l_shoulder5_ref = ((ref_candidate[5][0] - ref_candidate[1][0]) ** 2 + (ref_candidate[5][1] - ref_candidate[1][1]) ** 2) ** 0.5 + l_shoulder5_0 = ((candidate[5][0] - candidate[1][0]) ** 2 + (candidate[5][1] - candidate[1][1]) ** 2) ** 0.5 + + shoulder5_ratio = l_shoulder5_ref / l_shoulder5_0 + + x_offset_shoulder5 = (candidate[1][0]-candidate[5][0])*(1.-shoulder5_ratio) + y_offset_shoulder5 = (candidate[1][1]-candidate[5][1])*(1.-shoulder5_ratio) + + results_vis[0]['bodies']['candidate'][5,0] += x_offset_shoulder5 + results_vis[0]['bodies']['candidate'][5,1] += y_offset_shoulder5 + results_vis[0]['bodies']['candidate'][6,0] += x_offset_shoulder5 + results_vis[0]['bodies']['candidate'][6,1] += y_offset_shoulder5 + results_vis[0]['bodies']['candidate'][7,0] += x_offset_shoulder5 + results_vis[0]['bodies']['candidate'][7,1] += y_offset_shoulder5 + results_vis[0]['hands'][0,:,0] += x_offset_shoulder5 + results_vis[0]['hands'][0,:,1] += y_offset_shoulder5 + + ########arm3######## + l_arm3_ref = ((ref_candidate[3][0] - ref_candidate[2][0]) ** 2 + (ref_candidate[3][1] - ref_candidate[2][1]) ** 2) ** 0.5 + l_arm3_0 = ((candidate[3][0] - candidate[2][0]) ** 2 + (candidate[3][1] - candidate[2][1]) ** 2) ** 0.5 + + arm3_ratio = l_arm3_ref / l_arm3_0 + + x_offset_arm3 = (candidate[2][0]-candidate[3][0])*(1.-arm3_ratio) + y_offset_arm3 = (candidate[2][1]-candidate[3][1])*(1.-arm3_ratio) + + results_vis[0]['bodies']['candidate'][3,0] += x_offset_arm3 + results_vis[0]['bodies']['candidate'][3,1] += y_offset_arm3 + results_vis[0]['bodies']['candidate'][4,0] += x_offset_arm3 + results_vis[0]['bodies']['candidate'][4,1] += y_offset_arm3 + results_vis[0]['hands'][1,:,0] += x_offset_arm3 + results_vis[0]['hands'][1,:,1] += y_offset_arm3 + + ########arm4######## + l_arm4_ref = ((ref_candidate[4][0] - ref_candidate[3][0]) ** 2 + (ref_candidate[4][1] - ref_candidate[3][1]) ** 2) ** 0.5 + l_arm4_0 = ((candidate[4][0] - candidate[3][0]) ** 2 + (candidate[4][1] - candidate[3][1]) ** 2) ** 0.5 + + arm4_ratio = l_arm4_ref / l_arm4_0 + + x_offset_arm4 = (candidate[3][0]-candidate[4][0])*(1.-arm4_ratio) + y_offset_arm4 = (candidate[3][1]-candidate[4][1])*(1.-arm4_ratio) + + results_vis[0]['bodies']['candidate'][4,0] += x_offset_arm4 + results_vis[0]['bodies']['candidate'][4,1] += y_offset_arm4 + results_vis[0]['hands'][1,:,0] += x_offset_arm4 + results_vis[0]['hands'][1,:,1] += y_offset_arm4 + + ########arm6######## + l_arm6_ref = ((ref_candidate[6][0] - ref_candidate[5][0]) ** 2 + (ref_candidate[6][1] - ref_candidate[5][1]) ** 2) ** 0.5 + l_arm6_0 = ((candidate[6][0] - candidate[5][0]) ** 2 + (candidate[6][1] - candidate[5][1]) ** 2) ** 0.5 + + arm6_ratio = l_arm6_ref / l_arm6_0 + + x_offset_arm6 = (candidate[5][0]-candidate[6][0])*(1.-arm6_ratio) + y_offset_arm6 = (candidate[5][1]-candidate[6][1])*(1.-arm6_ratio) + + results_vis[0]['bodies']['candidate'][6,0] += x_offset_arm6 + results_vis[0]['bodies']['candidate'][6,1] += y_offset_arm6 + results_vis[0]['bodies']['candidate'][7,0] += x_offset_arm6 + results_vis[0]['bodies']['candidate'][7,1] += y_offset_arm6 + results_vis[0]['hands'][0,:,0] += x_offset_arm6 + results_vis[0]['hands'][0,:,1] += y_offset_arm6 + + ########arm7######## + l_arm7_ref = ((ref_candidate[7][0] - ref_candidate[6][0]) ** 2 + (ref_candidate[7][1] - ref_candidate[6][1]) ** 2) ** 0.5 + l_arm7_0 = ((candidate[7][0] - candidate[6][0]) ** 2 + (candidate[7][1] - candidate[6][1]) ** 2) ** 0.5 + + arm7_ratio = l_arm7_ref / l_arm7_0 + + x_offset_arm7 = (candidate[6][0]-candidate[7][0])*(1.-arm7_ratio) + y_offset_arm7 = (candidate[6][1]-candidate[7][1])*(1.-arm7_ratio) + + results_vis[0]['bodies']['candidate'][7,0] += x_offset_arm7 + results_vis[0]['bodies']['candidate'][7,1] += y_offset_arm7 + results_vis[0]['hands'][0,:,0] += x_offset_arm7 + results_vis[0]['hands'][0,:,1] += y_offset_arm7 + + ########head14######## + l_head14_ref = ((ref_candidate[14][0] - ref_candidate[0][0]) ** 2 + (ref_candidate[14][1] - ref_candidate[0][1]) ** 2) ** 0.5 + l_head14_0 = ((candidate[14][0] - candidate[0][0]) ** 2 + (candidate[14][1] - candidate[0][1]) ** 2) ** 0.5 + + head14_ratio = l_head14_ref / l_head14_0 + + x_offset_head14 = (candidate[0][0]-candidate[14][0])*(1.-head14_ratio) + y_offset_head14 = (candidate[0][1]-candidate[14][1])*(1.-head14_ratio) + + results_vis[0]['bodies']['candidate'][14,0] += x_offset_head14 + results_vis[0]['bodies']['candidate'][14,1] += y_offset_head14 + results_vis[0]['bodies']['candidate'][16,0] += x_offset_head14 + results_vis[0]['bodies']['candidate'][16,1] += y_offset_head14 + + ########head15######## + l_head15_ref = ((ref_candidate[15][0] - ref_candidate[0][0]) ** 2 + (ref_candidate[15][1] - ref_candidate[0][1]) ** 2) ** 0.5 + l_head15_0 = ((candidate[15][0] - candidate[0][0]) ** 2 + (candidate[15][1] - candidate[0][1]) ** 2) ** 0.5 + + head15_ratio = l_head15_ref / l_head15_0 + + x_offset_head15 = (candidate[0][0]-candidate[15][0])*(1.-head15_ratio) + y_offset_head15 = (candidate[0][1]-candidate[15][1])*(1.-head15_ratio) + + results_vis[0]['bodies']['candidate'][15,0] += x_offset_head15 + results_vis[0]['bodies']['candidate'][15,1] += y_offset_head15 + results_vis[0]['bodies']['candidate'][17,0] += x_offset_head15 + results_vis[0]['bodies']['candidate'][17,1] += y_offset_head15 + + ########head16######## + l_head16_ref = ((ref_candidate[16][0] - ref_candidate[14][0]) ** 2 + (ref_candidate[16][1] - ref_candidate[14][1]) ** 2) ** 0.5 + l_head16_0 = ((candidate[16][0] - candidate[14][0]) ** 2 + (candidate[16][1] - candidate[14][1]) ** 2) ** 0.5 + + head16_ratio = l_head16_ref / l_head16_0 + + x_offset_head16 = (candidate[14][0]-candidate[16][0])*(1.-head16_ratio) + y_offset_head16 = (candidate[14][1]-candidate[16][1])*(1.-head16_ratio) + + results_vis[0]['bodies']['candidate'][16,0] += x_offset_head16 + results_vis[0]['bodies']['candidate'][16,1] += y_offset_head16 + + ########head17######## + l_head17_ref = ((ref_candidate[17][0] - ref_candidate[15][0]) ** 2 + (ref_candidate[17][1] - ref_candidate[15][1]) ** 2) ** 0.5 + l_head17_0 = ((candidate[17][0] - candidate[15][0]) ** 2 + (candidate[17][1] - candidate[15][1]) ** 2) ** 0.5 + + head17_ratio = l_head17_ref / l_head17_0 + + x_offset_head17 = (candidate[15][0]-candidate[17][0])*(1.-head17_ratio) + y_offset_head17 = (candidate[15][1]-candidate[17][1])*(1.-head17_ratio) + + results_vis[0]['bodies']['candidate'][17,0] += x_offset_head17 + results_vis[0]['bodies']['candidate'][17,1] += y_offset_head17 + + ########MovingAverage######## + + ########left leg######## + l_ll1_ref = ((ref_candidate[8][0] - ref_candidate[9][0]) ** 2 + (ref_candidate[8][1] - ref_candidate[9][1]) ** 2) ** 0.5 + l_ll1_0 = ((candidate[8][0] - candidate[9][0]) ** 2 + (candidate[8][1] - candidate[9][1]) ** 2) ** 0.5 + ll1_ratio = l_ll1_ref / l_ll1_0 + + x_offset_ll1 = (candidate[9][0]-candidate[8][0])*(ll1_ratio-1.) + y_offset_ll1 = (candidate[9][1]-candidate[8][1])*(ll1_ratio-1.) + + results_vis[0]['bodies']['candidate'][9,0] += x_offset_ll1 + results_vis[0]['bodies']['candidate'][9,1] += y_offset_ll1 + results_vis[0]['bodies']['candidate'][10,0] += x_offset_ll1 + results_vis[0]['bodies']['candidate'][10,1] += y_offset_ll1 + results_vis[0]['bodies']['candidate'][19,0] += x_offset_ll1 + results_vis[0]['bodies']['candidate'][19,1] += y_offset_ll1 + + l_ll2_ref = ((ref_candidate[9][0] - ref_candidate[10][0]) ** 2 + (ref_candidate[9][1] - ref_candidate[10][1]) ** 2) ** 0.5 + l_ll2_0 = ((candidate[9][0] - candidate[10][0]) ** 2 + (candidate[9][1] - candidate[10][1]) ** 2) ** 0.5 + ll2_ratio = l_ll2_ref / l_ll2_0 + + x_offset_ll2 = (candidate[10][0]-candidate[9][0])*(ll2_ratio-1.) + y_offset_ll2 = (candidate[10][1]-candidate[9][1])*(ll2_ratio-1.) + + results_vis[0]['bodies']['candidate'][10,0] += x_offset_ll2 + results_vis[0]['bodies']['candidate'][10,1] += y_offset_ll2 + results_vis[0]['bodies']['candidate'][19,0] += x_offset_ll2 + results_vis[0]['bodies']['candidate'][19,1] += y_offset_ll2 + + ########right leg######## + l_rl1_ref = ((ref_candidate[11][0] - ref_candidate[12][0]) ** 2 + (ref_candidate[11][1] - ref_candidate[12][1]) ** 2) ** 0.5 + l_rl1_0 = ((candidate[11][0] - candidate[12][0]) ** 2 + (candidate[11][1] - candidate[12][1]) ** 2) ** 0.5 + rl1_ratio = l_rl1_ref / l_rl1_0 + + x_offset_rl1 = (candidate[12][0]-candidate[11][0])*(rl1_ratio-1.) + y_offset_rl1 = (candidate[12][1]-candidate[11][1])*(rl1_ratio-1.) + + results_vis[0]['bodies']['candidate'][12,0] += x_offset_rl1 + results_vis[0]['bodies']['candidate'][12,1] += y_offset_rl1 + results_vis[0]['bodies']['candidate'][13,0] += x_offset_rl1 + results_vis[0]['bodies']['candidate'][13,1] += y_offset_rl1 + results_vis[0]['bodies']['candidate'][18,0] += x_offset_rl1 + results_vis[0]['bodies']['candidate'][18,1] += y_offset_rl1 + + l_rl2_ref = ((ref_candidate[12][0] - ref_candidate[13][0]) ** 2 + (ref_candidate[12][1] - ref_candidate[13][1]) ** 2) ** 0.5 + l_rl2_0 = ((candidate[12][0] - candidate[13][0]) ** 2 + (candidate[12][1] - candidate[13][1]) ** 2) ** 0.5 + rl2_ratio = l_rl2_ref / l_rl2_0 + + x_offset_rl2 = (candidate[13][0]-candidate[12][0])*(rl2_ratio-1.) + y_offset_rl2 = (candidate[13][1]-candidate[12][1])*(rl2_ratio-1.) + + results_vis[0]['bodies']['candidate'][13,0] += x_offset_rl2 + results_vis[0]['bodies']['candidate'][13,1] += y_offset_rl2 + results_vis[0]['bodies']['candidate'][18,0] += x_offset_rl2 + results_vis[0]['bodies']['candidate'][18,1] += y_offset_rl2 + + offset = ref_candidate[1] - results_vis[0]['bodies']['candidate'][1] + + results_vis[0]['bodies']['candidate'] += offset[np.newaxis, :] + results_vis[0]['faces'] += offset[np.newaxis, np.newaxis, :] + results_vis[0]['hands'] += offset[np.newaxis, np.newaxis, :] + + for i in range(1, len(results_vis)): + results_vis[i]['bodies']['candidate'][:,0] *= x_ratio + results_vis[i]['bodies']['candidate'][:,1] *= y_ratio + results_vis[i]['faces'][:,:,0] *= x_ratio + results_vis[i]['faces'][:,:,1] *= y_ratio + results_vis[i]['hands'][:,:,0] *= x_ratio + results_vis[i]['hands'][:,:,1] *= y_ratio + + ########neck######## + x_offset_neck = (results_vis[i]['bodies']['candidate'][1][0]-results_vis[i]['bodies']['candidate'][0][0])*(1.-neck_ratio) + y_offset_neck = (results_vis[i]['bodies']['candidate'][1][1]-results_vis[i]['bodies']['candidate'][0][1])*(1.-neck_ratio) + + results_vis[i]['bodies']['candidate'][0,0] += x_offset_neck + results_vis[i]['bodies']['candidate'][0,1] += y_offset_neck + results_vis[i]['bodies']['candidate'][14,0] += x_offset_neck + results_vis[i]['bodies']['candidate'][14,1] += y_offset_neck + results_vis[i]['bodies']['candidate'][15,0] += x_offset_neck + results_vis[i]['bodies']['candidate'][15,1] += y_offset_neck + results_vis[i]['bodies']['candidate'][16,0] += x_offset_neck + results_vis[i]['bodies']['candidate'][16,1] += y_offset_neck + results_vis[i]['bodies']['candidate'][17,0] += x_offset_neck + results_vis[i]['bodies']['candidate'][17,1] += y_offset_neck + + ########shoulder2######## + + + x_offset_shoulder2 = (results_vis[i]['bodies']['candidate'][1][0]-results_vis[i]['bodies']['candidate'][2][0])*(1.-shoulder2_ratio) + y_offset_shoulder2 = (results_vis[i]['bodies']['candidate'][1][1]-results_vis[i]['bodies']['candidate'][2][1])*(1.-shoulder2_ratio) + + results_vis[i]['bodies']['candidate'][2,0] += x_offset_shoulder2 + results_vis[i]['bodies']['candidate'][2,1] += y_offset_shoulder2 + results_vis[i]['bodies']['candidate'][3,0] += x_offset_shoulder2 + results_vis[i]['bodies']['candidate'][3,1] += y_offset_shoulder2 + results_vis[i]['bodies']['candidate'][4,0] += x_offset_shoulder2 + results_vis[i]['bodies']['candidate'][4,1] += y_offset_shoulder2 + results_vis[i]['hands'][1,:,0] += x_offset_shoulder2 + results_vis[i]['hands'][1,:,1] += y_offset_shoulder2 + + ########shoulder5######## + + x_offset_shoulder5 = (results_vis[i]['bodies']['candidate'][1][0]-results_vis[i]['bodies']['candidate'][5][0])*(1.-shoulder5_ratio) + y_offset_shoulder5 = (results_vis[i]['bodies']['candidate'][1][1]-results_vis[i]['bodies']['candidate'][5][1])*(1.-shoulder5_ratio) + + results_vis[i]['bodies']['candidate'][5,0] += x_offset_shoulder5 + results_vis[i]['bodies']['candidate'][5,1] += y_offset_shoulder5 + results_vis[i]['bodies']['candidate'][6,0] += x_offset_shoulder5 + results_vis[i]['bodies']['candidate'][6,1] += y_offset_shoulder5 + results_vis[i]['bodies']['candidate'][7,0] += x_offset_shoulder5 + results_vis[i]['bodies']['candidate'][7,1] += y_offset_shoulder5 + results_vis[i]['hands'][0,:,0] += x_offset_shoulder5 + results_vis[i]['hands'][0,:,1] += y_offset_shoulder5 + + ########arm3######## + + x_offset_arm3 = (results_vis[i]['bodies']['candidate'][2][0]-results_vis[i]['bodies']['candidate'][3][0])*(1.-arm3_ratio) + y_offset_arm3 = (results_vis[i]['bodies']['candidate'][2][1]-results_vis[i]['bodies']['candidate'][3][1])*(1.-arm3_ratio) + + results_vis[i]['bodies']['candidate'][3,0] += x_offset_arm3 + results_vis[i]['bodies']['candidate'][3,1] += y_offset_arm3 + results_vis[i]['bodies']['candidate'][4,0] += x_offset_arm3 + results_vis[i]['bodies']['candidate'][4,1] += y_offset_arm3 + results_vis[i]['hands'][1,:,0] += x_offset_arm3 + results_vis[i]['hands'][1,:,1] += y_offset_arm3 + + ########arm4######## + + x_offset_arm4 = (results_vis[i]['bodies']['candidate'][3][0]-results_vis[i]['bodies']['candidate'][4][0])*(1.-arm4_ratio) + y_offset_arm4 = (results_vis[i]['bodies']['candidate'][3][1]-results_vis[i]['bodies']['candidate'][4][1])*(1.-arm4_ratio) + + results_vis[i]['bodies']['candidate'][4,0] += x_offset_arm4 + results_vis[i]['bodies']['candidate'][4,1] += y_offset_arm4 + results_vis[i]['hands'][1,:,0] += x_offset_arm4 + results_vis[i]['hands'][1,:,1] += y_offset_arm4 + + ########arm6######## + + x_offset_arm6 = (results_vis[i]['bodies']['candidate'][5][0]-results_vis[i]['bodies']['candidate'][6][0])*(1.-arm6_ratio) + y_offset_arm6 = (results_vis[i]['bodies']['candidate'][5][1]-results_vis[i]['bodies']['candidate'][6][1])*(1.-arm6_ratio) + + results_vis[i]['bodies']['candidate'][6,0] += x_offset_arm6 + results_vis[i]['bodies']['candidate'][6,1] += y_offset_arm6 + results_vis[i]['bodies']['candidate'][7,0] += x_offset_arm6 + results_vis[i]['bodies']['candidate'][7,1] += y_offset_arm6 + results_vis[i]['hands'][0,:,0] += x_offset_arm6 + results_vis[i]['hands'][0,:,1] += y_offset_arm6 + + ########arm7######## + + x_offset_arm7 = (results_vis[i]['bodies']['candidate'][6][0]-results_vis[i]['bodies']['candidate'][7][0])*(1.-arm7_ratio) + y_offset_arm7 = (results_vis[i]['bodies']['candidate'][6][1]-results_vis[i]['bodies']['candidate'][7][1])*(1.-arm7_ratio) + + results_vis[i]['bodies']['candidate'][7,0] += x_offset_arm7 + results_vis[i]['bodies']['candidate'][7,1] += y_offset_arm7 + results_vis[i]['hands'][0,:,0] += x_offset_arm7 + results_vis[i]['hands'][0,:,1] += y_offset_arm7 + + ########head14######## + + x_offset_head14 = (results_vis[i]['bodies']['candidate'][0][0]-results_vis[i]['bodies']['candidate'][14][0])*(1.-head14_ratio) + y_offset_head14 = (results_vis[i]['bodies']['candidate'][0][1]-results_vis[i]['bodies']['candidate'][14][1])*(1.-head14_ratio) + + results_vis[i]['bodies']['candidate'][14,0] += x_offset_head14 + results_vis[i]['bodies']['candidate'][14,1] += y_offset_head14 + results_vis[i]['bodies']['candidate'][16,0] += x_offset_head14 + results_vis[i]['bodies']['candidate'][16,1] += y_offset_head14 + + ########head15######## + + x_offset_head15 = (results_vis[i]['bodies']['candidate'][0][0]-results_vis[i]['bodies']['candidate'][15][0])*(1.-head15_ratio) + y_offset_head15 = (results_vis[i]['bodies']['candidate'][0][1]-results_vis[i]['bodies']['candidate'][15][1])*(1.-head15_ratio) + + results_vis[i]['bodies']['candidate'][15,0] += x_offset_head15 + results_vis[i]['bodies']['candidate'][15,1] += y_offset_head15 + results_vis[i]['bodies']['candidate'][17,0] += x_offset_head15 + results_vis[i]['bodies']['candidate'][17,1] += y_offset_head15 + + ########head16######## + + x_offset_head16 = (results_vis[i]['bodies']['candidate'][14][0]-results_vis[i]['bodies']['candidate'][16][0])*(1.-head16_ratio) + y_offset_head16 = (results_vis[i]['bodies']['candidate'][14][1]-results_vis[i]['bodies']['candidate'][16][1])*(1.-head16_ratio) + + results_vis[i]['bodies']['candidate'][16,0] += x_offset_head16 + results_vis[i]['bodies']['candidate'][16,1] += y_offset_head16 + + ########head17######## + x_offset_head17 = (results_vis[i]['bodies']['candidate'][15][0]-results_vis[i]['bodies']['candidate'][17][0])*(1.-head17_ratio) + y_offset_head17 = (results_vis[i]['bodies']['candidate'][15][1]-results_vis[i]['bodies']['candidate'][17][1])*(1.-head17_ratio) + + results_vis[i]['bodies']['candidate'][17,0] += x_offset_head17 + results_vis[i]['bodies']['candidate'][17,1] += y_offset_head17 + + # ########MovingAverage######## + + ########left leg######## + x_offset_ll1 = (results_vis[i]['bodies']['candidate'][9][0]-results_vis[i]['bodies']['candidate'][8][0])*(ll1_ratio-1.) + y_offset_ll1 = (results_vis[i]['bodies']['candidate'][9][1]-results_vis[i]['bodies']['candidate'][8][1])*(ll1_ratio-1.) + + results_vis[i]['bodies']['candidate'][9,0] += x_offset_ll1 + results_vis[i]['bodies']['candidate'][9,1] += y_offset_ll1 + results_vis[i]['bodies']['candidate'][10,0] += x_offset_ll1 + results_vis[i]['bodies']['candidate'][10,1] += y_offset_ll1 + results_vis[i]['bodies']['candidate'][19,0] += x_offset_ll1 + results_vis[i]['bodies']['candidate'][19,1] += y_offset_ll1 + + + + x_offset_ll2 = (results_vis[i]['bodies']['candidate'][10][0]-results_vis[i]['bodies']['candidate'][9][0])*(ll2_ratio-1.) + y_offset_ll2 = (results_vis[i]['bodies']['candidate'][10][1]-results_vis[i]['bodies']['candidate'][9][1])*(ll2_ratio-1.) + + results_vis[i]['bodies']['candidate'][10,0] += x_offset_ll2 + results_vis[i]['bodies']['candidate'][10,1] += y_offset_ll2 + results_vis[i]['bodies']['candidate'][19,0] += x_offset_ll2 + results_vis[i]['bodies']['candidate'][19,1] += y_offset_ll2 + + ########right leg######## + + x_offset_rl1 = (results_vis[i]['bodies']['candidate'][12][0]-results_vis[i]['bodies']['candidate'][11][0])*(rl1_ratio-1.) + y_offset_rl1 = (results_vis[i]['bodies']['candidate'][12][1]-results_vis[i]['bodies']['candidate'][11][1])*(rl1_ratio-1.) + + results_vis[i]['bodies']['candidate'][12,0] += x_offset_rl1 + results_vis[i]['bodies']['candidate'][12,1] += y_offset_rl1 + results_vis[i]['bodies']['candidate'][13,0] += x_offset_rl1 + results_vis[i]['bodies']['candidate'][13,1] += y_offset_rl1 + results_vis[i]['bodies']['candidate'][18,0] += x_offset_rl1 + results_vis[i]['bodies']['candidate'][18,1] += y_offset_rl1 + + + x_offset_rl2 = (results_vis[i]['bodies']['candidate'][13][0]-results_vis[i]['bodies']['candidate'][12][0])*(rl2_ratio-1.) + y_offset_rl2 = (results_vis[i]['bodies']['candidate'][13][1]-results_vis[i]['bodies']['candidate'][12][1])*(rl2_ratio-1.) + + results_vis[i]['bodies']['candidate'][13,0] += x_offset_rl2 + results_vis[i]['bodies']['candidate'][13,1] += y_offset_rl2 + results_vis[i]['bodies']['candidate'][18,0] += x_offset_rl2 + results_vis[i]['bodies']['candidate'][18,1] += y_offset_rl2 + + results_vis[i]['bodies']['candidate'] += offset[np.newaxis, :] + results_vis[i]['faces'] += offset[np.newaxis, np.newaxis, :] + results_vis[i]['hands'] += offset[np.newaxis, np.newaxis, :] + + for i in range(len(results_vis)): + dwpose_woface, dwpose_wface = draw_pose(results_vis[i], H=768, W=512) + img_path = save_motion+'/' + str(i).zfill(4) + '.jpg' + cv2.imwrite(img_path, dwpose_woface) + + dwpose_woface, dwpose_wface = draw_pose(pose_ref, H=768, W=512) + img_path = save_warp+'/' + 'ref_pose.jpg' + cv2.imwrite(img_path, dwpose_woface) + + +logger = get_logger('dw pose extraction') + + +if __name__=='__main__': + def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument("--ref_name", type=str, default="data/images/IMG_20240514_104337.jpg",) + parser.add_argument("--source_video_paths", type=str, default="data/videos/source_video.mp4",) + parser.add_argument("--saved_pose_dir", type=str, default="data/saved_pose/IMG_20240514_104337",) + args = parser.parse_args() + + return args + + args = parse_args() + mp_main(args) \ No newline at end of file diff --git a/animation/UniAnimate/test_func/save_targer_keys.py b/animation/UniAnimate/test_func/save_targer_keys.py new file mode 100644 index 0000000..b2d7545 --- /dev/null +++ b/animation/UniAnimate/test_func/save_targer_keys.py @@ -0,0 +1,108 @@ +import os +import sys +import json +import torch +import imageio +import numpy as np +import os.path as osp +sys.path.insert(0, '/'.join(osp.realpath(__file__).split('/')[:-2])) +from thop import profile +from ptflops import get_model_complexity_info + +import artist.data as data +from tools.modules.config import cfg +from tools.modules.unet.util import * +from utils.config import Config as pConfig +from utils.registry_class import ENGINE, MODEL + + +def save_temporal_key(): + cfg_update = pConfig(load=True) + + for k, v in cfg_update.cfg_dict.items(): + if isinstance(v, dict) and k in cfg: + cfg[k].update(v) + else: + cfg[k] = v + + model = MODEL.build(cfg.UNet) + + temp_name = '' + temp_key_list = [] + spth = 'workspace/module_list/UNetSD_I2V_vs_Text_temporal_key_list.json' + for name, module in model.named_modules(): + if isinstance(module, (TemporalTransformer, TemporalTransformer_attemask, TemporalAttentionBlock, TemporalAttentionMultiBlock, TemporalConvBlock_v2, TemporalConvBlock)): + temp_name = name + print(f'Model: {name}') + elif isinstance(module, (ResidualBlock, ResBlock, SpatialTransformer, Upsample, Downsample)): + temp_name = '' + + if hasattr(module, 'weight'): + if temp_name != '' and (temp_name in name): + temp_key_list.append(name) + print(f'{name}') + # print(name) + + save_module_list = [] + for k, p in model.named_parameters(): + for item in temp_key_list: + if item in k: + print(f'{item} --> {k}') + save_module_list.append(k) + + print(int(sum(p.numel() for k, p in model.named_parameters()) / (1024 ** 2)), 'M parameters') + + # spth = 'workspace/module_list/{}' + json.dump(save_module_list, open(spth, 'w')) + a = 0 + + +def save_spatial_key(): + cfg_update = pConfig(load=True) + + for k, v in cfg_update.cfg_dict.items(): + if isinstance(v, dict) and k in cfg: + cfg[k].update(v) + else: + cfg[k] = v + + model = MODEL.build(cfg.UNet) + temp_name = '' + temp_key_list = [] + spth = 'workspace/module_list/UNetSD_I2V_HQ_P_spatial_key_list.json' + for name, module in model.named_modules(): + if isinstance(module, (ResidualBlock, ResBlock, SpatialTransformer, Upsample, Downsample)): + temp_name = name + print(f'Model: {name}') + elif isinstance(module, (TemporalTransformer, TemporalTransformer_attemask, TemporalAttentionBlock, TemporalAttentionMultiBlock, TemporalConvBlock_v2, TemporalConvBlock)): + temp_name = '' + + if hasattr(module, 'weight'): + if temp_name != '' and (temp_name in name): + temp_key_list.append(name) + print(f'{name}') + # print(name) + + save_module_list = [] + for k, p in model.named_parameters(): + for item in temp_key_list: + if item in k: + print(f'{item} --> {k}') + save_module_list.append(k) + + print(int(sum(p.numel() for k, p in model.named_parameters()) / (1024 ** 2)), 'M parameters') + + # spth = 'workspace/module_list/{}' + json.dump(save_module_list, open(spth, 'w')) + a = 0 + + +if __name__ == '__main__': + # save_temporal_key() + save_spatial_key() + + + +# print([k for (k, _) in self.input_blocks.named_parameters()]) + + diff --git a/animation/UniAnimate/test_func/test_EndDec.py b/animation/UniAnimate/test_func/test_EndDec.py new file mode 100644 index 0000000..0f256b6 --- /dev/null +++ b/animation/UniAnimate/test_func/test_EndDec.py @@ -0,0 +1,95 @@ +import os +import sys +import torch +import imageio +import numpy as np +import os.path as osp +sys.path.insert(0, '/'.join(osp.realpath(__file__).split('/')[:-2])) +from PIL import Image, ImageDraw, ImageFont + +from einops import rearrange + +from tools import * +import utils.transforms as data +from utils.seed import setup_seed +from tools.modules.config import cfg +from utils.config import Config as pConfig +from utils.registry_class import ENGINE, DATASETS, AUTO_ENCODER + + +def test_enc_dec(gpu=0): + setup_seed(0) + cfg_update = pConfig(load=True) + + for k, v in cfg_update.cfg_dict.items(): + if isinstance(v, dict) and k in cfg: + cfg[k].update(v) + else: + cfg[k] = v + + save_dir = os.path.join('workspace/test_data/autoencoder', cfg.auto_encoder['type']) + os.system('rm -rf %s' % (save_dir)) + os.makedirs(save_dir, exist_ok=True) + + train_trans = data.Compose([ + data.CenterCropWide(size=cfg.resolution), + data.ToTensor(), + data.Normalize(mean=cfg.mean, std=cfg.std)]) + + vit_trans = data.Compose([ + data.CenterCropWide(size=(cfg.resolution[0], cfg.resolution[0])) if cfg.resolution[0]>cfg.vit_resolution[0] else data.CenterCropWide(size=cfg.vit_resolution), + data.Resize(cfg.vit_resolution), + data.ToTensor(), + data.Normalize(mean=cfg.vit_mean, std=cfg.vit_std)]) + + video_mean = torch.tensor(cfg.mean).view(1, -1, 1, 1) #n c f h w + video_std = torch.tensor(cfg.std).view(1, -1, 1, 1) #n c f h w + + txt_size = cfg.resolution[1] + nc = int(38 * (txt_size / 256)) + font = ImageFont.truetype('data/font/DejaVuSans.ttf', size=13) + + dataset = DATASETS.build(cfg.vid_dataset, sample_fps=4, transforms=train_trans, vit_transforms=vit_trans) + print('There are %d videos' % (len(dataset))) + + autoencoder = AUTO_ENCODER.build(cfg.auto_encoder) + autoencoder.eval() # freeze + for param in autoencoder.parameters(): + param.requires_grad = False + autoencoder.to(gpu) + for idx, item in enumerate(dataset): + local_path = os.path.join(save_dir, '%04d.mp4' % idx) + # ref_frame, video_data, caption = item + ref_frame, vit_frame, video_data = item[:3] + video_data = video_data.to(gpu) + + image_list = [] + video_data_list = torch.chunk(video_data, video_data.shape[0]//cfg.chunk_size,dim=0) + with torch.no_grad(): + decode_data = [] + for chunk_data in video_data_list: + latent_z = autoencoder.encode_firsr_stage(chunk_data).detach() + # latent_z = get_first_stage_encoding(encoder_posterior).detach() + kwargs = {"timesteps": chunk_data.shape[0]} + recons_data = autoencoder.decode(latent_z, **kwargs) + + vis_data = torch.cat([chunk_data, recons_data], dim=2).cpu() + vis_data = vis_data.mul_(video_std).add_(video_mean) # 8x3x16x256x384 + vis_data = vis_data.cpu() + vis_data.clamp_(0, 1) + vis_data = vis_data.permute(0, 2, 3, 1) + vis_data = [(image.numpy() * 255).astype('uint8') for image in vis_data] + image_list.extend(vis_data) + + num_image = len(image_list) + frame_dir = os.path.join(save_dir, 'temp') + os.makedirs(frame_dir, exist_ok=True) + for idx in range(num_image): + tpth = os.path.join(frame_dir, '%04d.png' % (idx+1)) + cv2.imwrite(tpth, image_list[idx][:,:,::-1], [int(cv2.IMWRITE_JPEG_QUALITY), 100]) + cmd = f'ffmpeg -y -f image2 -loglevel quiet -framerate 8 -i {frame_dir}/%04d.png -vcodec libx264 -crf 17 -pix_fmt yuv420p {local_path}' + os.system(cmd); os.system(f'rm -rf {frame_dir}') + + +if __name__ == '__main__': + test_enc_dec() diff --git a/animation/UniAnimate/test_func/test_dataset.py b/animation/UniAnimate/test_func/test_dataset.py new file mode 100644 index 0000000..33f2a35 --- /dev/null +++ b/animation/UniAnimate/test_func/test_dataset.py @@ -0,0 +1,152 @@ +import os +import sys +import imageio +import numpy as np +import os.path as osp +sys.path.insert(0, '/'.join(osp.realpath(__file__).split('/')[:-2])) +from PIL import Image, ImageDraw, ImageFont +import torchvision.transforms as T + +import utils.transforms as data +from tools.modules.config import cfg +from utils.config import Config as pConfig +from utils.registry_class import ENGINE, DATASETS + +from tools import * + +def test_video_dataset(): + cfg_update = pConfig(load=True) + + for k, v in cfg_update.cfg_dict.items(): + if isinstance(v, dict) and k in cfg: + cfg[k].update(v) + else: + cfg[k] = v + + exp_name = os.path.basename(cfg.cfg_file).split('.')[0] + save_dir = os.path.join('workspace', 'test_data/datasets', cfg.vid_dataset['type'], exp_name) + os.system('rm -rf %s' % (save_dir)) + os.makedirs(save_dir, exist_ok=True) + + train_trans = data.Compose([ + data.CenterCropWide(size=cfg.resolution), + data.ToTensor(), + data.Normalize(mean=cfg.mean, std=cfg.std)]) + vit_trans = T.Compose([ + data.CenterCropWide(cfg.vit_resolution), + T.ToTensor(), + T.Normalize(mean=cfg.vit_mean, std=cfg.vit_std)]) + + video_mean = torch.tensor(cfg.mean).view(1, -1, 1, 1) #n c f h w + video_std = torch.tensor(cfg.std).view(1, -1, 1, 1) #n c f h w + + img_mean = torch.tensor(cfg.mean).view(-1, 1, 1) # c f h w + img_std = torch.tensor(cfg.std).view(-1, 1, 1) # c f h w + + vit_mean = torch.tensor(cfg.vit_mean).view(-1, 1, 1) # c f h w + vit_std = torch.tensor(cfg.vit_std).view(-1, 1, 1) # c f h w + + txt_size = cfg.resolution[1] + nc = int(38 * (txt_size / 256)) + font = ImageFont.truetype('data/font/DejaVuSans.ttf', size=13) + + dataset = DATASETS.build(cfg.vid_dataset, sample_fps=cfg.sample_fps[0], transforms=train_trans, vit_transforms=vit_trans) + print('There are %d videos' % (len(dataset))) + for idx, item in enumerate(dataset): + ref_frame, vit_frame, video_data, caption, video_key = item + + video_data = video_data.mul_(video_std).add_(video_mean) + video_data.clamp_(0, 1) + video_data = video_data.permute(0, 2, 3, 1) + video_data = [(image.numpy() * 255).astype('uint8') for image in video_data] + + # Single Image + ref_frame = ref_frame.mul_(img_mean).add_(img_std) + ref_frame.clamp_(0, 1) + ref_frame = ref_frame.permute(1, 2, 0) + ref_frame = (ref_frame.numpy() * 255).astype('uint8') + + # Text image + txt_img = Image.new("RGB", (txt_size, txt_size), color="white") + draw = ImageDraw.Draw(txt_img) + lines = "\n".join(caption[start:start + nc] for start in range(0, len(caption), nc)) + draw.text((0, 0), lines, fill="black", font=font) + txt_img = np.array(txt_img) + + video_data = [np.concatenate([ref_frame, u, txt_img], axis=1) for u in video_data] + spath = os.path.join(save_dir, '%04d.gif' % (idx)) + imageio.mimwrite(spath, video_data, fps =8) + + # if idx > 100: break + + +def test_vit_image(test_video_flag=True): + cfg_update = pConfig(load=True) + + for k, v in cfg_update.cfg_dict.items(): + if isinstance(v, dict) and k in cfg: + cfg[k].update(v) + else: + cfg[k] = v + + exp_name = os.path.basename(cfg.cfg_file).split('.')[0] + save_dir = os.path.join('workspace', 'test_data/datasets', cfg.img_dataset['type'], exp_name) + os.system('rm -rf %s' % (save_dir)) + os.makedirs(save_dir, exist_ok=True) + + train_trans = data.Compose([ + data.CenterCropWide(size=cfg.resolution), + data.ToTensor(), + data.Normalize(mean=cfg.mean, std=cfg.std)]) + vit_trans = data.Compose([ + data.CenterCropWide(cfg.resolution), + data.Resize(cfg.vit_resolution), + data.ToTensor(), + data.Normalize(mean=cfg.vit_mean, std=cfg.vit_std)]) + + img_mean = torch.tensor(cfg.mean).view(-1, 1, 1) # c f h w + img_std = torch.tensor(cfg.std).view(-1, 1, 1) # c f h w + + vit_mean = torch.tensor(cfg.vit_mean).view(-1, 1, 1) # c f h w + vit_std = torch.tensor(cfg.vit_std).view(-1, 1, 1) # c f h w + + txt_size = cfg.resolution[1] + nc = int(38 * (txt_size / 256)) + font = ImageFont.truetype('artist/font/DejaVuSans.ttf', size=13) + + dataset = DATASETS.build(cfg.img_dataset, transforms=train_trans, vit_transforms=vit_trans) + print('There are %d videos' % (len(dataset))) + for idx, item in enumerate(dataset): + ref_frame, vit_frame, video_data, caption, video_key = item + video_data = video_data.mul_(img_std).add_(img_mean) + video_data.clamp_(0, 1) + video_data = video_data.permute(0, 2, 3, 1) + video_data = [(image.numpy() * 255).astype('uint8') for image in video_data] + + # Single Image + vit_frame = vit_frame.mul_(vit_std).add_(vit_mean) + vit_frame.clamp_(0, 1) + vit_frame = vit_frame.permute(1, 2, 0) + vit_frame = (vit_frame.numpy() * 255).astype('uint8') + + zero_frame = np.zeros((cfg.resolution[1], cfg.resolution[1], 3), dtype=np.uint8) + zero_frame[:vit_frame.shape[0], :vit_frame.shape[1], :] = vit_frame + + # Text image + txt_img = Image.new("RGB", (txt_size, txt_size), color="white") + draw = ImageDraw.Draw(txt_img) + lines = "\n".join(caption[start:start + nc] for start in range(0, len(caption), nc)) + draw.text((0, 0), lines, fill="black", font=font) + txt_img = np.array(txt_img) + + video_data = [np.concatenate([zero_frame, u, txt_img], axis=1) for u in video_data] + spath = os.path.join(save_dir, '%04d.gif' % (idx)) + imageio.mimwrite(spath, video_data, fps =8) + + # if idx > 100: break + + +if __name__ == '__main__': + # test_video_dataset() + test_vit_image() + diff --git a/animation/UniAnimate/test_func/test_models.py b/animation/UniAnimate/test_func/test_models.py new file mode 100644 index 0000000..aa7f78f --- /dev/null +++ b/animation/UniAnimate/test_func/test_models.py @@ -0,0 +1,56 @@ +import os +import sys +import torch +import imageio +import numpy as np +import os.path as osp +sys.path.insert(0, '/'.join(osp.realpath(__file__).split('/')[:-2])) +from thop import profile +from ptflops import get_model_complexity_info + +import artist.data as data +from tools.modules.config import cfg +from utils.config import Config as pConfig +from utils.registry_class import ENGINE, MODEL + + +def test_model(): + cfg_update = pConfig(load=True) + + for k, v in cfg_update.cfg_dict.items(): + if isinstance(v, dict) and k in cfg: + cfg[k].update(v) + else: + cfg[k] = v + + model = MODEL.build(cfg.UNet) + print(int(sum(p.numel() for k, p in model.named_parameters()) / (1024 ** 2)), 'M parameters') + + # state_dict = torch.load('cache/pretrain_model/jiuniu_0600000.pth', map_location='cpu') + # model.load_state_dict(state_dict, strict=False) + model = model.cuda() + + x = torch.Tensor(1, 4, 16, 32, 56).cuda() + t = torch.Tensor(1).cuda() + sims = torch.Tensor(1, 32).cuda() + fps = torch.Tensor([8]).cuda() + y = torch.Tensor(1, 1, 1024).cuda() + image = torch.Tensor(1, 3, 256, 448).cuda() + + ret = model(x=x, t=t, y=y, ori_img=image, sims=sims, fps=fps) + print('Out shape if {}'.format(ret.shape)) + + # flops, params = profile(model=model, inputs=(x, t, y, image, sims, fps)) + # print('Model: {:.2f} GFLOPs and {:.2f}M parameters'.format(flops/1e9, params/1e6)) + + def prepare_input(resolution): + return dict(x=[x, t, y, image, sims, fps]) + + flops, params = get_model_complexity_info(model, (1, 4, 16, 32, 56), + input_constructor = prepare_input, + as_strings=True, print_per_layer_stat=True) + print(' - Flops: ' + flops) + print(' - Params: ' + params) + +if __name__ == '__main__': + test_model() diff --git a/animation/UniAnimate/test_func/test_save_video.py b/animation/UniAnimate/test_func/test_save_video.py new file mode 100644 index 0000000..926308f --- /dev/null +++ b/animation/UniAnimate/test_func/test_save_video.py @@ -0,0 +1,24 @@ +import numpy as np +import cv2 + +cap = cv2.VideoCapture('workspace/img_dir/tst.mp4') + +fourcc = cv2.VideoWriter_fourcc(*'H264') + +ret, frame = cap.read() +vid_size = frame.shape[:2][::-1] + +out = cv2.VideoWriter('workspace/img_dir/testwrite.mp4',fourcc, 8, vid_size) +out.write(frame) + +while(cap.isOpened()): + ret, frame = cap.read() + if not ret: break + out.write(frame) + + +cap.release() +out.release() + + + diff --git a/animation/UniAnimate/tools/__init__.py b/animation/UniAnimate/tools/__init__.py new file mode 100644 index 0000000..62ee854 --- /dev/null +++ b/animation/UniAnimate/tools/__init__.py @@ -0,0 +1,3 @@ +from .datasets import * +from .modules import * +from .inferences import * diff --git a/animation/UniAnimate/tools/datasets/__init__.py b/animation/UniAnimate/tools/datasets/__init__.py new file mode 100644 index 0000000..8c8ab56 --- /dev/null +++ b/animation/UniAnimate/tools/datasets/__init__.py @@ -0,0 +1,2 @@ +from .image_dataset import * +from .video_dataset import * diff --git a/animation/UniAnimate/tools/datasets/image_dataset.py b/animation/UniAnimate/tools/datasets/image_dataset.py new file mode 100644 index 0000000..3b710be --- /dev/null +++ b/animation/UniAnimate/tools/datasets/image_dataset.py @@ -0,0 +1,86 @@ +import os +import cv2 +import torch +import random +import logging +import tempfile +import numpy as np +from copy import copy +from PIL import Image +from io import BytesIO +from torch.utils.data import Dataset +from utils.registry_class import DATASETS + +@DATASETS.register_class() +class ImageDataset(Dataset): + def __init__(self, + data_list, + data_dir_list, + max_words=1000, + vit_resolution=[224, 224], + resolution=(384, 256), + max_frames=1, + transforms=None, + vit_transforms=None, + **kwargs): + + self.max_frames = max_frames + self.resolution = resolution + self.transforms = transforms + self.vit_resolution = vit_resolution + self.vit_transforms = vit_transforms + + image_list = [] + for item_path, data_dir in zip(data_list, data_dir_list): + lines = open(item_path, 'r').readlines() + lines = [[data_dir, item.strip()] for item in lines] + image_list.extend(lines) + self.image_list = image_list + + def __len__(self): + return len(self.image_list) + + def __getitem__(self, index): + data_dir, file_path = self.image_list[index] + img_key = file_path.split('|||')[0] + try: + ref_frame, vit_frame, video_data, caption = self._get_image_data(data_dir, file_path) + except Exception as e: + logging.info('{} get frames failed... with error: {}'.format(img_key, e)) + caption = '' + img_key = '' + ref_frame = torch.zeros(3, self.resolution[1], self.resolution[0]) + vit_frame = torch.zeros(3, self.vit_resolution[1], self.vit_resolution[0]) + video_data = torch.zeros(self.max_frames, 3, self.resolution[1], self.resolution[0]) + return ref_frame, vit_frame, video_data, caption, img_key + + def _get_image_data(self, data_dir, file_path): + frame_list = [] + img_key, caption = file_path.split('|||') + file_path = os.path.join(data_dir, img_key) + for _ in range(5): + try: + image = Image.open(file_path) + if image.mode != 'RGB': + image = image.convert('RGB') + frame_list.append(image) + break + except Exception as e: + logging.info('{} read video frame failed with error: {}'.format(img_key, e)) + continue + + video_data = torch.zeros(self.max_frames, 3, self.resolution[1], self.resolution[0]) + try: + if len(frame_list) > 0: + mid_frame = frame_list[0] + vit_frame = self.vit_transforms(mid_frame) + frame_tensor = self.transforms(frame_list) + video_data[:len(frame_list), ...] = frame_tensor + else: + vit_frame = torch.zeros(3, self.vit_resolution[1], self.vit_resolution[0]) + except: + vit_frame = torch.zeros(3, self.vit_resolution[1], self.vit_resolution[0]) + ref_frame = copy(video_data[0]) + + return ref_frame, vit_frame, video_data, caption + diff --git a/animation/UniAnimate/tools/datasets/video_dataset.py b/animation/UniAnimate/tools/datasets/video_dataset.py new file mode 100644 index 0000000..f4a41d5 --- /dev/null +++ b/animation/UniAnimate/tools/datasets/video_dataset.py @@ -0,0 +1,118 @@ +import os +import cv2 +import json +import torch +import random +import logging +import tempfile +import numpy as np +from copy import copy +from PIL import Image +from torch.utils.data import Dataset +from utils.registry_class import DATASETS + + +@DATASETS.register_class() +class VideoDataset(Dataset): + def __init__(self, + data_list, + data_dir_list, + max_words=1000, + resolution=(384, 256), + vit_resolution=(224, 224), + max_frames=16, + sample_fps=8, + transforms=None, + vit_transforms=None, + get_first_frame=False, + **kwargs): + + self.max_words = max_words + self.max_frames = max_frames + self.resolution = resolution + self.vit_resolution = vit_resolution + self.sample_fps = sample_fps + self.transforms = transforms + self.vit_transforms = vit_transforms + self.get_first_frame = get_first_frame + + image_list = [] + for item_path, data_dir in zip(data_list, data_dir_list): + lines = open(item_path, 'r').readlines() + lines = [[data_dir, item] for item in lines] + image_list.extend(lines) + self.image_list = image_list + + + def __getitem__(self, index): + data_dir, file_path = self.image_list[index] + video_key = file_path.split('|||')[0] + try: + ref_frame, vit_frame, video_data, caption = self._get_video_data(data_dir, file_path) + except Exception as e: + logging.info('{} get frames failed... with error: {}'.format(video_key, e)) + caption = '' + video_key = '' + ref_frame = torch.zeros(3, self.resolution[1], self.resolution[0]) + vit_frame = torch.zeros(3, self.vit_resolution[1], self.vit_resolution[0]) + video_data = torch.zeros(self.max_frames, 3, self.resolution[1], self.resolution[0]) + return ref_frame, vit_frame, video_data, caption, video_key + + + def _get_video_data(self, data_dir, file_path): + video_key, caption = file_path.split('|||') + file_path = os.path.join(data_dir, video_key) + + for _ in range(5): + try: + capture = cv2.VideoCapture(file_path) + _fps = capture.get(cv2.CAP_PROP_FPS) + _total_frame_num = capture.get(cv2.CAP_PROP_FRAME_COUNT) + stride = round(_fps / self.sample_fps) + cover_frame_num = (stride * self.max_frames) + if _total_frame_num < cover_frame_num + 5: + start_frame = 0 + end_frame = _total_frame_num + else: + start_frame = random.randint(0, _total_frame_num-cover_frame_num-5) + end_frame = start_frame + cover_frame_num + + pointer, frame_list = 0, [] + while(True): + ret, frame = capture.read() + pointer +=1 + if (not ret) or (frame is None): break + if pointer < start_frame: continue + if pointer >= end_frame - 1: break + if (pointer - start_frame) % stride == 0: + frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + frame = Image.fromarray(frame) + frame_list.append(frame) + break + except Exception as e: + logging.info('{} read video frame failed with error: {}'.format(video_key, e)) + continue + + video_data = torch.zeros(self.max_frames, 3, self.resolution[1], self.resolution[0]) + if self.get_first_frame: + ref_idx = 0 + else: + ref_idx = int(len(frame_list)/2) + try: + if len(frame_list)>0: + mid_frame = copy(frame_list[ref_idx]) + vit_frame = self.vit_transforms(mid_frame) + frames = self.transforms(frame_list) + video_data[:len(frame_list), ...] = frames + else: + vit_frame = torch.zeros(3, self.vit_resolution[1], self.vit_resolution[0]) + except: + vit_frame = torch.zeros(3, self.vit_resolution[1], self.vit_resolution[0]) + ref_frame = copy(frames[ref_idx]) + + return ref_frame, vit_frame, video_data, caption + + def __len__(self): + return len(self.image_list) + + diff --git a/animation/UniAnimate/tools/inferences/__init__.py b/animation/UniAnimate/tools/inferences/__init__.py new file mode 100644 index 0000000..1ac1b5c --- /dev/null +++ b/animation/UniAnimate/tools/inferences/__init__.py @@ -0,0 +1,2 @@ +from .inference_unianimate_entrance import * +from .inference_unianimate_long_entrance import * diff --git a/animation/UniAnimate/tools/inferences/inference_unianimate_entrance.py b/animation/UniAnimate/tools/inferences/inference_unianimate_entrance.py new file mode 100644 index 0000000..0086caf --- /dev/null +++ b/animation/UniAnimate/tools/inferences/inference_unianimate_entrance.py @@ -0,0 +1,483 @@ +''' +/* +*Copyright (c) 2021, Alibaba Group; +*Licensed under the Apache License, Version 2.0 (the "License"); +*you may not use this file except in compliance with the License. +*You may obtain a copy of the License at + +* http://www.apache.org/licenses/LICENSE-2.0 + +*Unless required by applicable law or agreed to in writing, software +*distributed under the License is distributed on an "AS IS" BASIS, +*WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +*See the License for the specific language governing permissions and +*limitations under the License. +*/ +''' + +import os +import re +import os.path as osp +import sys +sys.path.insert(0, '/'.join(osp.realpath(__file__).split('/')[:-4])) +import json +import math +import torch +import pynvml +import logging +import cv2 +import numpy as np +from PIL import Image +from tqdm import tqdm +import torch.cuda.amp as amp +from importlib import reload +import torch.distributed as dist +import torch.multiprocessing as mp +import random +from einops import rearrange +import torchvision.transforms as T +import torchvision.transforms.functional as TF +from torch.nn.parallel import DistributedDataParallel + +import utils.transforms as data +from ..modules.config import cfg +from utils.seed import setup_seed +from utils.multi_port import find_free_port +from utils.assign_cfg import assign_signle_cfg +from utils.distributed import generalized_all_gather, all_reduce +from utils.video_op import save_i2vgen_video, save_t2vhigen_video_safe, save_video_multiple_conditions_not_gif_horizontal_3col +from tools.modules.autoencoder import get_first_stage_encoding +from utils.registry_class import INFER_ENGINE, MODEL, EMBEDDER, AUTO_ENCODER, DIFFUSION +from copy import copy +import cv2 + + +@INFER_ENGINE.register_function() +def inference_unianimate_entrance(cfg_update, **kwargs): + for k, v in cfg_update.items(): + if isinstance(v, dict) and k in cfg: + cfg[k].update(v) + else: + cfg[k] = v + + if not 'MASTER_ADDR' in os.environ: + os.environ['MASTER_ADDR']='localhost' + os.environ['MASTER_PORT']= find_free_port() + cfg.pmi_rank = int(os.getenv('RANK', 0)) + cfg.pmi_world_size = int(os.getenv('WORLD_SIZE', 1)) + + if cfg.debug: + cfg.gpus_per_machine = 1 + cfg.world_size = 1 + else: + cfg.gpus_per_machine = torch.cuda.device_count() + cfg.world_size = cfg.pmi_world_size * cfg.gpus_per_machine + + if cfg.world_size == 1: + worker(0, cfg, cfg_update) + else: + mp.spawn(worker, nprocs=cfg.gpus_per_machine, args=(cfg, cfg_update)) + return cfg + + +def make_masked_images(imgs, masks): + masked_imgs = [] + for i, mask in enumerate(masks): + # concatenation + masked_imgs.append(torch.cat([imgs[i] * (1 - mask), (1 - mask)], dim=1)) + return torch.stack(masked_imgs, dim=0) + +def load_video_frames(ref_image_path, pose_file_path, train_trans, vit_transforms, train_trans_pose, max_frames=32, frame_interval = 1, resolution=[512, 768], get_first_frame=True, vit_resolution=[224, 224]): + for _ in range(5): + try: + dwpose_all = {} + frames_all = {} + for ii_index in sorted(os.listdir(pose_file_path)): + if ii_index != "ref_pose.jpg": + dwpose_all[ii_index] = Image.open(os.path.join(pose_file_path, ii_index)) + frames_all[ii_index] = Image.fromarray(cv2.cvtColor(cv2.imread(ref_image_path), cv2.COLOR_BGR2RGB)) + + pose_ref = Image.open(os.path.join(pose_file_path, "ref_pose.jpg")) + + # Sample max_frames poses for video generation + stride = frame_interval + total_frame_num = len(frames_all) + cover_frame_num = (stride * (max_frames - 1) + 1) + + if total_frame_num < cover_frame_num: + print(f'_total_frame_num ({total_frame_num}) is smaller than cover_frame_num ({cover_frame_num}), the sampled frame interval is changed') + start_frame = 0 + end_frame = total_frame_num + stride = max((total_frame_num - 1) // (max_frames - 1), 1) + end_frame = stride * max_frames + else: + start_frame = 0 + end_frame = start_frame + cover_frame_num + + frame_list = [] + dwpose_list = [] + random_ref_frame = frames_all[list(frames_all.keys())[0]] + if random_ref_frame.mode != 'RGB': + random_ref_frame = random_ref_frame.convert('RGB') + random_ref_dwpose = pose_ref + if random_ref_dwpose.mode != 'RGB': + random_ref_dwpose = random_ref_dwpose.convert('RGB') + + for i_index in range(start_frame, end_frame, stride): + if i_index < len(frames_all): # Check index within bounds + i_key = list(frames_all.keys())[i_index] + i_frame = frames_all[i_key] + if i_frame.mode != 'RGB': + i_frame = i_frame.convert('RGB') + + i_dwpose = dwpose_all[i_key] + if i_dwpose.mode != 'RGB': + i_dwpose = i_dwpose.convert('RGB') + frame_list.append(i_frame) + dwpose_list.append(i_dwpose) + + if frame_list: + middle_indix = 0 + ref_frame = frame_list[middle_indix] + vit_frame = vit_transforms(ref_frame) + random_ref_frame_tmp = train_trans_pose(random_ref_frame) + random_ref_dwpose_tmp = train_trans_pose(random_ref_dwpose) + misc_data_tmp = torch.stack([train_trans_pose(ss) for ss in frame_list], dim=0) + video_data_tmp = torch.stack([train_trans(ss) for ss in frame_list], dim=0) + dwpose_data_tmp = torch.stack([train_trans_pose(ss) for ss in dwpose_list], dim=0) + + video_data = torch.zeros(max_frames, 3, resolution[1], resolution[0]) + dwpose_data = torch.zeros(max_frames, 3, resolution[1], resolution[0]) + misc_data = torch.zeros(max_frames, 3, resolution[1], resolution[0]) + random_ref_frame_data = torch.zeros(max_frames, 3, resolution[1], resolution[0]) + random_ref_dwpose_data = torch.zeros(max_frames, 3, resolution[1], resolution[0]) + + video_data[:len(frame_list), ...] = video_data_tmp + misc_data[:len(frame_list), ...] = misc_data_tmp + dwpose_data[:len(frame_list), ...] = dwpose_data_tmp + random_ref_frame_data[:, ...] = random_ref_frame_tmp + random_ref_dwpose_data[:, ...] = random_ref_dwpose_tmp + + return vit_frame, video_data, misc_data, dwpose_data, random_ref_frame_data, random_ref_dwpose_data + + except Exception as e: + logging.info(f'Error reading video frame: {e}') + continue + + return None, None, None, None, None, None + +def worker(gpu, cfg, cfg_update): + ''' + Inference worker for each gpu + ''' + for k, v in cfg_update.items(): + if isinstance(v, dict) and k in cfg: + cfg[k].update(v) + else: + cfg[k] = v + + cfg.gpu = gpu + cfg.seed = int(cfg.seed) + cfg.rank = cfg.pmi_rank * cfg.gpus_per_machine + gpu + setup_seed(cfg.seed + cfg.rank) + + if not cfg.debug: + torch.cuda.set_device(gpu) + torch.backends.cudnn.benchmark = True + if hasattr(cfg, "CPU_CLIP_VAE") and cfg.CPU_CLIP_VAE: + torch.backends.cudnn.benchmark = False + dist.init_process_group(backend='nccl', world_size=cfg.world_size, rank=cfg.rank) + + # [Log] Save logging and make log dir + log_dir = generalized_all_gather(cfg.log_dir)[0] + inf_name = osp.basename(cfg.cfg_file).split('.')[0] + test_model = osp.basename(cfg.test_model).split('.')[0].split('_')[-1] + + cfg.log_dir = osp.join(cfg.log_dir, '%s' % (inf_name)) + os.makedirs(cfg.log_dir, exist_ok=True) + log_file = osp.join(cfg.log_dir, 'log_%02d.txt' % (cfg.rank)) + cfg.log_file = log_file + reload(logging) + logging.basicConfig( + level=logging.INFO, + format='[%(asctime)s] %(levelname)s: %(message)s', + handlers=[ + logging.FileHandler(filename=log_file), + logging.StreamHandler(stream=sys.stdout)]) + logging.info(cfg) + logging.info(f"Running UniAnimate inference on gpu {gpu}") + + # [Diffusion] + diffusion = DIFFUSION.build(cfg.Diffusion) + + # [Data] Data Transform + train_trans = data.Compose([ + data.Resize(cfg.resolution), + data.ToTensor(), + data.Normalize(mean=cfg.mean, std=cfg.std) + ]) + + train_trans_pose = data.Compose([ + data.Resize(cfg.resolution), + data.ToTensor(), + ] + ) + + vit_transforms = T.Compose([ + data.Resize(cfg.vit_resolution), + T.ToTensor(), + T.Normalize(mean=cfg.vit_mean, std=cfg.vit_std)]) + + # [Model] embedder + clip_encoder = EMBEDDER.build(cfg.embedder) + clip_encoder.model.to(gpu) + with torch.no_grad(): + _, _, zero_y = clip_encoder(text="") + + + # [Model] auotoencoder + autoencoder = AUTO_ENCODER.build(cfg.auto_encoder) + autoencoder.eval() # freeze + for param in autoencoder.parameters(): + param.requires_grad = False + autoencoder.cuda() + + # [Model] UNet + if "config" in cfg.UNet: + cfg.UNet["config"] = cfg + cfg.UNet["zero_y"] = zero_y + model = MODEL.build(cfg.UNet) + state_dict = torch.load(cfg.test_model, map_location='cpu') + if 'state_dict' in state_dict: + state_dict = state_dict['state_dict'] + if 'step' in state_dict: + resume_step = state_dict['step'] + else: + resume_step = 0 + status = model.load_state_dict(state_dict, strict=True) + logging.info('Load model from {} with status {}'.format(cfg.test_model, status)) + model = model.to(gpu) + model.eval() + if hasattr(cfg, "CPU_CLIP_VAE") and cfg.CPU_CLIP_VAE: + model.to(torch.float16) + else: + model = DistributedDataParallel(model, device_ids=[gpu]) if not cfg.debug else model + torch.cuda.empty_cache() + + + + test_list = cfg.test_list_path + num_videos = len(test_list) + logging.info(f'There are {num_videos} videos. with {cfg.round} times') + # test_list = [item for item in test_list for _ in range(cfg.round)] + test_list = [item for _ in range(cfg.round) for item in test_list] + + for idx, file_path in enumerate(test_list): + cfg.frame_interval, ref_image_key, pose_seq_key = file_path[0], file_path[1], file_path[2] + + manual_seed = int(cfg.seed + cfg.rank + idx//num_videos) + setup_seed(manual_seed) + logging.info(f"[{idx}]/[{len(test_list)}] Begin to sample {ref_image_key}, pose sequence from {pose_seq_key} init seed {manual_seed} ...") + + + vit_frame, video_data, misc_data, dwpose_data, random_ref_frame_data, random_ref_dwpose_data = load_video_frames(ref_image_key, pose_seq_key, train_trans, vit_transforms, train_trans_pose, max_frames=cfg.max_frames, frame_interval =cfg.frame_interval, resolution=cfg.resolution) + misc_data = misc_data.unsqueeze(0).to(gpu) + vit_frame = vit_frame.unsqueeze(0).to(gpu) + dwpose_data = dwpose_data.unsqueeze(0).to(gpu) + random_ref_frame_data = random_ref_frame_data.unsqueeze(0).to(gpu) + random_ref_dwpose_data = random_ref_dwpose_data.unsqueeze(0).to(gpu) + + ### save for visualization + misc_backups = copy(misc_data) + frames_num = misc_data.shape[1] + misc_backups = rearrange(misc_backups, 'b f c h w -> b c f h w') + mv_data_video = [] + + + ### local image (first frame) + image_local = [] + if 'local_image' in cfg.video_compositions: + frames_num = misc_data.shape[1] + bs_vd_local = misc_data.shape[0] + image_local = misc_data[:,:1].clone().repeat(1,frames_num,1,1,1) + image_local_clone = rearrange(image_local, 'b f c h w -> b c f h w', b = bs_vd_local) + image_local = rearrange(image_local, 'b f c h w -> b c f h w', b = bs_vd_local) + if hasattr(cfg, "latent_local_image") and cfg.latent_local_image: + with torch.no_grad(): + temporal_length = frames_num + encoder_posterior = autoencoder.encode(video_data[:,0]) + local_image_data = get_first_stage_encoding(encoder_posterior).detach() + image_local = local_image_data.unsqueeze(1).repeat(1,temporal_length,1,1,1) # [10, 16, 4, 64, 40] + + + + ### encode the video_data + bs_vd = misc_data.shape[0] + misc_data = rearrange(misc_data, 'b f c h w -> (b f) c h w') + misc_data_list = torch.chunk(misc_data, misc_data.shape[0]//cfg.chunk_size,dim=0) + + + with torch.no_grad(): + + random_ref_frame = [] + if 'randomref' in cfg.video_compositions: + random_ref_frame_clone = rearrange(random_ref_frame_data, 'b f c h w -> b c f h w') + if hasattr(cfg, "latent_random_ref") and cfg.latent_random_ref: + + temporal_length = random_ref_frame_data.shape[1] + encoder_posterior = autoencoder.encode(random_ref_frame_data[:,0].sub(0.5).div_(0.5)) + random_ref_frame_data = get_first_stage_encoding(encoder_posterior).detach() + random_ref_frame_data = random_ref_frame_data.unsqueeze(1).repeat(1,temporal_length,1,1,1) # [10, 16, 4, 64, 40] + + random_ref_frame = rearrange(random_ref_frame_data, 'b f c h w -> b c f h w') + + + if 'dwpose' in cfg.video_compositions: + bs_vd_local = dwpose_data.shape[0] + dwpose_data_clone = rearrange(dwpose_data.clone(), 'b f c h w -> b c f h w', b = bs_vd_local) + if 'randomref_pose' in cfg.video_compositions: + dwpose_data = torch.cat([random_ref_dwpose_data[:,:1], dwpose_data], dim=1) + dwpose_data = rearrange(dwpose_data, 'b f c h w -> b c f h w', b = bs_vd_local) + + + y_visual = [] + if 'image' in cfg.video_compositions: + with torch.no_grad(): + vit_frame = vit_frame.squeeze(1) + y_visual = clip_encoder.encode_image(vit_frame).unsqueeze(1) # [60, 1024] + y_visual0 = y_visual.clone() + + + with amp.autocast(enabled=True): + pynvml.nvmlInit() + handle=pynvml.nvmlDeviceGetHandleByIndex(0) + meminfo=pynvml.nvmlDeviceGetMemoryInfo(handle) + cur_seed = torch.initial_seed() + logging.info(f"Current seed {cur_seed} ...") + + noise = torch.randn([1, 4, cfg.max_frames, int(cfg.resolution[1]/cfg.scale), int(cfg.resolution[0]/cfg.scale)]) + noise = noise.to(gpu) + + if hasattr(cfg.Diffusion, "noise_strength"): + b, c, f, _, _= noise.shape + offset_noise = torch.randn(b, c, f, 1, 1, device=noise.device) + noise = noise + cfg.Diffusion.noise_strength * offset_noise + + # add a noise prior + noise = diffusion.q_sample(random_ref_frame.clone(), getattr(cfg, "noise_prior_value", 949), noise=noise) + + # construct model inputs (CFG) + full_model_kwargs=[{ + 'y': None, + "local_image": None if len(image_local) == 0 else image_local[:], + 'image': None if len(y_visual) == 0 else y_visual0[:], + 'dwpose': None if len(dwpose_data) == 0 else dwpose_data[:], + 'randomref': None if len(random_ref_frame) == 0 else random_ref_frame[:], + }, + { + 'y': None, + "local_image": None, + 'image': None, + 'randomref': None, + 'dwpose': None, + }] + + # for visualization + full_model_kwargs_vis =[{ + 'y': None, + "local_image": None if len(image_local) == 0 else image_local_clone[:], + 'image': None, + 'dwpose': None if len(dwpose_data_clone) == 0 else dwpose_data_clone[:], + 'randomref': None if len(random_ref_frame) == 0 else random_ref_frame_clone[:, :3], + }, + { + 'y': None, + "local_image": None, + 'image': None, + 'randomref': None, + 'dwpose': None, + }] + + + partial_keys = [ + ['image', 'randomref', "dwpose"], + ] + if hasattr(cfg, "partial_keys") and cfg.partial_keys: + partial_keys = cfg.partial_keys + + + for partial_keys_one in partial_keys: + model_kwargs_one = prepare_model_kwargs(partial_keys = partial_keys_one, + full_model_kwargs = full_model_kwargs, + use_fps_condition = cfg.use_fps_condition) + model_kwargs_one_vis = prepare_model_kwargs(partial_keys = partial_keys_one, + full_model_kwargs = full_model_kwargs_vis, + use_fps_condition = cfg.use_fps_condition) + noise_one = noise + + if hasattr(cfg, "CPU_CLIP_VAE") and cfg.CPU_CLIP_VAE: + clip_encoder.cpu() # add this line + autoencoder.cpu() # add this line + torch.cuda.empty_cache() # add this line + + video_data = diffusion.ddim_sample_loop( + noise=noise_one, + model=model.eval(), + model_kwargs=model_kwargs_one, + guide_scale=cfg.guide_scale, + ddim_timesteps=cfg.ddim_timesteps, + eta=0.0) + + if hasattr(cfg, "CPU_CLIP_VAE") and cfg.CPU_CLIP_VAE: + # if run forward of autoencoder or clip_encoder second times, load them again + clip_encoder.cuda() + autoencoder.cuda() + video_data = 1. / cfg.scale_factor * video_data + video_data = rearrange(video_data, 'b c f h w -> (b f) c h w') + chunk_size = min(cfg.decoder_bs, video_data.shape[0]) + video_data_list = torch.chunk(video_data, video_data.shape[0]//chunk_size, dim=0) + decode_data = [] + for vd_data in video_data_list: + gen_frames = autoencoder.decode(vd_data) + decode_data.append(gen_frames) + video_data = torch.cat(decode_data, dim=0) + video_data = rearrange(video_data, '(b f) c h w -> b c f h w', b = cfg.batch_size).float() + + text_size = cfg.resolution[-1] + cap_name = re.sub(r'[^\w\s]', '', ref_image_key.split("/")[-1].split('.')[0]) # .replace(' ', '_') + name = f'seed_{cur_seed}' + for ii in partial_keys_one: + name = name + "_" + ii + file_name = f'rank_{cfg.world_size:02d}_{cfg.rank:02d}_{idx:02d}_{name}_{cap_name}_{cfg.resolution[1]}x{cfg.resolution[0]}.mp4' + local_path = os.path.join(cfg.log_dir, f'{file_name}') + os.makedirs(os.path.dirname(local_path), exist_ok=True) + captions = "human" + del model_kwargs_one_vis[0][list(model_kwargs_one_vis[0].keys())[0]] + del model_kwargs_one_vis[1][list(model_kwargs_one_vis[1].keys())[0]] + + save_video_multiple_conditions_not_gif_horizontal_3col(local_path, video_data.cpu(), model_kwargs_one_vis, misc_backups, + cfg.mean, cfg.std, nrow=1, save_fps=cfg.save_fps) + + # try: + # save_t2vhigen_video_safe(local_path, video_data.cpu(), captions, cfg.mean, cfg.std, text_size) + # logging.info('Save video to dir %s:' % (local_path)) + # except Exception as e: + # logging.info(f'Step: save text or video error with {e}') + + logging.info('Congratulations! The inference is completed!') + # synchronize to finish some processes + if not cfg.debug: + torch.cuda.synchronize() + dist.barrier() + +def prepare_model_kwargs(partial_keys, full_model_kwargs, use_fps_condition=False): + + if use_fps_condition is True: + partial_keys.append('fps') + + partial_model_kwargs = [{}, {}] + for partial_key in partial_keys: + partial_model_kwargs[0][partial_key] = full_model_kwargs[0][partial_key] + partial_model_kwargs[1][partial_key] = full_model_kwargs[1][partial_key] + + return partial_model_kwargs diff --git a/animation/UniAnimate/tools/inferences/inference_unianimate_long_entrance.py b/animation/UniAnimate/tools/inferences/inference_unianimate_long_entrance.py new file mode 100644 index 0000000..1d17e02 --- /dev/null +++ b/animation/UniAnimate/tools/inferences/inference_unianimate_long_entrance.py @@ -0,0 +1,508 @@ +''' +/* +*Copyright (c) 2021, Alibaba Group; +*Licensed under the Apache License, Version 2.0 (the "License"); +*you may not use this file except in compliance with the License. +*You may obtain a copy of the License at + +* http://www.apache.org/licenses/LICENSE-2.0 + +*Unless required by applicable law or agreed to in writing, software +*distributed under the License is distributed on an "AS IS" BASIS, +*WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +*See the License for the specific language governing permissions and +*limitations under the License. +*/ +''' + +import os +import re +import os.path as osp +import sys +sys.path.insert(0, '/'.join(osp.realpath(__file__).split('/')[:-4])) +import json +import math +import torch +import pynvml +import logging +import cv2 +import numpy as np +from PIL import Image +from tqdm import tqdm +import torch.cuda.amp as amp +from importlib import reload +import torch.distributed as dist +import torch.multiprocessing as mp +import random +from einops import rearrange +import torchvision.transforms as T +import torchvision.transforms.functional as TF +from torch.nn.parallel import DistributedDataParallel + +import utils.transforms as data +from ..modules.config import cfg +from utils.seed import setup_seed +from utils.multi_port import find_free_port +from utils.assign_cfg import assign_signle_cfg +from utils.distributed import generalized_all_gather, all_reduce +from utils.video_op import save_i2vgen_video, save_t2vhigen_video_safe, save_video_multiple_conditions_not_gif_horizontal_3col +from tools.modules.autoencoder import get_first_stage_encoding +from utils.registry_class import INFER_ENGINE, MODEL, EMBEDDER, AUTO_ENCODER, DIFFUSION +from copy import copy +import cv2 + + +@INFER_ENGINE.register_function() +def inference_unianimate_long_entrance(cfg_update, **kwargs): + for k, v in cfg_update.items(): + if isinstance(v, dict) and k in cfg: + cfg[k].update(v) + else: + cfg[k] = v + + if not 'MASTER_ADDR' in os.environ: + os.environ['MASTER_ADDR']='localhost' + os.environ['MASTER_PORT']= find_free_port() + cfg.pmi_rank = int(os.getenv('RANK', 0)) + cfg.pmi_world_size = int(os.getenv('WORLD_SIZE', 1)) + + if cfg.debug: + cfg.gpus_per_machine = 1 + cfg.world_size = 1 + else: + cfg.gpus_per_machine = torch.cuda.device_count() + cfg.world_size = cfg.pmi_world_size * cfg.gpus_per_machine + + if cfg.world_size == 1: + worker(0, cfg, cfg_update) + else: + mp.spawn(worker, nprocs=cfg.gpus_per_machine, args=(cfg, cfg_update)) + return cfg + + +def make_masked_images(imgs, masks): + masked_imgs = [] + for i, mask in enumerate(masks): + # concatenation + masked_imgs.append(torch.cat([imgs[i] * (1 - mask), (1 - mask)], dim=1)) + return torch.stack(masked_imgs, dim=0) + +def load_video_frames(ref_image_path, pose_file_path, train_trans, vit_transforms, train_trans_pose, max_frames=32, frame_interval = 1, resolution=[512, 768], get_first_frame=True, vit_resolution=[224, 224]): + + for _ in range(5): + try: + dwpose_all = {} + frames_all = {} + for ii_index in sorted(os.listdir(pose_file_path)): + if ii_index != "ref_pose.jpg": + dwpose_all[ii_index] = Image.open(pose_file_path+"/"+ii_index) + frames_all[ii_index] = Image.fromarray(cv2.cvtColor(cv2.imread(ref_image_path),cv2.COLOR_BGR2RGB)) + # frames_all[ii_index] = Image.open(ref_image_path) + + pose_ref = Image.open(os.path.join(pose_file_path, "ref_pose.jpg")) + first_eq_ref = False + + # sample max_frames poses for video generation + stride = frame_interval + _total_frame_num = len(frames_all) + if max_frames == "None": + max_frames = (_total_frame_num-1)//frame_interval + 1 + cover_frame_num = (stride * (max_frames-1)+1) + if _total_frame_num < cover_frame_num: + print('_total_frame_num is smaller than cover_frame_num, the sampled frame interval is changed') + start_frame = 0 # we set start_frame = 0 because the pose alignment is performed on the first frame + end_frame = _total_frame_num + stride = max((_total_frame_num-1//(max_frames-1)),1) + end_frame = stride*max_frames + else: + start_frame = 0 # we set start_frame = 0 because the pose alignment is performed on the first frame + end_frame = start_frame + cover_frame_num + + frame_list = [] + dwpose_list = [] + random_ref_frame = frames_all[list(frames_all.keys())[0]] + if random_ref_frame.mode != 'RGB': + random_ref_frame = random_ref_frame.convert('RGB') + random_ref_dwpose = pose_ref + if random_ref_dwpose.mode != 'RGB': + random_ref_dwpose = random_ref_dwpose.convert('RGB') + for i_index in range(start_frame, end_frame, stride): + if i_index == start_frame and first_eq_ref: + i_key = list(frames_all.keys())[i_index] + i_frame = frames_all[i_key] + + if i_frame.mode != 'RGB': + i_frame = i_frame.convert('RGB') + i_dwpose = frames_pose_ref + if i_dwpose.mode != 'RGB': + i_dwpose = i_dwpose.convert('RGB') + frame_list.append(i_frame) + dwpose_list.append(i_dwpose) + else: + # added + if first_eq_ref: + i_index = i_index - stride + + i_key = list(frames_all.keys())[i_index] + i_frame = frames_all[i_key] + if i_frame.mode != 'RGB': + i_frame = i_frame.convert('RGB') + i_dwpose = dwpose_all[i_key] + if i_dwpose.mode != 'RGB': + i_dwpose = i_dwpose.convert('RGB') + frame_list.append(i_frame) + dwpose_list.append(i_dwpose) + have_frames = len(frame_list)>0 + middle_indix = 0 + if have_frames: + ref_frame = frame_list[middle_indix] + vit_frame = vit_transforms(ref_frame) + random_ref_frame_tmp = train_trans_pose(random_ref_frame) + random_ref_dwpose_tmp = train_trans_pose(random_ref_dwpose) + misc_data_tmp = torch.stack([train_trans_pose(ss) for ss in frame_list], dim=0) + video_data_tmp = torch.stack([train_trans(ss) for ss in frame_list], dim=0) + dwpose_data_tmp = torch.stack([train_trans_pose(ss) for ss in dwpose_list], dim=0) + + video_data = torch.zeros(max_frames, 3, resolution[1], resolution[0]) + dwpose_data = torch.zeros(max_frames, 3, resolution[1], resolution[0]) + misc_data = torch.zeros(max_frames, 3, resolution[1], resolution[0]) + random_ref_frame_data = torch.zeros(max_frames, 3, resolution[1], resolution[0]) # [32, 3, 512, 768] + random_ref_dwpose_data = torch.zeros(max_frames, 3, resolution[1], resolution[0]) + if have_frames: + video_data[:len(frame_list), ...] = video_data_tmp + misc_data[:len(frame_list), ...] = misc_data_tmp + dwpose_data[:len(frame_list), ...] = dwpose_data_tmp + random_ref_frame_data[:,...] = random_ref_frame_tmp + random_ref_dwpose_data[:,...] = random_ref_dwpose_tmp + + break + + except Exception as e: + logging.info('{} read video frame failed with error: {}'.format(pose_file_path, e)) + continue + + return vit_frame, video_data, misc_data, dwpose_data, random_ref_frame_data, random_ref_dwpose_data, max_frames + + + +def worker(gpu, cfg, cfg_update): + ''' + Inference worker for each gpu + ''' + for k, v in cfg_update.items(): + if isinstance(v, dict) and k in cfg: + cfg[k].update(v) + else: + cfg[k] = v + + cfg.gpu = gpu + cfg.seed = int(cfg.seed) + cfg.rank = cfg.pmi_rank * cfg.gpus_per_machine + gpu + setup_seed(cfg.seed + cfg.rank) + + if not cfg.debug: + torch.cuda.set_device(gpu) + torch.backends.cudnn.benchmark = True + if hasattr(cfg, "CPU_CLIP_VAE") and cfg.CPU_CLIP_VAE: + torch.backends.cudnn.benchmark = False + dist.init_process_group(backend='nccl', world_size=cfg.world_size, rank=cfg.rank) + + # [Log] Save logging and make log dir + log_dir = generalized_all_gather(cfg.log_dir)[0] + inf_name = osp.basename(cfg.cfg_file).split('.')[0] + test_model = osp.basename(cfg.test_model).split('.')[0].split('_')[-1] + + cfg.log_dir = osp.join(cfg.log_dir, '%s' % (inf_name)) + os.makedirs(cfg.log_dir, exist_ok=True) + log_file = osp.join(cfg.log_dir, 'log_%02d.txt' % (cfg.rank)) + cfg.log_file = log_file + reload(logging) + logging.basicConfig( + level=logging.INFO, + format='[%(asctime)s] %(levelname)s: %(message)s', + handlers=[ + logging.FileHandler(filename=log_file), + logging.StreamHandler(stream=sys.stdout)]) + logging.info(cfg) + logging.info(f"Running UniAnimate inference on gpu {gpu}") + + # [Diffusion] + diffusion = DIFFUSION.build(cfg.Diffusion) + + # [Data] Data Transform + train_trans = data.Compose([ + data.Resize(cfg.resolution), + data.ToTensor(), + data.Normalize(mean=cfg.mean, std=cfg.std) + ]) + + train_trans_pose = data.Compose([ + data.Resize(cfg.resolution), + data.ToTensor(), + ] + ) + + vit_transforms = T.Compose([ + data.Resize(cfg.vit_resolution), + T.ToTensor(), + T.Normalize(mean=cfg.vit_mean, std=cfg.vit_std)]) + + # [Model] embedder + clip_encoder = EMBEDDER.build(cfg.embedder) + clip_encoder.model.to(gpu) + with torch.no_grad(): + _, _, zero_y = clip_encoder(text="") + + + # [Model] auotoencoder + autoencoder = AUTO_ENCODER.build(cfg.auto_encoder) + autoencoder.eval() # freeze + for param in autoencoder.parameters(): + param.requires_grad = False + autoencoder.cuda() + + # [Model] UNet + if "config" in cfg.UNet: + cfg.UNet["config"] = cfg + cfg.UNet["zero_y"] = zero_y + model = MODEL.build(cfg.UNet) + state_dict = torch.load(cfg.test_model, map_location='cpu') + if 'state_dict' in state_dict: + state_dict = state_dict['state_dict'] + if 'step' in state_dict: + resume_step = state_dict['step'] + else: + resume_step = 0 + status = model.load_state_dict(state_dict, strict=True) + logging.info('Load model from {} with status {}'.format(cfg.test_model, status)) + model = model.to(gpu) + model.eval() + if hasattr(cfg, "CPU_CLIP_VAE") and cfg.CPU_CLIP_VAE: + model.to(torch.float16) + else: + model = DistributedDataParallel(model, device_ids=[gpu]) if not cfg.debug else model + torch.cuda.empty_cache() + + + + test_list = cfg.test_list_path + num_videos = len(test_list) + logging.info(f'There are {num_videos} videos. with {cfg.round} times') + test_list = [item for _ in range(cfg.round) for item in test_list] + + for idx, file_path in enumerate(test_list): + cfg.frame_interval, ref_image_key, pose_seq_key = file_path[0], file_path[1], file_path[2] + + manual_seed = int(cfg.seed + cfg.rank + idx//num_videos) + setup_seed(manual_seed) + logging.info(f"[{idx}]/[{len(test_list)}] Begin to sample {ref_image_key}, pose sequence from {pose_seq_key} init seed {manual_seed} ...") + + + vit_frame, video_data, misc_data, dwpose_data, random_ref_frame_data, random_ref_dwpose_data, max_frames = load_video_frames(ref_image_key, pose_seq_key, train_trans, vit_transforms, train_trans_pose, max_frames=cfg.max_frames, frame_interval =cfg.frame_interval, resolution=cfg.resolution) + cfg.max_frames_new = max_frames + misc_data = misc_data.unsqueeze(0).to(gpu) + vit_frame = vit_frame.unsqueeze(0).to(gpu) + dwpose_data = dwpose_data.unsqueeze(0).to(gpu) + random_ref_frame_data = random_ref_frame_data.unsqueeze(0).to(gpu) + random_ref_dwpose_data = random_ref_dwpose_data.unsqueeze(0).to(gpu) + + ### save for visualization + misc_backups = copy(misc_data) + frames_num = misc_data.shape[1] + misc_backups = rearrange(misc_backups, 'b f c h w -> b c f h w') + mv_data_video = [] + + + ### local image (first frame) + image_local = [] + if 'local_image' in cfg.video_compositions: + frames_num = misc_data.shape[1] + bs_vd_local = misc_data.shape[0] + image_local = misc_data[:,:1].clone().repeat(1,frames_num,1,1,1) + image_local_clone = rearrange(image_local, 'b f c h w -> b c f h w', b = bs_vd_local) + image_local = rearrange(image_local, 'b f c h w -> b c f h w', b = bs_vd_local) + if hasattr(cfg, "latent_local_image") and cfg.latent_local_image: + with torch.no_grad(): + temporal_length = frames_num + encoder_posterior = autoencoder.encode(video_data[:,0]) + local_image_data = get_first_stage_encoding(encoder_posterior).detach() + image_local = local_image_data.unsqueeze(1).repeat(1,temporal_length,1,1,1) # [10, 16, 4, 64, 40] + + + + ### encode the video_data + bs_vd = misc_data.shape[0] + misc_data = rearrange(misc_data, 'b f c h w -> (b f) c h w') + misc_data_list = torch.chunk(misc_data, misc_data.shape[0]//cfg.chunk_size,dim=0) + + + with torch.no_grad(): + + random_ref_frame = [] + if 'randomref' in cfg.video_compositions: + random_ref_frame_clone = rearrange(random_ref_frame_data, 'b f c h w -> b c f h w') + if hasattr(cfg, "latent_random_ref") and cfg.latent_random_ref: + + temporal_length = random_ref_frame_data.shape[1] + encoder_posterior = autoencoder.encode(random_ref_frame_data[:,0].sub(0.5).div_(0.5)) + random_ref_frame_data = get_first_stage_encoding(encoder_posterior).detach() + random_ref_frame_data = random_ref_frame_data.unsqueeze(1).repeat(1,temporal_length,1,1,1) # [10, 16, 4, 64, 40] + + random_ref_frame = rearrange(random_ref_frame_data, 'b f c h w -> b c f h w') + + + if 'dwpose' in cfg.video_compositions: + bs_vd_local = dwpose_data.shape[0] + dwpose_data_clone = rearrange(dwpose_data.clone(), 'b f c h w -> b c f h w', b = bs_vd_local) + if 'randomref_pose' in cfg.video_compositions: + dwpose_data = torch.cat([random_ref_dwpose_data[:,:1], dwpose_data], dim=1) + dwpose_data = rearrange(dwpose_data, 'b f c h w -> b c f h w', b = bs_vd_local) + + + y_visual = [] + if 'image' in cfg.video_compositions: + with torch.no_grad(): + vit_frame = vit_frame.squeeze(1) + y_visual = clip_encoder.encode_image(vit_frame).unsqueeze(1) # [60, 1024] + y_visual0 = y_visual.clone() + + + with amp.autocast(enabled=True): + pynvml.nvmlInit() + handle=pynvml.nvmlDeviceGetHandleByIndex(0) + meminfo=pynvml.nvmlDeviceGetMemoryInfo(handle) + cur_seed = torch.initial_seed() + logging.info(f"Current seed {cur_seed} ..., cfg.max_frames_new: {cfg.max_frames_new} ....") + + noise = torch.randn([1, 4, cfg.max_frames_new, int(cfg.resolution[1]/cfg.scale), int(cfg.resolution[0]/cfg.scale)]) + noise = noise.to(gpu) + + # add a noise prior + noise = diffusion.q_sample(random_ref_frame.clone(), getattr(cfg, "noise_prior_value", 939), noise=noise) + + if hasattr(cfg.Diffusion, "noise_strength"): + b, c, f, _, _= noise.shape + offset_noise = torch.randn(b, c, f, 1, 1, device=noise.device) + noise = noise + cfg.Diffusion.noise_strength * offset_noise + + # construct model inputs (CFG) + full_model_kwargs=[{ + 'y': None, + "local_image": None if len(image_local) == 0 else image_local[:], + 'image': None if len(y_visual) == 0 else y_visual0[:], + 'dwpose': None if len(dwpose_data) == 0 else dwpose_data[:], + 'randomref': None if len(random_ref_frame) == 0 else random_ref_frame[:], + }, + { + 'y': None, + "local_image": None, + 'image': None, + 'randomref': None, + 'dwpose': None, + }] + + # for visualization + full_model_kwargs_vis =[{ + 'y': None, + "local_image": None if len(image_local) == 0 else image_local_clone[:], + 'image': None, + 'dwpose': None if len(dwpose_data_clone) == 0 else dwpose_data_clone[:], + 'randomref': None if len(random_ref_frame) == 0 else random_ref_frame_clone[:, :3], + }, + { + 'y': None, + "local_image": None, + 'image': None, + 'randomref': None, + 'dwpose': None, + }] + + + partial_keys = [ + ['image', 'randomref', "dwpose"], + ] + if hasattr(cfg, "partial_keys") and cfg.partial_keys: + partial_keys = cfg.partial_keys + + for partial_keys_one in partial_keys: + model_kwargs_one = prepare_model_kwargs(partial_keys = partial_keys_one, + full_model_kwargs = full_model_kwargs, + use_fps_condition = cfg.use_fps_condition) + model_kwargs_one_vis = prepare_model_kwargs(partial_keys = partial_keys_one, + full_model_kwargs = full_model_kwargs_vis, + use_fps_condition = cfg.use_fps_condition) + noise_one = noise + + if hasattr(cfg, "CPU_CLIP_VAE") and cfg.CPU_CLIP_VAE: + clip_encoder.cpu() # add this line + autoencoder.cpu() # add this line + torch.cuda.empty_cache() # add this line + + video_data = diffusion.ddim_sample_loop( + noise=noise_one, + context_size=cfg.context_size, + context_stride=cfg.context_stride, + context_overlap=cfg.context_overlap, + model=model.eval(), + model_kwargs=model_kwargs_one, + guide_scale=cfg.guide_scale, + ddim_timesteps=cfg.ddim_timesteps, + eta=0.0, + context_batch_size=getattr(cfg, "context_batch_size", 1) + ) + + if hasattr(cfg, "CPU_CLIP_VAE") and cfg.CPU_CLIP_VAE: + # if run forward of autoencoder or clip_encoder second times, load them again + clip_encoder.cuda() + autoencoder.cuda() + + + video_data = 1. / cfg.scale_factor * video_data # [1, 4, h, w] + video_data = rearrange(video_data, 'b c f h w -> (b f) c h w') + chunk_size = min(cfg.decoder_bs, video_data.shape[0]) + video_data_list = torch.chunk(video_data, video_data.shape[0]//chunk_size, dim=0) + decode_data = [] + for vd_data in video_data_list: + gen_frames = autoencoder.decode(vd_data) + decode_data.append(gen_frames) + video_data = torch.cat(decode_data, dim=0) + video_data = rearrange(video_data, '(b f) c h w -> b c f h w', b = cfg.batch_size).float() + + text_size = cfg.resolution[-1] + cap_name = re.sub(r'[^\w\s]', '', ref_image_key.split("/")[-1].split('.')[0]) # .replace(' ', '_') + name = f'seed_{cur_seed}' + for ii in partial_keys_one: + name = name + "_" + ii + file_name = f'rank_{cfg.world_size:02d}_{cfg.rank:02d}_{idx:02d}_{name}_{cap_name}_{cfg.resolution[1]}x{cfg.resolution[0]}.mp4' + local_path = os.path.join(cfg.log_dir, f'{file_name}') + os.makedirs(os.path.dirname(local_path), exist_ok=True) + captions = "human" + del model_kwargs_one_vis[0][list(model_kwargs_one_vis[0].keys())[0]] + del model_kwargs_one_vis[1][list(model_kwargs_one_vis[1].keys())[0]] + + save_video_multiple_conditions_not_gif_horizontal_3col(local_path, video_data.cpu(), model_kwargs_one_vis, misc_backups, + cfg.mean, cfg.std, nrow=1, save_fps=cfg.save_fps) + + # try: + # save_t2vhigen_video_safe(local_path, video_data.cpu(), captions, cfg.mean, cfg.std, text_size) + # logging.info('Save video to dir %s:' % (local_path)) + # except Exception as e: + # logging.info(f'Step: save text or video error with {e}') + + logging.info('Congratulations! The inference is completed!') + # synchronize to finish some processes + if not cfg.debug: + torch.cuda.synchronize() + dist.barrier() + +def prepare_model_kwargs(partial_keys, full_model_kwargs, use_fps_condition=False): + + if use_fps_condition is True: + partial_keys.append('fps') + + partial_model_kwargs = [{}, {}] + for partial_key in partial_keys: + partial_model_kwargs[0][partial_key] = full_model_kwargs[0][partial_key] + partial_model_kwargs[1][partial_key] = full_model_kwargs[1][partial_key] + + return partial_model_kwargs diff --git a/animation/UniAnimate/tools/modules/__init__.py b/animation/UniAnimate/tools/modules/__init__.py new file mode 100644 index 0000000..8939090 --- /dev/null +++ b/animation/UniAnimate/tools/modules/__init__.py @@ -0,0 +1,7 @@ +from .clip_embedder import FrozenOpenCLIPEmbedder +from .autoencoder import DiagonalGaussianDistribution, AutoencoderKL +from .clip_embedder import * +from .autoencoder import * +from .unet import * +from .diffusions import * +from .embedding_manager import * diff --git a/animation/UniAnimate/tools/modules/autoencoder.py b/animation/UniAnimate/tools/modules/autoencoder.py new file mode 100644 index 0000000..637fd89 --- /dev/null +++ b/animation/UniAnimate/tools/modules/autoencoder.py @@ -0,0 +1,690 @@ +import torch +import logging +import collections +import numpy as np +import torch.nn as nn +import torch.nn.functional as F + +from utils.registry_class import AUTO_ENCODER,DISTRIBUTION + + +def nonlinearity(x): + # swish + return x*torch.sigmoid(x) + +def Normalize(in_channels, num_groups=32): + return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) + + +@torch.no_grad() +def get_first_stage_encoding(encoder_posterior, scale_factor=0.18215): + if isinstance(encoder_posterior, DiagonalGaussianDistribution): + z = encoder_posterior.sample() + elif isinstance(encoder_posterior, torch.Tensor): + z = encoder_posterior + else: + raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented") + return scale_factor * z + + +@AUTO_ENCODER.register_class() +class AutoencoderKL(nn.Module): + def __init__(self, + ddconfig, + embed_dim, + pretrained=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + ema_decay=None, + learn_logvar=False, + use_vid_decoder=False, + **kwargs): + super().__init__() + self.learn_logvar = learn_logvar + self.image_key = image_key + self.encoder = Encoder(**ddconfig) + self.decoder = Decoder(**ddconfig) + assert ddconfig["double_z"] + self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1) + self.embed_dim = embed_dim + if colorize_nlabels is not None: + assert type(colorize_nlabels)==int + self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1)) + if monitor is not None: + self.monitor = monitor + + self.use_ema = ema_decay is not None + + if pretrained is not None: + self.init_from_ckpt(pretrained, ignore_keys=ignore_keys) + + def init_from_ckpt(self, path, ignore_keys=list()): + sd = torch.load(path, map_location="cpu")["state_dict"] + keys = list(sd.keys()) + sd_new = collections.OrderedDict() + for k in keys: + if k.find('first_stage_model') >= 0: + k_new = k.split('first_stage_model.')[-1] + sd_new[k_new] = sd[k] + self.load_state_dict(sd_new, strict=True) + logging.info(f"Restored from {path}") + + def on_train_batch_end(self, *args, **kwargs): + if self.use_ema: + self.model_ema(self) + + def encode(self, x): + h = self.encoder(x) + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + return posterior + + def encode_firsr_stage(self, x, scale_factor=1.0): + h = self.encoder(x) + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + z = get_first_stage_encoding(posterior, scale_factor) + return z + + def encode_ms(self, x): + hs = self.encoder(x, True) + h = hs[-1] + moments = self.quant_conv(h) + posterior = DiagonalGaussianDistribution(moments) + hs[-1] = h + return hs + + def decode(self, z, **kwargs): + z = self.post_quant_conv(z) + dec = self.decoder(z, **kwargs) + return dec + + + def forward(self, input, sample_posterior=True): + posterior = self.encode(input) + if sample_posterior: + z = posterior.sample() + else: + z = posterior.mode() + dec = self.decode(z) + return dec, posterior + + def get_input(self, batch, k): + x = batch[k] + if len(x.shape) == 3: + x = x[..., None] + x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float() + return x + + def get_last_layer(self): + return self.decoder.conv_out.weight + + @torch.no_grad() + def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs): + log = dict() + x = self.get_input(batch, self.image_key) + x = x.to(self.device) + if not only_inputs: + xrec, posterior = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec.shape[1] > 3 + x = self.to_rgb(x) + xrec = self.to_rgb(xrec) + log["samples"] = self.decode(torch.randn_like(posterior.sample())) + log["reconstructions"] = xrec + if log_ema or self.use_ema: + with self.ema_scope(): + xrec_ema, posterior_ema = self(x) + if x.shape[1] > 3: + # colorize with random projection + assert xrec_ema.shape[1] > 3 + xrec_ema = self.to_rgb(xrec_ema) + log["samples_ema"] = self.decode(torch.randn_like(posterior_ema.sample())) + log["reconstructions_ema"] = xrec_ema + log["inputs"] = x + return log + + def to_rgb(self, x): + assert self.image_key == "segmentation" + if not hasattr(self, "colorize"): + self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x)) + x = F.conv2d(x, weight=self.colorize) + x = 2.*(x-x.min())/(x.max()-x.min()) - 1. + return x + + +@AUTO_ENCODER.register_class() +class AutoencoderVideo(AutoencoderKL): + def __init__(self, + ddconfig, + embed_dim, + pretrained=None, + ignore_keys=[], + image_key="image", + colorize_nlabels=None, + monitor=None, + ema_decay=None, + use_vid_decoder=True, + learn_logvar=False, + **kwargs): + use_vid_decoder = True + super().__init__(ddconfig, embed_dim, pretrained, ignore_keys, image_key, colorize_nlabels, monitor, ema_decay, learn_logvar, use_vid_decoder, **kwargs) + + def decode(self, z, **kwargs): + # z = self.post_quant_conv(z) + dec = self.decoder(z, **kwargs) + return dec + + def encode(self, x): + h = self.encoder(x) + # moments = self.quant_conv(h) + moments = h + posterior = DiagonalGaussianDistribution(moments) + return posterior + + +class IdentityFirstStage(torch.nn.Module): + def __init__(self, *args, vq_interface=False, **kwargs): + self.vq_interface = vq_interface + super().__init__() + + def encode(self, x, *args, **kwargs): + return x + + def decode(self, x, *args, **kwargs): + return x + + def quantize(self, x, *args, **kwargs): + if self.vq_interface: + return x, None, [None, None, None] + return x + + def forward(self, x, *args, **kwargs): + return x + + + +@DISTRIBUTION.register_class() +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) + + def sample(self): + x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.]) + else: + if other is None: + return 0.5 * torch.sum(torch.pow(self.mean, 2) + + self.var - 1.0 - self.logvar, + dim=[1, 2, 3]) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var - 1.0 - self.logvar + other.logvar, + dim=[1, 2, 3]) + + def nll(self, sample, dims=[1,2,3]): + if self.deterministic: + return torch.Tensor([0.]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims) + + def mode(self): + return self.mean + + +# -------------------------------modules-------------------------------- + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=2, + padding=0) + + def forward(self, x): + if self.with_conv: + pad = (0,1,0,1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, + dropout, temb_channels=512): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, + out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + else: + self.nin_shortcut = torch.nn.Conv2d(in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x+h + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = q.reshape(b,c,h*w) + q = q.permute(0,2,1) # b,hw,c + k = k.reshape(b,c,h*w) # b,c,hw + w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b,c,h*w) + w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q) + h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] + h_ = h_.reshape(b,c,h,w) + + h_ = self.proj_out(h_) + + return x+h_ + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b,c,h,w = q.shape + q = q.reshape(b,c,h*w) + q = q.permute(0,2,1) # b,hw,c + k = k.reshape(b,c,h*w) # b,c,hw + w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j] + w_ = w_ * (int(c)**(-0.5)) + w_ = torch.nn.functional.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b,c,h*w) + w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q) + h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j] + h_ = h_.reshape(b,c,h,w) + + h_ = self.proj_out(h_) + + return x+h_ + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=2, + padding=0) + + def forward(self, x): + if self.with_conv: + pad = (0,1,0,1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + +class Encoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", + **ignore_kwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = torch.nn.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(AttnBlock(block_in)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + 2*z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x, return_feat=False): + # timestep embedding + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + if return_feat: + hs[-1] = h + return hs + else: + return h + + +class Decoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, + attn_type="vanilla", **ignorekwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,)+tuple(ch_mult) + block_in = ch*ch_mult[self.num_resolutions-1] + curr_res = resolution // 2**(self.num_resolutions-1) + self.z_shape = (1,z_channels, curr_res, curr_res) + # logging.info("Working with z of shape {} = {} dimensions.".format(self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = torch.nn.Conv2d(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = AttnBlock(block_in) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(AttnBlock(block_in)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z, **kwargs): + #assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + if self.tanh_out: + h = torch.tanh(h) + return h + + + + diff --git a/animation/UniAnimate/tools/modules/clip_embedder.py b/animation/UniAnimate/tools/modules/clip_embedder.py new file mode 100644 index 0000000..fda4aa0 --- /dev/null +++ b/animation/UniAnimate/tools/modules/clip_embedder.py @@ -0,0 +1,212 @@ +import os +import torch +import logging +import open_clip +import numpy as np +import torch.nn as nn +import torchvision.transforms as T + +from utils.registry_class import EMBEDDER + + +@EMBEDDER.register_class() +class FrozenOpenCLIPEmbedder(nn.Module): + """ + Uses the OpenCLIP transformer encoder for text + """ + LAYERS = [ + #"pooled", + "last", + "penultimate" + ] + def __init__(self, pretrained, arch="ViT-H-14", device="cuda", max_length=77, + freeze=True, layer="last"): + super().__init__() + assert layer in self.LAYERS + model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=pretrained) + del model.visual + self.model = model + + self.device = device + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + if self.layer == "last": + self.layer_idx = 0 + elif self.layer == "penultimate": + self.layer_idx = 1 + else: + raise NotImplementedError() + + def freeze(self): + self.model = self.model.eval() + for param in self.parameters(): + param.requires_grad = False + + def forward(self, text): + tokens = open_clip.tokenize(text) + z = self.encode_with_transformer(tokens.to(self.device)) + return z + + def encode_with_transformer(self, text): + x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model] + x = x + self.model.positional_embedding + x = x.permute(1, 0, 2) # NLD -> LND + x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.model.ln_final(x) + return x + + def text_transformer_forward(self, x: torch.Tensor, attn_mask = None): + for i, r in enumerate(self.model.transformer.resblocks): + if i == len(self.model.transformer.resblocks) - self.layer_idx: + break + if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint(r, x, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + def encode(self, text): + return self(text) + + +@EMBEDDER.register_class() +class FrozenOpenCLIPVisualEmbedder(nn.Module): + """ + Uses the OpenCLIP transformer encoder for text + """ + LAYERS = [ + #"pooled", + "last", + "penultimate" + ] + def __init__(self, pretrained, vit_resolution=(224, 224), arch="ViT-H-14", device="cuda", max_length=77, + freeze=True, layer="last"): + super().__init__() + assert layer in self.LAYERS + model, _, preprocess = open_clip.create_model_and_transforms( + arch, device=torch.device('cpu'), pretrained=pretrained) + + del model.transformer + self.model = model + data_white = np.ones((vit_resolution[0], vit_resolution[1], 3), dtype=np.uint8)*255 + self.white_image = preprocess(T.ToPILImage()(data_white)).unsqueeze(0) + + self.device = device + self.max_length = max_length # 77 + if freeze: + self.freeze() + self.layer = layer # 'penultimate' + if self.layer == "last": + self.layer_idx = 0 + elif self.layer == "penultimate": + self.layer_idx = 1 + else: + raise NotImplementedError() + + def freeze(self): + self.model = self.model.eval() + for param in self.parameters(): + param.requires_grad = False + + def forward(self, image): + # tokens = open_clip.tokenize(text) + z = self.model.encode_image(image.to(self.device)) + return z + + def encode_with_transformer(self, text): + x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model] + x = x + self.model.positional_embedding + x = x.permute(1, 0, 2) # NLD -> LND + x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.model.ln_final(x) + + return x + + def text_transformer_forward(self, x: torch.Tensor, attn_mask = None): + for i, r in enumerate(self.model.transformer.resblocks): + if i == len(self.model.transformer.resblocks) - self.layer_idx: + break + if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint(r, x, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + def encode(self, text): + return self(text) + + + +@EMBEDDER.register_class() +class FrozenOpenCLIPTextVisualEmbedder(nn.Module): + """ + Uses the OpenCLIP transformer encoder for text + """ + LAYERS = [ + #"pooled", + "last", + "penultimate" + ] + def __init__(self, pretrained, arch="ViT-H-14", device="cuda", max_length=77, + freeze=True, layer="last", **kwargs): + super().__init__() + assert layer in self.LAYERS + model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=pretrained) + self.model = model + + self.device = device + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + if self.layer == "last": + self.layer_idx = 0 + elif self.layer == "penultimate": + self.layer_idx = 1 + else: + raise NotImplementedError() + + def freeze(self): + self.model = self.model.eval() + for param in self.parameters(): + param.requires_grad = False + + + def forward(self, image=None, text=None): + + xi = self.model.encode_image(image.to(self.device)) if image is not None else None + tokens = open_clip.tokenize(text) + xt, x = self.encode_with_transformer(tokens.to(self.device)) + return xi, xt, x + + def encode_with_transformer(self, text): + x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model] + x = x + self.model.positional_embedding + x = x.permute(1, 0, 2) # NLD -> LND + x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.model.ln_final(x) + xt = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.model.text_projection + return xt, x + + + def encode_image(self, image): + return self.model.visual(image) + + def text_transformer_forward(self, x: torch.Tensor, attn_mask = None): + for i, r in enumerate(self.model.transformer.resblocks): + if i == len(self.model.transformer.resblocks) - self.layer_idx: + break + if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint(r, x, attn_mask) + else: + x = r(x, attn_mask=attn_mask) + return x + + def encode(self, text): + + return self(text) diff --git a/animation/UniAnimate/tools/modules/config.py b/animation/UniAnimate/tools/modules/config.py new file mode 100644 index 0000000..04b9516 --- /dev/null +++ b/animation/UniAnimate/tools/modules/config.py @@ -0,0 +1,206 @@ +import torch +import logging +import os.path as osp +from datetime import datetime +from easydict import EasyDict +import os + +cfg = EasyDict(__name__='Config: VideoLDM Decoder') + +# -------------------------------distributed training-------------------------- +pmi_world_size = int(os.getenv('WORLD_SIZE', 1)) +gpus_per_machine = torch.cuda.device_count() +world_size = pmi_world_size * gpus_per_machine +# ----------------------------------------------------------------------------- + + +# ---------------------------Dataset Parameter--------------------------------- +cfg.mean = [0.5, 0.5, 0.5] +cfg.std = [0.5, 0.5, 0.5] +cfg.max_words = 1000 +cfg.num_workers = 8 +cfg.prefetch_factor = 2 + +# PlaceHolder +cfg.resolution = [448, 256] +cfg.vit_out_dim = 1024 +cfg.vit_resolution = 336 +cfg.depth_clamp = 10.0 +cfg.misc_size = 384 +cfg.depth_std = 20.0 + +cfg.save_fps = 8 + +cfg.frame_lens = [32, 32, 32, 1] +cfg.sample_fps = [4, ] +cfg.vid_dataset = { + 'type': 'VideoBaseDataset', + 'data_list': [], + 'max_words': cfg.max_words, + 'resolution': cfg.resolution} +cfg.img_dataset = { + 'type': 'ImageBaseDataset', + 'data_list': ['laion_400m',], + 'max_words': cfg.max_words, + 'resolution': cfg.resolution} + +cfg.batch_sizes = { + str(1):256, + str(4):4, + str(8):4, + str(16):4} +# ----------------------------------------------------------------------------- + + +# ---------------------------Mode Parameters----------------------------------- +# Diffusion +cfg.Diffusion = { + 'type': 'DiffusionDDIM', + 'schedule': 'cosine', # cosine + 'schedule_param': { + 'num_timesteps': 1000, + 'cosine_s': 0.008, + 'zero_terminal_snr': True, + }, + 'mean_type': 'v', # [v, eps] + 'loss_type': 'mse', + 'var_type': 'fixed_small', + 'rescale_timesteps': False, + 'noise_strength': 0.1, + 'ddim_timesteps': 50 +} +cfg.ddim_timesteps = 50 # official: 250 +cfg.use_div_loss = False +# classifier-free guidance +cfg.p_zero = 0.9 +cfg.guide_scale = 3.0 + +# clip vision encoder +cfg.vit_mean = [0.48145466, 0.4578275, 0.40821073] +cfg.vit_std = [0.26862954, 0.26130258, 0.27577711] + +# sketch +cfg.sketch_mean = [0.485, 0.456, 0.406] +cfg.sketch_std = [0.229, 0.224, 0.225] +# cfg.misc_size = 256 +cfg.depth_std = 20.0 +cfg.depth_clamp = 10.0 +cfg.hist_sigma = 10.0 + +# Model +cfg.scale_factor = 0.18215 +cfg.use_checkpoint = True +cfg.use_sharded_ddp = False +cfg.use_fsdp = False +cfg.use_fp16 = True +cfg.temporal_attention = True + +cfg.UNet = { + 'type': 'UNetSD', + 'in_dim': 4, + 'dim': 320, + 'y_dim': cfg.vit_out_dim, + 'context_dim': 1024, + 'out_dim': 8, + 'dim_mult': [1, 2, 4, 4], + 'num_heads': 8, + 'head_dim': 64, + 'num_res_blocks': 2, + 'attn_scales': [1 / 1, 1 / 2, 1 / 4], + 'dropout': 0.1, + 'temporal_attention': cfg.temporal_attention, + 'temporal_attn_times': 1, + 'use_checkpoint': cfg.use_checkpoint, + 'use_fps_condition': False, + 'use_sim_mask': False +} + +# auotoencoder from stabel diffusion +cfg.guidances = [] +cfg.auto_encoder = { + 'type': 'AutoencoderKL', + 'ddconfig': { + 'double_z': True, + 'z_channels': 4, + 'resolution': 256, + 'in_channels': 3, + 'out_ch': 3, + 'ch': 128, + 'ch_mult': [1, 2, 4, 4], + 'num_res_blocks': 2, + 'attn_resolutions': [], + 'dropout': 0.0, + 'video_kernel_size': [3, 1, 1] + }, + 'embed_dim': 4, + 'pretrained': 'models/v2-1_512-ema-pruned.ckpt' +} +# clip embedder +cfg.embedder = { + 'type': 'FrozenOpenCLIPEmbedder', + 'layer': 'penultimate', + 'pretrained': 'models/open_clip_pytorch_model.bin' +} +# ----------------------------------------------------------------------------- + +# ---------------------------Training Settings--------------------------------- +# training and optimizer +cfg.ema_decay = 0.9999 +cfg.num_steps = 600000 +cfg.lr = 5e-5 +cfg.weight_decay = 0.0 +cfg.betas = (0.9, 0.999) +cfg.eps = 1.0e-8 +cfg.chunk_size = 16 +cfg.decoder_bs = 8 +cfg.alpha = 0.7 +cfg.save_ckp_interval = 1000 + +# scheduler +cfg.warmup_steps = 10 +cfg.decay_mode = 'cosine' + +# acceleration +cfg.use_ema = True +if world_size<2: + cfg.use_ema = False +cfg.load_from = None +# ----------------------------------------------------------------------------- + + +# ----------------------------Pretrain Settings--------------------------------- +cfg.Pretrain = { + 'type': 'pretrain_specific_strategies', + 'fix_weight': False, + 'grad_scale': 0.2, + 'resume_checkpoint': 'models/jiuniu_0267000.pth', + 'sd_keys_path': 'models/stable_diffusion_image_key_temporal_attention_x1.json', +} +# ----------------------------------------------------------------------------- + + +# -----------------------------Visual------------------------------------------- +# Visual videos +cfg.viz_interval = 1000 +cfg.visual_train = { + 'type': 'VisualTrainTextImageToVideo', +} +cfg.visual_inference = { + 'type': 'VisualGeneratedVideos', +} +cfg.inference_list_path = '' + +# logging +cfg.log_interval = 100 + +### Default log_dir +cfg.log_dir = 'outputs/' +# ----------------------------------------------------------------------------- + + +# ---------------------------Others-------------------------------------------- +# seed +cfg.seed = 8888 +cfg.negative_prompt = 'Distorted, discontinuous, Ugly, blurry, low resolution, motionless, static, disfigured, disconnected limbs, Ugly faces, incomplete arms' +# ----------------------------------------------------------------------------- + diff --git a/animation/UniAnimate/tools/modules/diffusions/__init__.py b/animation/UniAnimate/tools/modules/diffusions/__init__.py new file mode 100644 index 0000000..eb4b2c3 --- /dev/null +++ b/animation/UniAnimate/tools/modules/diffusions/__init__.py @@ -0,0 +1 @@ +from .diffusion_ddim import * diff --git a/animation/UniAnimate/tools/modules/diffusions/diffusion_ddim.py b/animation/UniAnimate/tools/modules/diffusions/diffusion_ddim.py new file mode 100644 index 0000000..507d71c --- /dev/null +++ b/animation/UniAnimate/tools/modules/diffusions/diffusion_ddim.py @@ -0,0 +1,1121 @@ +import torch +import math + +from utils.registry_class import DIFFUSION +from .schedules import beta_schedule, sigma_schedule +from .losses import kl_divergence, discretized_gaussian_log_likelihood +# from .dpm_solver import NoiseScheduleVP, model_wrapper_guided_diffusion, model_wrapper, DPM_Solver +from typing import Callable, List, Optional +import numpy as np + +def _i(tensor, t, x): + r"""Index tensor using t and format the output according to x. + """ + if tensor.device != x.device: + tensor = tensor.to(x.device) + shape = (x.size(0), ) + (1, ) * (x.ndim - 1) + return tensor[t].view(shape).to(x) + +@DIFFUSION.register_class() +class DiffusionDDIMSR(object): + def __init__(self, reverse_diffusion, forward_diffusion, **kwargs): + from .diffusion_gauss import GaussianDiffusion + self.reverse_diffusion = GaussianDiffusion(sigmas=sigma_schedule(reverse_diffusion.schedule, **reverse_diffusion.schedule_param), + prediction_type=reverse_diffusion.mean_type) + self.forward_diffusion = GaussianDiffusion(sigmas=sigma_schedule(forward_diffusion.schedule, **forward_diffusion.schedule_param), + prediction_type=forward_diffusion.mean_type) + + +@DIFFUSION.register_class() +class DiffusionDPM(object): + def __init__(self, forward_diffusion, **kwargs): + from .diffusion_gauss import GaussianDiffusion + self.forward_diffusion = GaussianDiffusion(sigmas=sigma_schedule(forward_diffusion.schedule, **forward_diffusion.schedule_param), + prediction_type=forward_diffusion.mean_type) + + +@DIFFUSION.register_class() +class DiffusionDDIM(object): + def __init__(self, + schedule='linear_sd', + schedule_param={}, + mean_type='eps', + var_type='learned_range', + loss_type='mse', + epsilon = 1e-12, + rescale_timesteps=False, + noise_strength=0.0, + **kwargs): + + assert mean_type in ['x0', 'x_{t-1}', 'eps', 'v'] + assert var_type in ['learned', 'learned_range', 'fixed_large', 'fixed_small'] + assert loss_type in ['mse', 'rescaled_mse', 'kl', 'rescaled_kl', 'l1', 'rescaled_l1','charbonnier'] + + betas = beta_schedule(schedule, **schedule_param) + assert min(betas) > 0 and max(betas) <= 1 + + if not isinstance(betas, torch.DoubleTensor): + betas = torch.tensor(betas, dtype=torch.float64) + + self.betas = betas + self.num_timesteps = len(betas) + self.mean_type = mean_type # eps + self.var_type = var_type # 'fixed_small' + self.loss_type = loss_type # mse + self.epsilon = epsilon # 1e-12 + self.rescale_timesteps = rescale_timesteps # False + self.noise_strength = noise_strength # 0.0 + + # alphas + alphas = 1 - self.betas + self.alphas_cumprod = torch.cumprod(alphas, dim=0) + self.alphas_cumprod_prev = torch.cat([alphas.new_ones([1]), self.alphas_cumprod[:-1]]) + self.alphas_cumprod_next = torch.cat([self.alphas_cumprod[1:], alphas.new_zeros([1])]) + + # q(x_t | x_{t-1}) + self.sqrt_alphas_cumprod = torch.sqrt(self.alphas_cumprod) + self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - self.alphas_cumprod) + self.log_one_minus_alphas_cumprod = torch.log(1.0 - self.alphas_cumprod) + self.sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / self.alphas_cumprod) + self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1.0 / self.alphas_cumprod - 1) + + # q(x_{t-1} | x_t, x_0) + self.posterior_variance = betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod) + self.posterior_log_variance_clipped = torch.log(self.posterior_variance.clamp(1e-20)) + self.posterior_mean_coef1 = betas * torch.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod) + self.posterior_mean_coef2 = (1.0 - self.alphas_cumprod_prev) * torch.sqrt(alphas) / (1.0 - self.alphas_cumprod) + + + def sample_loss(self, x0, noise=None): + if noise is None: + noise = torch.randn_like(x0) + if self.noise_strength > 0: + b, c, f, _, _= x0.shape + offset_noise = torch.randn(b, c, f, 1, 1, device=x0.device) + noise = noise + self.noise_strength * offset_noise + return noise + + + def q_sample(self, x0, t, noise=None): + r"""Sample from q(x_t | x_0). + """ + # noise = torch.randn_like(x0) if noise is None else noise + noise = self.sample_loss(x0, noise) + return _i(self.sqrt_alphas_cumprod, t, x0) * x0 + \ + _i(self.sqrt_one_minus_alphas_cumprod, t, x0) * noise + + def q_mean_variance(self, x0, t): + r"""Distribution of q(x_t | x_0). + """ + mu = _i(self.sqrt_alphas_cumprod, t, x0) * x0 + var = _i(1.0 - self.alphas_cumprod, t, x0) + log_var = _i(self.log_one_minus_alphas_cumprod, t, x0) + return mu, var, log_var + + def q_posterior_mean_variance(self, x0, xt, t): + r"""Distribution of q(x_{t-1} | x_t, x_0). + """ + mu = _i(self.posterior_mean_coef1, t, xt) * x0 + _i(self.posterior_mean_coef2, t, xt) * xt + var = _i(self.posterior_variance, t, xt) + log_var = _i(self.posterior_log_variance_clipped, t, xt) + return mu, var, log_var + + @torch.no_grad() + def p_sample(self, xt, t, model, model_kwargs={}, clamp=None, percentile=None, condition_fn=None, guide_scale=None): + r"""Sample from p(x_{t-1} | x_t). + - condition_fn: for classifier-based guidance (guided-diffusion). + - guide_scale: for classifier-free guidance (glide/dalle-2). + """ + # predict distribution of p(x_{t-1} | x_t) + mu, var, log_var, x0 = self.p_mean_variance(xt, t, model, model_kwargs, clamp, percentile, guide_scale) + + # random sample (with optional conditional function) + noise = torch.randn_like(xt) + mask = t.ne(0).float().view(-1, *((1, ) * (xt.ndim - 1))) # no noise when t == 0 + if condition_fn is not None: + grad = condition_fn(xt, self._scale_timesteps(t), **model_kwargs) + mu = mu.float() + var * grad.float() + xt_1 = mu + mask * torch.exp(0.5 * log_var) * noise + return xt_1, x0 + + @torch.no_grad() + def p_sample_loop(self, noise, model, model_kwargs={}, clamp=None, percentile=None, condition_fn=None, guide_scale=None): + r"""Sample from p(x_{t-1} | x_t) p(x_{t-2} | x_{t-1}) ... p(x_0 | x_1). + """ + # prepare input + b = noise.size(0) + xt = noise + + # diffusion process + for step in torch.arange(self.num_timesteps).flip(0): + t = torch.full((b, ), step, dtype=torch.long, device=xt.device) + xt, _ = self.p_sample(xt, t, model, model_kwargs, clamp, percentile, condition_fn, guide_scale) + return xt + + def p_mean_variance(self, xt, t, model, model_kwargs={}, clamp=None, percentile=None, guide_scale=None): + r"""Distribution of p(x_{t-1} | x_t). + """ + # predict distribution + if guide_scale is None: + out = model(xt, self._scale_timesteps(t), **model_kwargs) + else: + # classifier-free guidance + # (model_kwargs[0]: conditional kwargs; model_kwargs[1]: non-conditional kwargs) + assert isinstance(model_kwargs, list) and len(model_kwargs) == 2 + y_out = model(xt, self._scale_timesteps(t), **model_kwargs[0]) + u_out = model(xt, self._scale_timesteps(t), **model_kwargs[1]) + dim = y_out.size(1) if self.var_type.startswith('fixed') else y_out.size(1) // 2 + out = torch.cat([ + u_out[:, :dim] + guide_scale * (y_out[:, :dim] - u_out[:, :dim]), + y_out[:, dim:]], dim=1) # guide_scale=9.0 + + # compute variance + if self.var_type == 'learned': + out, log_var = out.chunk(2, dim=1) + var = torch.exp(log_var) + elif self.var_type == 'learned_range': + out, fraction = out.chunk(2, dim=1) + min_log_var = _i(self.posterior_log_variance_clipped, t, xt) + max_log_var = _i(torch.log(self.betas), t, xt) + fraction = (fraction + 1) / 2.0 + log_var = fraction * max_log_var + (1 - fraction) * min_log_var + var = torch.exp(log_var) + elif self.var_type == 'fixed_large': + var = _i(torch.cat([self.posterior_variance[1:2], self.betas[1:]]), t, xt) + log_var = torch.log(var) + elif self.var_type == 'fixed_small': + var = _i(self.posterior_variance, t, xt) + log_var = _i(self.posterior_log_variance_clipped, t, xt) + + # compute mean and x0 + if self.mean_type == 'x_{t-1}': + mu = out # x_{t-1} + x0 = _i(1.0 / self.posterior_mean_coef1, t, xt) * mu - \ + _i(self.posterior_mean_coef2 / self.posterior_mean_coef1, t, xt) * xt + elif self.mean_type == 'x0': + x0 = out + mu, _, _ = self.q_posterior_mean_variance(x0, xt, t) + elif self.mean_type == 'eps': + x0 = _i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) * out + mu, _, _ = self.q_posterior_mean_variance(x0, xt, t) + elif self.mean_type == 'v': + x0 = _i(self.sqrt_alphas_cumprod, t, xt) * xt - \ + _i(self.sqrt_one_minus_alphas_cumprod, t, xt) * out + mu, _, _ = self.q_posterior_mean_variance(x0, xt, t) + + # restrict the range of x0 + if percentile is not None: + assert percentile > 0 and percentile <= 1 # e.g., 0.995 + s = torch.quantile(x0.flatten(1).abs(), percentile, dim=1).clamp_(1.0).view(-1, 1, 1, 1) + x0 = torch.min(s, torch.max(-s, x0)) / s + elif clamp is not None: + x0 = x0.clamp(-clamp, clamp) + return mu, var, log_var, x0 + + @torch.no_grad() + def ddim_sample(self, xt, t, model, model_kwargs={}, clamp=None, percentile=None, condition_fn=None, guide_scale=None, ddim_timesteps=20, eta=0.0): + r"""Sample from p(x_{t-1} | x_t) using DDIM. + - condition_fn: for classifier-based guidance (guided-diffusion). + - guide_scale: for classifier-free guidance (glide/dalle-2). + """ + stride = self.num_timesteps // ddim_timesteps + + # predict distribution of p(x_{t-1} | x_t) + _, _, _, x0 = self.p_mean_variance(xt, t, model, model_kwargs, clamp, percentile, guide_scale) + if condition_fn is not None: + # x0 -> eps + alpha = _i(self.alphas_cumprod, t, xt) + eps = (_i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - x0) / \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) + eps = eps - (1 - alpha).sqrt() * condition_fn(xt, self._scale_timesteps(t), **model_kwargs) + + # eps -> x0 + x0 = _i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) * eps + + # derive variables + eps = (_i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - x0) / \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) + alphas = _i(self.alphas_cumprod, t, xt) + alphas_prev = _i(self.alphas_cumprod, (t - stride).clamp(0), xt) + sigmas = eta * torch.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) + + # random sample + noise = torch.randn_like(xt) + direction = torch.sqrt(1 - alphas_prev - sigmas ** 2) * eps + mask = t.ne(0).float().view(-1, *((1, ) * (xt.ndim - 1))) + xt_1 = torch.sqrt(alphas_prev) * x0 + direction + mask * sigmas * noise + return xt_1, x0 + + @torch.no_grad() + def ddim_sample_loop(self, noise, model, model_kwargs={}, clamp=None, percentile=None, condition_fn=None, guide_scale=None, ddim_timesteps=20, eta=0.0): + # prepare input + b = noise.size(0) + xt = noise + + # diffusion process (TODO: clamp is inaccurate! Consider replacing the stride by explicit prev/next steps) + steps = (1 + torch.arange(0, self.num_timesteps, self.num_timesteps // ddim_timesteps)).clamp(0, self.num_timesteps - 1).flip(0) + from tqdm import tqdm + for step in tqdm(steps): + t = torch.full((b, ), step, dtype=torch.long, device=xt.device) + xt, _ = self.ddim_sample(xt, t, model, model_kwargs, clamp, percentile, condition_fn, guide_scale, ddim_timesteps, eta) + # from ipdb import set_trace; set_trace() + return xt + + @torch.no_grad() + def ddim_reverse_sample(self, xt, t, model, model_kwargs={}, clamp=None, percentile=None, guide_scale=None, ddim_timesteps=20): + r"""Sample from p(x_{t+1} | x_t) using DDIM reverse ODE (deterministic). + """ + stride = self.num_timesteps // ddim_timesteps + + # predict distribution of p(x_{t-1} | x_t) + _, _, _, x0 = self.p_mean_variance(xt, t, model, model_kwargs, clamp, percentile, guide_scale) + + # derive variables + eps = (_i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - x0) / \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) + alphas_next = _i( + torch.cat([self.alphas_cumprod, self.alphas_cumprod.new_zeros([1])]), + (t + stride).clamp(0, self.num_timesteps), xt) + + # reverse sample + mu = torch.sqrt(alphas_next) * x0 + torch.sqrt(1 - alphas_next) * eps + return mu, x0 + + @torch.no_grad() + def ddim_reverse_sample_loop(self, x0, model, model_kwargs={}, clamp=None, percentile=None, guide_scale=None, ddim_timesteps=20): + # prepare input + b = x0.size(0) + xt = x0 + + # reconstruction steps + steps = torch.arange(0, self.num_timesteps, self.num_timesteps // ddim_timesteps) + for step in steps: + t = torch.full((b, ), step, dtype=torch.long, device=xt.device) + xt, _ = self.ddim_reverse_sample(xt, t, model, model_kwargs, clamp, percentile, guide_scale, ddim_timesteps) + return xt + + @torch.no_grad() + def plms_sample(self, xt, t, model, model_kwargs={}, clamp=None, percentile=None, condition_fn=None, guide_scale=None, plms_timesteps=20): + r"""Sample from p(x_{t-1} | x_t) using PLMS. + - condition_fn: for classifier-based guidance (guided-diffusion). + - guide_scale: for classifier-free guidance (glide/dalle-2). + """ + stride = self.num_timesteps // plms_timesteps + + # function for compute eps + def compute_eps(xt, t): + # predict distribution of p(x_{t-1} | x_t) + _, _, _, x0 = self.p_mean_variance(xt, t, model, model_kwargs, clamp, percentile, guide_scale) + + # condition + if condition_fn is not None: + # x0 -> eps + alpha = _i(self.alphas_cumprod, t, xt) + eps = (_i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - x0) / \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) + eps = eps - (1 - alpha).sqrt() * condition_fn(xt, self._scale_timesteps(t), **model_kwargs) + + # eps -> x0 + x0 = _i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) * eps + + # derive eps + eps = (_i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - x0) / \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) + return eps + + # function for compute x_0 and x_{t-1} + def compute_x0(eps, t): + # eps -> x0 + x0 = _i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) * eps + + # deterministic sample + alphas_prev = _i(self.alphas_cumprod, (t - stride).clamp(0), xt) + direction = torch.sqrt(1 - alphas_prev) * eps + mask = t.ne(0).float().view(-1, *((1, ) * (xt.ndim - 1))) + xt_1 = torch.sqrt(alphas_prev) * x0 + direction + return xt_1, x0 + + # PLMS sample + eps = compute_eps(xt, t) + if len(eps_cache) == 0: + # 2nd order pseudo improved Euler + xt_1, x0 = compute_x0(eps, t) + eps_next = compute_eps(xt_1, (t - stride).clamp(0)) + eps_prime = (eps + eps_next) / 2.0 + elif len(eps_cache) == 1: + # 2nd order pseudo linear multistep (Adams-Bashforth) + eps_prime = (3 * eps - eps_cache[-1]) / 2.0 + elif len(eps_cache) == 2: + # 3nd order pseudo linear multistep (Adams-Bashforth) + eps_prime = (23 * eps - 16 * eps_cache[-1] + 5 * eps_cache[-2]) / 12.0 + elif len(eps_cache) >= 3: + # 4nd order pseudo linear multistep (Adams-Bashforth) + eps_prime = (55 * eps - 59 * eps_cache[-1] + 37 * eps_cache[-2] - 9 * eps_cache[-3]) / 24.0 + xt_1, x0 = compute_x0(eps_prime, t) + return xt_1, x0, eps + + @torch.no_grad() + def plms_sample_loop(self, noise, model, model_kwargs={}, clamp=None, percentile=None, condition_fn=None, guide_scale=None, plms_timesteps=20): + # prepare input + b = noise.size(0) + xt = noise + + # diffusion process + steps = (1 + torch.arange(0, self.num_timesteps, self.num_timesteps // plms_timesteps)).clamp(0, self.num_timesteps - 1).flip(0) + eps_cache = [] + for step in steps: + # PLMS sampling step + t = torch.full((b, ), step, dtype=torch.long, device=xt.device) + xt, _, eps = self.plms_sample(xt, t, model, model_kwargs, clamp, percentile, condition_fn, guide_scale, plms_timesteps, eps_cache) + + # update eps cache + eps_cache.append(eps) + if len(eps_cache) >= 4: + eps_cache.pop(0) + return xt + + def loss(self, x0, t, model, model_kwargs={}, noise=None, weight = None, use_div_loss= False, loss_mask=None): + + # noise = torch.randn_like(x0) if noise is None else noise # [80, 4, 8, 32, 32] + noise = self.sample_loss(x0, noise) + + xt = self.q_sample(x0, t, noise=noise) + + # compute loss + if self.loss_type in ['kl', 'rescaled_kl']: + loss, _ = self.variational_lower_bound(x0, xt, t, model, model_kwargs) + if self.loss_type == 'rescaled_kl': + loss = loss * self.num_timesteps + elif self.loss_type in ['mse', 'rescaled_mse', 'l1', 'rescaled_l1']: # self.loss_type: mse + out = model(xt, self._scale_timesteps(t), **model_kwargs) + + # VLB for variation + loss_vlb = 0.0 + if self.var_type in ['learned', 'learned_range']: # self.var_type: 'fixed_small' + out, var = out.chunk(2, dim=1) + frozen = torch.cat([out.detach(), var], dim=1) # learn var without affecting the prediction of mean + loss_vlb, _ = self.variational_lower_bound(x0, xt, t, model=lambda *args, **kwargs: frozen) + if self.loss_type.startswith('rescaled_'): + loss_vlb = loss_vlb * self.num_timesteps / 1000.0 + + # MSE/L1 for x0/eps + # target = {'eps': noise, 'x0': x0, 'x_{t-1}': self.q_posterior_mean_variance(x0, xt, t)[0]}[self.mean_type] + target = { + 'eps': noise, + 'x0': x0, + 'x_{t-1}': self.q_posterior_mean_variance(x0, xt, t)[0], + 'v':_i(self.sqrt_alphas_cumprod, t, xt) * noise - _i(self.sqrt_one_minus_alphas_cumprod, t, xt) * x0}[self.mean_type] + if loss_mask is not None: + loss_mask = loss_mask[:, :, 0, ...].unsqueeze(2) # just use one channel (all channels are same) + loss_mask = loss_mask.permute(0, 2, 1, 3, 4) # b,c,f,h,w + # use masked diffusion + loss = (out * loss_mask - target * loss_mask).pow(1 if self.loss_type.endswith('l1') else 2).abs().flatten(1).mean(dim=1) + else: + loss = (out - target).pow(1 if self.loss_type.endswith('l1') else 2).abs().flatten(1).mean(dim=1) + if weight is not None: + loss = loss*weight + + # div loss + if use_div_loss and self.mean_type == 'eps' and x0.shape[2]>1: + + # derive x0 + x0_ = _i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) * out + + # # derive xt_1, set eta=0 as ddim + # alphas_prev = _i(self.alphas_cumprod, (t - 1).clamp(0), xt) + # direction = torch.sqrt(1 - alphas_prev) * out + # xt_1 = torch.sqrt(alphas_prev) * x0_ + direction + + # ncfhw, std on f + div_loss = 0.001/(x0_.std(dim=2).flatten(1).mean(dim=1)+1e-4) + # print(div_loss,loss) + loss = loss+div_loss + + # total loss + loss = loss + loss_vlb + elif self.loss_type in ['charbonnier']: + out = model(xt, self._scale_timesteps(t), **model_kwargs) + + # VLB for variation + loss_vlb = 0.0 + if self.var_type in ['learned', 'learned_range']: + out, var = out.chunk(2, dim=1) + frozen = torch.cat([out.detach(), var], dim=1) # learn var without affecting the prediction of mean + loss_vlb, _ = self.variational_lower_bound(x0, xt, t, model=lambda *args, **kwargs: frozen) + if self.loss_type.startswith('rescaled_'): + loss_vlb = loss_vlb * self.num_timesteps / 1000.0 + + # MSE/L1 for x0/eps + target = {'eps': noise, 'x0': x0, 'x_{t-1}': self.q_posterior_mean_variance(x0, xt, t)[0]}[self.mean_type] + loss = torch.sqrt((out - target)**2 + self.epsilon) + if weight is not None: + loss = loss*weight + loss = loss.flatten(1).mean(dim=1) + + # total loss + loss = loss + loss_vlb + return loss + + def variational_lower_bound(self, x0, xt, t, model, model_kwargs={}, clamp=None, percentile=None): + # compute groundtruth and predicted distributions + mu1, _, log_var1 = self.q_posterior_mean_variance(x0, xt, t) + mu2, _, log_var2, x0 = self.p_mean_variance(xt, t, model, model_kwargs, clamp, percentile) + + # compute KL loss + kl = kl_divergence(mu1, log_var1, mu2, log_var2) + kl = kl.flatten(1).mean(dim=1) / math.log(2.0) + + # compute discretized NLL loss (for p(x0 | x1) only) + nll = -discretized_gaussian_log_likelihood(x0, mean=mu2, log_scale=0.5 * log_var2) + nll = nll.flatten(1).mean(dim=1) / math.log(2.0) + + # NLL for p(x0 | x1) and KL otherwise + vlb = torch.where(t == 0, nll, kl) + return vlb, x0 + + @torch.no_grad() + def variational_lower_bound_loop(self, x0, model, model_kwargs={}, clamp=None, percentile=None): + r"""Compute the entire variational lower bound, measured in bits-per-dim. + """ + # prepare input and output + b = x0.size(0) + metrics = {'vlb': [], 'mse': [], 'x0_mse': []} + + # loop + for step in torch.arange(self.num_timesteps).flip(0): + # compute VLB + t = torch.full((b, ), step, dtype=torch.long, device=x0.device) + # noise = torch.randn_like(x0) + noise = self.sample_loss(x0) + xt = self.q_sample(x0, t, noise) + vlb, pred_x0 = self.variational_lower_bound(x0, xt, t, model, model_kwargs, clamp, percentile) + + # predict eps from x0 + eps = (_i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - x0) / \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) + + # collect metrics + metrics['vlb'].append(vlb) + metrics['x0_mse'].append((pred_x0 - x0).square().flatten(1).mean(dim=1)) + metrics['mse'].append((eps - noise).square().flatten(1).mean(dim=1)) + metrics = {k: torch.stack(v, dim=1) for k, v in metrics.items()} + + # compute the prior KL term for VLB, measured in bits-per-dim + mu, _, log_var = self.q_mean_variance(x0, t) + kl_prior = kl_divergence(mu, log_var, torch.zeros_like(mu), torch.zeros_like(log_var)) + kl_prior = kl_prior.flatten(1).mean(dim=1) / math.log(2.0) + + # update metrics + metrics['prior_bits_per_dim'] = kl_prior + metrics['total_bits_per_dim'] = metrics['vlb'].sum(dim=1) + kl_prior + return metrics + + def _scale_timesteps(self, t): + if self.rescale_timesteps: + return t.float() * 1000.0 / self.num_timesteps + return t + #return t.float() + + + + + + +@DIFFUSION.register_class() +class DiffusionDDIMLong(object): + def __init__(self, + schedule='linear_sd', + schedule_param={}, + mean_type='eps', + var_type='learned_range', + loss_type='mse', + epsilon = 1e-12, + rescale_timesteps=False, + noise_strength=0.0, + **kwargs): + + assert mean_type in ['x0', 'x_{t-1}', 'eps', 'v'] + assert var_type in ['learned', 'learned_range', 'fixed_large', 'fixed_small'] + assert loss_type in ['mse', 'rescaled_mse', 'kl', 'rescaled_kl', 'l1', 'rescaled_l1','charbonnier'] + + betas = beta_schedule(schedule, **schedule_param) + assert min(betas) > 0 and max(betas) <= 1 + + if not isinstance(betas, torch.DoubleTensor): + betas = torch.tensor(betas, dtype=torch.float64) + + self.betas = betas + self.num_timesteps = len(betas) + self.mean_type = mean_type # v + self.var_type = var_type # 'fixed_small' + self.loss_type = loss_type # mse + self.epsilon = epsilon # 1e-12 + self.rescale_timesteps = rescale_timesteps # False + self.noise_strength = noise_strength + + # alphas + alphas = 1 - self.betas + self.alphas_cumprod = torch.cumprod(alphas, dim=0) + self.alphas_cumprod_prev = torch.cat([alphas.new_ones([1]), self.alphas_cumprod[:-1]]) + self.alphas_cumprod_next = torch.cat([self.alphas_cumprod[1:], alphas.new_zeros([1])]) + + # q(x_t | x_{t-1}) + self.sqrt_alphas_cumprod = torch.sqrt(self.alphas_cumprod) + self.sqrt_one_minus_alphas_cumprod = torch.sqrt(1.0 - self.alphas_cumprod) + self.log_one_minus_alphas_cumprod = torch.log(1.0 - self.alphas_cumprod) + self.sqrt_recip_alphas_cumprod = torch.sqrt(1.0 / self.alphas_cumprod) + self.sqrt_recipm1_alphas_cumprod = torch.sqrt(1.0 / self.alphas_cumprod - 1) + + # q(x_{t-1} | x_t, x_0) + self.posterior_variance = betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod) + self.posterior_log_variance_clipped = torch.log(self.posterior_variance.clamp(1e-20)) + self.posterior_mean_coef1 = betas * torch.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod) + self.posterior_mean_coef2 = (1.0 - self.alphas_cumprod_prev) * torch.sqrt(alphas) / (1.0 - self.alphas_cumprod) + + + def sample_loss(self, x0, noise=None): + if noise is None: + noise = torch.randn_like(x0) + if self.noise_strength > 0: + b, c, f, _, _= x0.shape + offset_noise = torch.randn(b, c, f, 1, 1, device=x0.device) + noise = noise + self.noise_strength * offset_noise + return noise + + + def q_sample(self, x0, t, noise=None): + r"""Sample from q(x_t | x_0). + """ + # noise = torch.randn_like(x0) if noise is None else noise + noise = self.sample_loss(x0, noise) + return _i(self.sqrt_alphas_cumprod, t, x0) * x0 + \ + _i(self.sqrt_one_minus_alphas_cumprod, t, x0) * noise + + def q_mean_variance(self, x0, t): + r"""Distribution of q(x_t | x_0). + """ + mu = _i(self.sqrt_alphas_cumprod, t, x0) * x0 + var = _i(1.0 - self.alphas_cumprod, t, x0) + log_var = _i(self.log_one_minus_alphas_cumprod, t, x0) + return mu, var, log_var + + def q_posterior_mean_variance(self, x0, xt, t): + r"""Distribution of q(x_{t-1} | x_t, x_0). + """ + mu = _i(self.posterior_mean_coef1, t, xt) * x0 + _i(self.posterior_mean_coef2, t, xt) * xt + var = _i(self.posterior_variance, t, xt) + log_var = _i(self.posterior_log_variance_clipped, t, xt) + return mu, var, log_var + + @torch.no_grad() + def p_sample(self, xt, t, model, model_kwargs={}, clamp=None, percentile=None, condition_fn=None, guide_scale=None): + r"""Sample from p(x_{t-1} | x_t). + - condition_fn: for classifier-based guidance (guided-diffusion). + - guide_scale: for classifier-free guidance (glide/dalle-2). + """ + # predict distribution of p(x_{t-1} | x_t) + mu, var, log_var, x0 = self.p_mean_variance(xt, t, model, model_kwargs, clamp, percentile, guide_scale) + + # random sample (with optional conditional function) + noise = torch.randn_like(xt) + mask = t.ne(0).float().view(-1, *((1, ) * (xt.ndim - 1))) # no noise when t == 0 + if condition_fn is not None: + grad = condition_fn(xt, self._scale_timesteps(t), **model_kwargs) + mu = mu.float() + var * grad.float() + xt_1 = mu + mask * torch.exp(0.5 * log_var) * noise + return xt_1, x0 + + @torch.no_grad() + def p_sample_loop(self, noise, model, model_kwargs={}, clamp=None, percentile=None, condition_fn=None, guide_scale=None): + r"""Sample from p(x_{t-1} | x_t) p(x_{t-2} | x_{t-1}) ... p(x_0 | x_1). + """ + # prepare input + b = noise.size(0) + xt = noise + + # diffusion process + for step in torch.arange(self.num_timesteps).flip(0): + t = torch.full((b, ), step, dtype=torch.long, device=xt.device) + xt, _ = self.p_sample(xt, t, model, model_kwargs, clamp, percentile, condition_fn, guide_scale) + return xt + + def p_mean_variance(self, xt, t, model, model_kwargs={}, clamp=None, percentile=None, guide_scale=None, context_size=32, context_stride=1, context_overlap=4, context_batch_size=1): + r"""Distribution of p(x_{t-1} | x_t). + """ + noise = xt + context_queue = list( + context_scheduler( + 0, + 31, + noise.shape[2], + context_size=context_size, + context_stride=context_stride, + context_overlap=context_overlap, + ) + ) + context_step = min( + context_stride, int(np.ceil(np.log2(noise.shape[2] / context_size))) + 1 + ) + # replace the final segment to improve temporal consistency + num_frames = noise.shape[2] + context_queue[-1] = [ + e % num_frames + for e in range(num_frames - context_size * context_step, num_frames, context_step) + ] + + import math + # context_batch_size = 1 + num_context_batches = math.ceil(len(context_queue) / context_batch_size) + global_context = [] + for i in range(num_context_batches): + global_context.append( + context_queue[ + i * context_batch_size : (i + 1) * context_batch_size + ] + ) + noise_pred = torch.zeros_like(noise) + noise_pred_uncond = torch.zeros_like(noise) + counter = torch.zeros( + (1, 1, xt.shape[2], 1, 1), + device=xt.device, + dtype=xt.dtype, + ) + + for i_index, context in enumerate(global_context): + + + latent_model_input = torch.cat([xt[:, :, c] for c in context]) + bs_context = len(context) + + model_kwargs_new = [{ + 'y': None, + "local_image": None if not model_kwargs[0].__contains__('local_image') else torch.cat([model_kwargs[0]["local_image"][:, :, c] for c in context]), + 'image': None if not model_kwargs[0].__contains__('image') else model_kwargs[0]["image"].repeat(bs_context, 1, 1), + 'dwpose': None if not model_kwargs[0].__contains__('dwpose') else torch.cat([model_kwargs[0]["dwpose"][:, :, [0]+[ii+1 for ii in c]] for c in context]), + 'randomref': None if not model_kwargs[0].__contains__('randomref') else torch.cat([model_kwargs[0]["randomref"][:, :, c] for c in context]), + }, + { + 'y': None, + "local_image": None, + 'image': None, + 'randomref': None, + 'dwpose': None, + }] + + if guide_scale is None: + out = model(latent_model_input, self._scale_timesteps(t), **model_kwargs) + for j, c in enumerate(context): + noise_pred[:, :, c] = noise_pred[:, :, c] + out + counter[:, :, c] = counter[:, :, c] + 1 + else: + # classifier-free guidance + # (model_kwargs[0]: conditional kwargs; model_kwargs[1]: non-conditional kwargs) + # assert isinstance(model_kwargs, list) and len(model_kwargs) == 2 + y_out = model(latent_model_input, self._scale_timesteps(t).repeat(bs_context), **model_kwargs_new[0]) + u_out = model(latent_model_input, self._scale_timesteps(t).repeat(bs_context), **model_kwargs_new[1]) + dim = y_out.size(1) if self.var_type.startswith('fixed') else y_out.size(1) // 2 + for j, c in enumerate(context): + noise_pred[:, :, c] = noise_pred[:, :, c] + y_out[j:j+1] + noise_pred_uncond[:, :, c] = noise_pred_uncond[:, :, c] + u_out[j:j+1] + counter[:, :, c] = counter[:, :, c] + 1 + + noise_pred = noise_pred / counter + noise_pred_uncond = noise_pred_uncond / counter + out = torch.cat([ + noise_pred_uncond[:, :dim] + guide_scale * (noise_pred[:, :dim] - noise_pred_uncond[:, :dim]), + noise_pred[:, dim:]], dim=1) # guide_scale=2.5 + + + # compute variance + if self.var_type == 'learned': + out, log_var = out.chunk(2, dim=1) + var = torch.exp(log_var) + elif self.var_type == 'learned_range': + out, fraction = out.chunk(2, dim=1) + min_log_var = _i(self.posterior_log_variance_clipped, t, xt) + max_log_var = _i(torch.log(self.betas), t, xt) + fraction = (fraction + 1) / 2.0 + log_var = fraction * max_log_var + (1 - fraction) * min_log_var + var = torch.exp(log_var) + elif self.var_type == 'fixed_large': + var = _i(torch.cat([self.posterior_variance[1:2], self.betas[1:]]), t, xt) + log_var = torch.log(var) + elif self.var_type == 'fixed_small': + var = _i(self.posterior_variance, t, xt) + log_var = _i(self.posterior_log_variance_clipped, t, xt) + + # compute mean and x0 + if self.mean_type == 'x_{t-1}': + mu = out # x_{t-1} + x0 = _i(1.0 / self.posterior_mean_coef1, t, xt) * mu - \ + _i(self.posterior_mean_coef2 / self.posterior_mean_coef1, t, xt) * xt + elif self.mean_type == 'x0': + x0 = out + mu, _, _ = self.q_posterior_mean_variance(x0, xt, t) + elif self.mean_type == 'eps': + x0 = _i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) * out + mu, _, _ = self.q_posterior_mean_variance(x0, xt, t) + elif self.mean_type == 'v': + x0 = _i(self.sqrt_alphas_cumprod, t, xt) * xt - \ + _i(self.sqrt_one_minus_alphas_cumprod, t, xt) * out + mu, _, _ = self.q_posterior_mean_variance(x0, xt, t) + + # restrict the range of x0 + if percentile is not None: + assert percentile > 0 and percentile <= 1 # e.g., 0.995 + s = torch.quantile(x0.flatten(1).abs(), percentile, dim=1).clamp_(1.0).view(-1, 1, 1, 1) + x0 = torch.min(s, torch.max(-s, x0)) / s + elif clamp is not None: + x0 = x0.clamp(-clamp, clamp) + return mu, var, log_var, x0 + + @torch.no_grad() + def ddim_sample(self, xt, t, model, model_kwargs={}, clamp=None, percentile=None, condition_fn=None, guide_scale=None, ddim_timesteps=20, eta=0.0, context_size=32, context_stride=1, context_overlap=4, context_batch_size=1): + r"""Sample from p(x_{t-1} | x_t) using DDIM. + - condition_fn: for classifier-based guidance (guided-diffusion). + - guide_scale: for classifier-free guidance (glide/dalle-2). + """ + stride = self.num_timesteps // ddim_timesteps + + # predict distribution of p(x_{t-1} | x_t) + _, _, _, x0 = self.p_mean_variance(xt, t, model, model_kwargs, clamp, percentile, guide_scale, context_size, context_stride, context_overlap, context_batch_size) + if condition_fn is not None: + # x0 -> eps + alpha = _i(self.alphas_cumprod, t, xt) + eps = (_i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - x0) / \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) + eps = eps - (1 - alpha).sqrt() * condition_fn(xt, self._scale_timesteps(t), **model_kwargs) + + # eps -> x0 + x0 = _i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) * eps + + # derive variables + eps = (_i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - x0) / \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) + alphas = _i(self.alphas_cumprod, t, xt) + alphas_prev = _i(self.alphas_cumprod, (t - stride).clamp(0), xt) + sigmas = eta * torch.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) + + # random sample + noise = torch.randn_like(xt) + direction = torch.sqrt(1 - alphas_prev - sigmas ** 2) * eps + mask = t.ne(0).float().view(-1, *((1, ) * (xt.ndim - 1))) + xt_1 = torch.sqrt(alphas_prev) * x0 + direction + mask * sigmas * noise + return xt_1, x0 + + @torch.no_grad() + def ddim_sample_loop(self, noise, context_size, context_stride, context_overlap, model, model_kwargs={}, clamp=None, percentile=None, condition_fn=None, guide_scale=None, ddim_timesteps=20, eta=0.0, context_batch_size=1): + # prepare input + b = noise.size(0) + xt = noise + + # diffusion process (TODO: clamp is inaccurate! Consider replacing the stride by explicit prev/next steps) + steps = (1 + torch.arange(0, self.num_timesteps, self.num_timesteps // ddim_timesteps)).clamp(0, self.num_timesteps - 1).flip(0) + from tqdm import tqdm + + for step in tqdm(steps): + t = torch.full((b, ), step, dtype=torch.long, device=xt.device) + xt, _ = self.ddim_sample(xt, t, model, model_kwargs, clamp, percentile, condition_fn, guide_scale, ddim_timesteps, eta, context_size=context_size, context_stride=context_stride, context_overlap=context_overlap, context_batch_size=context_batch_size) + return xt + + @torch.no_grad() + def ddim_reverse_sample(self, xt, t, model, model_kwargs={}, clamp=None, percentile=None, guide_scale=None, ddim_timesteps=20): + r"""Sample from p(x_{t+1} | x_t) using DDIM reverse ODE (deterministic). + """ + stride = self.num_timesteps // ddim_timesteps + + # predict distribution of p(x_{t-1} | x_t) + _, _, _, x0 = self.p_mean_variance(xt, t, model, model_kwargs, clamp, percentile, guide_scale) + + # derive variables + eps = (_i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - x0) / \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) + alphas_next = _i( + torch.cat([self.alphas_cumprod, self.alphas_cumprod.new_zeros([1])]), + (t + stride).clamp(0, self.num_timesteps), xt) + + # reverse sample + mu = torch.sqrt(alphas_next) * x0 + torch.sqrt(1 - alphas_next) * eps + return mu, x0 + + @torch.no_grad() + def ddim_reverse_sample_loop(self, x0, model, model_kwargs={}, clamp=None, percentile=None, guide_scale=None, ddim_timesteps=20): + # prepare input + b = x0.size(0) + xt = x0 + + # reconstruction steps + steps = torch.arange(0, self.num_timesteps, self.num_timesteps // ddim_timesteps) + for step in steps: + t = torch.full((b, ), step, dtype=torch.long, device=xt.device) + xt, _ = self.ddim_reverse_sample(xt, t, model, model_kwargs, clamp, percentile, guide_scale, ddim_timesteps) + return xt + + @torch.no_grad() + def plms_sample(self, xt, t, model, model_kwargs={}, clamp=None, percentile=None, condition_fn=None, guide_scale=None, plms_timesteps=20): + r"""Sample from p(x_{t-1} | x_t) using PLMS. + - condition_fn: for classifier-based guidance (guided-diffusion). + - guide_scale: for classifier-free guidance (glide/dalle-2). + """ + stride = self.num_timesteps // plms_timesteps + + # function for compute eps + def compute_eps(xt, t): + # predict distribution of p(x_{t-1} | x_t) + _, _, _, x0 = self.p_mean_variance(xt, t, model, model_kwargs, clamp, percentile, guide_scale) + + # condition + if condition_fn is not None: + # x0 -> eps + alpha = _i(self.alphas_cumprod, t, xt) + eps = (_i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - x0) / \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) + eps = eps - (1 - alpha).sqrt() * condition_fn(xt, self._scale_timesteps(t), **model_kwargs) + + # eps -> x0 + x0 = _i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) * eps + + # derive eps + eps = (_i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - x0) / \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) + return eps + + # function for compute x_0 and x_{t-1} + def compute_x0(eps, t): + # eps -> x0 + x0 = _i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) * eps + + # deterministic sample + alphas_prev = _i(self.alphas_cumprod, (t - stride).clamp(0), xt) + direction = torch.sqrt(1 - alphas_prev) * eps + mask = t.ne(0).float().view(-1, *((1, ) * (xt.ndim - 1))) + xt_1 = torch.sqrt(alphas_prev) * x0 + direction + return xt_1, x0 + + # PLMS sample + eps = compute_eps(xt, t) + if len(eps_cache) == 0: + # 2nd order pseudo improved Euler + xt_1, x0 = compute_x0(eps, t) + eps_next = compute_eps(xt_1, (t - stride).clamp(0)) + eps_prime = (eps + eps_next) / 2.0 + elif len(eps_cache) == 1: + # 2nd order pseudo linear multistep (Adams-Bashforth) + eps_prime = (3 * eps - eps_cache[-1]) / 2.0 + elif len(eps_cache) == 2: + # 3nd order pseudo linear multistep (Adams-Bashforth) + eps_prime = (23 * eps - 16 * eps_cache[-1] + 5 * eps_cache[-2]) / 12.0 + elif len(eps_cache) >= 3: + # 4nd order pseudo linear multistep (Adams-Bashforth) + eps_prime = (55 * eps - 59 * eps_cache[-1] + 37 * eps_cache[-2] - 9 * eps_cache[-3]) / 24.0 + xt_1, x0 = compute_x0(eps_prime, t) + return xt_1, x0, eps + + @torch.no_grad() + def plms_sample_loop(self, noise, model, model_kwargs={}, clamp=None, percentile=None, condition_fn=None, guide_scale=None, plms_timesteps=20): + # prepare input + b = noise.size(0) + xt = noise + + # diffusion process + steps = (1 + torch.arange(0, self.num_timesteps, self.num_timesteps // plms_timesteps)).clamp(0, self.num_timesteps - 1).flip(0) + eps_cache = [] + for step in steps: + # PLMS sampling step + t = torch.full((b, ), step, dtype=torch.long, device=xt.device) + xt, _, eps = self.plms_sample(xt, t, model, model_kwargs, clamp, percentile, condition_fn, guide_scale, plms_timesteps, eps_cache) + + # update eps cache + eps_cache.append(eps) + if len(eps_cache) >= 4: + eps_cache.pop(0) + return xt + + def loss(self, x0, t, model, model_kwargs={}, noise=None, weight = None, use_div_loss= False, loss_mask=None): + + # noise = torch.randn_like(x0) if noise is None else noise # [80, 4, 8, 32, 32] + noise = self.sample_loss(x0, noise) + + xt = self.q_sample(x0, t, noise=noise) + + # compute loss + if self.loss_type in ['kl', 'rescaled_kl']: + loss, _ = self.variational_lower_bound(x0, xt, t, model, model_kwargs) + if self.loss_type == 'rescaled_kl': + loss = loss * self.num_timesteps + elif self.loss_type in ['mse', 'rescaled_mse', 'l1', 'rescaled_l1']: # self.loss_type: mse + out = model(xt, self._scale_timesteps(t), **model_kwargs) + + # VLB for variation + loss_vlb = 0.0 + if self.var_type in ['learned', 'learned_range']: # self.var_type: 'fixed_small' + out, var = out.chunk(2, dim=1) + frozen = torch.cat([out.detach(), var], dim=1) # learn var without affecting the prediction of mean + loss_vlb, _ = self.variational_lower_bound(x0, xt, t, model=lambda *args, **kwargs: frozen) + if self.loss_type.startswith('rescaled_'): + loss_vlb = loss_vlb * self.num_timesteps / 1000.0 + + # MSE/L1 for x0/eps + # target = {'eps': noise, 'x0': x0, 'x_{t-1}': self.q_posterior_mean_variance(x0, xt, t)[0]}[self.mean_type] + target = { + 'eps': noise, + 'x0': x0, + 'x_{t-1}': self.q_posterior_mean_variance(x0, xt, t)[0], + 'v':_i(self.sqrt_alphas_cumprod, t, xt) * noise - _i(self.sqrt_one_minus_alphas_cumprod, t, xt) * x0}[self.mean_type] + if loss_mask is not None: + loss_mask = loss_mask[:, :, 0, ...].unsqueeze(2) # just use one channel (all channels are same) + loss_mask = loss_mask.permute(0, 2, 1, 3, 4) # b,c,f,h,w + # use masked diffusion + loss = (out * loss_mask - target * loss_mask).pow(1 if self.loss_type.endswith('l1') else 2).abs().flatten(1).mean(dim=1) + else: + loss = (out - target).pow(1 if self.loss_type.endswith('l1') else 2).abs().flatten(1).mean(dim=1) + if weight is not None: + loss = loss*weight + + # div loss + if use_div_loss and self.mean_type == 'eps' and x0.shape[2]>1: + + # derive x0 + x0_ = _i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) * out + + + # ncfhw, std on f + div_loss = 0.001/(x0_.std(dim=2).flatten(1).mean(dim=1)+1e-4) + # print(div_loss,loss) + loss = loss+div_loss + + # total loss + loss = loss + loss_vlb + elif self.loss_type in ['charbonnier']: + out = model(xt, self._scale_timesteps(t), **model_kwargs) + + # VLB for variation + loss_vlb = 0.0 + if self.var_type in ['learned', 'learned_range']: + out, var = out.chunk(2, dim=1) + frozen = torch.cat([out.detach(), var], dim=1) # learn var without affecting the prediction of mean + loss_vlb, _ = self.variational_lower_bound(x0, xt, t, model=lambda *args, **kwargs: frozen) + if self.loss_type.startswith('rescaled_'): + loss_vlb = loss_vlb * self.num_timesteps / 1000.0 + + # MSE/L1 for x0/eps + target = {'eps': noise, 'x0': x0, 'x_{t-1}': self.q_posterior_mean_variance(x0, xt, t)[0]}[self.mean_type] + loss = torch.sqrt((out - target)**2 + self.epsilon) + if weight is not None: + loss = loss*weight + loss = loss.flatten(1).mean(dim=1) + + # total loss + loss = loss + loss_vlb + return loss + + def variational_lower_bound(self, x0, xt, t, model, model_kwargs={}, clamp=None, percentile=None): + # compute groundtruth and predicted distributions + mu1, _, log_var1 = self.q_posterior_mean_variance(x0, xt, t) + mu2, _, log_var2, x0 = self.p_mean_variance(xt, t, model, model_kwargs, clamp, percentile) + + # compute KL loss + kl = kl_divergence(mu1, log_var1, mu2, log_var2) + kl = kl.flatten(1).mean(dim=1) / math.log(2.0) + + # compute discretized NLL loss (for p(x0 | x1) only) + nll = -discretized_gaussian_log_likelihood(x0, mean=mu2, log_scale=0.5 * log_var2) + nll = nll.flatten(1).mean(dim=1) / math.log(2.0) + + # NLL for p(x0 | x1) and KL otherwise + vlb = torch.where(t == 0, nll, kl) + return vlb, x0 + + @torch.no_grad() + def variational_lower_bound_loop(self, x0, model, model_kwargs={}, clamp=None, percentile=None): + r"""Compute the entire variational lower bound, measured in bits-per-dim. + """ + # prepare input and output + b = x0.size(0) + metrics = {'vlb': [], 'mse': [], 'x0_mse': []} + + # loop + for step in torch.arange(self.num_timesteps).flip(0): + # compute VLB + t = torch.full((b, ), step, dtype=torch.long, device=x0.device) + # noise = torch.randn_like(x0) + noise = self.sample_loss(x0) + xt = self.q_sample(x0, t, noise) + vlb, pred_x0 = self.variational_lower_bound(x0, xt, t, model, model_kwargs, clamp, percentile) + + # predict eps from x0 + eps = (_i(self.sqrt_recip_alphas_cumprod, t, xt) * xt - x0) / \ + _i(self.sqrt_recipm1_alphas_cumprod, t, xt) + + # collect metrics + metrics['vlb'].append(vlb) + metrics['x0_mse'].append((pred_x0 - x0).square().flatten(1).mean(dim=1)) + metrics['mse'].append((eps - noise).square().flatten(1).mean(dim=1)) + metrics = {k: torch.stack(v, dim=1) for k, v in metrics.items()} + + # compute the prior KL term for VLB, measured in bits-per-dim + mu, _, log_var = self.q_mean_variance(x0, t) + kl_prior = kl_divergence(mu, log_var, torch.zeros_like(mu), torch.zeros_like(log_var)) + kl_prior = kl_prior.flatten(1).mean(dim=1) / math.log(2.0) + + # update metrics + metrics['prior_bits_per_dim'] = kl_prior + metrics['total_bits_per_dim'] = metrics['vlb'].sum(dim=1) + kl_prior + return metrics + + def _scale_timesteps(self, t): + if self.rescale_timesteps: + return t.float() * 1000.0 / self.num_timesteps + return t + #return t.float() + + + +def ordered_halving(val): + bin_str = f"{val:064b}" + bin_flip = bin_str[::-1] + as_int = int(bin_flip, 2) + + return as_int / (1 << 64) + + +def context_scheduler( + step: int = ..., + num_steps: Optional[int] = None, + num_frames: int = ..., + context_size: Optional[int] = None, + context_stride: int = 3, + context_overlap: int = 4, + closed_loop: bool = False, +): + if num_frames <= context_size: + yield list(range(num_frames)) + return + + context_stride = min( + context_stride, int(np.ceil(np.log2(num_frames / context_size))) + 1 + ) + + for context_step in 1 << np.arange(context_stride): + pad = int(round(num_frames * ordered_halving(step))) + for j in range( + int(ordered_halving(step) * context_step) + pad, + num_frames + pad + (0 if closed_loop else -context_overlap), + (context_size * context_step - context_overlap), + ): + + yield [ + e % num_frames + for e in range(j, j + context_size * context_step, context_step) + ] + diff --git a/animation/UniAnimate/tools/modules/diffusions/diffusion_gauss.py b/animation/UniAnimate/tools/modules/diffusions/diffusion_gauss.py new file mode 100644 index 0000000..16fd58c --- /dev/null +++ b/animation/UniAnimate/tools/modules/diffusions/diffusion_gauss.py @@ -0,0 +1,498 @@ +""" +GaussianDiffusion wraps operators for denoising diffusion models, including the +diffusion and denoising processes, as well as the loss evaluation. +""" +import torch +import torchsde +import random +from tqdm.auto import trange + + +__all__ = ['GaussianDiffusion'] + + +def _i(tensor, t, x): + """ + Index tensor using t and format the output according to x. + """ + shape = (x.size(0), ) + (1, ) * (x.ndim - 1) + return tensor[t.to(tensor.device)].view(shape).to(x.device) + + +class BatchedBrownianTree: + """ + A wrapper around torchsde.BrownianTree that enables batches of entropy. + """ + def __init__(self, x, t0, t1, seed=None, **kwargs): + t0, t1, self.sign = self.sort(t0, t1) + w0 = kwargs.get('w0', torch.zeros_like(x)) + if seed is None: + seed = torch.randint(0, 2 ** 63 - 1, []).item() + self.batched = True + try: + assert len(seed) == x.shape[0] + w0 = w0[0] + except TypeError: + seed = [seed] + self.batched = False + self.trees = [torchsde.BrownianTree( + t0, w0, t1, entropy=s, **kwargs + ) for s in seed] + + @staticmethod + def sort(a, b): + return (a, b, 1) if a < b else (b, a, -1) + + def __call__(self, t0, t1): + t0, t1, sign = self.sort(t0, t1) + w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign) + return w if self.batched else w[0] + + +class BrownianTreeNoiseSampler: + """ + A noise sampler backed by a torchsde.BrownianTree. + + Args: + x (Tensor): The tensor whose shape, device and dtype to use to generate + random samples. + sigma_min (float): The low end of the valid interval. + sigma_max (float): The high end of the valid interval. + seed (int or List[int]): The random seed. If a list of seeds is + supplied instead of a single integer, then the noise sampler will + use one BrownianTree per batch item, each with its own seed. + transform (callable): A function that maps sigma to the sampler's + internal timestep. + """ + def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x): + self.transform = transform + t0 = self.transform(torch.as_tensor(sigma_min)) + t1 = self.transform(torch.as_tensor(sigma_max)) + self.tree = BatchedBrownianTree(x, t0, t1, seed) + + def __call__(self, sigma, sigma_next): + t0 = self.transform(torch.as_tensor(sigma)) + t1 = self.transform(torch.as_tensor(sigma_next)) + return self.tree(t0, t1) / (t1 - t0).abs().sqrt() + + +def get_scalings(sigma): + c_out = -sigma + c_in = 1 / (sigma ** 2 + 1. ** 2) ** 0.5 + return c_out, c_in + + +@torch.no_grad() +def sample_dpmpp_2m_sde( + noise, + model, + sigmas, + eta=1., + s_noise=1., + solver_type='midpoint', + show_progress=True +): + """ + DPM-Solver++ (2M) SDE. + """ + assert solver_type in {'heun', 'midpoint'} + + x = noise * sigmas[0] + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas[sigmas < float('inf')].max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max) + old_denoised = None + h_last = None + + for i in trange(len(sigmas) - 1, disable=not show_progress): + if sigmas[i] == float('inf'): + # Euler method + denoised = model(noise, sigmas[i]) + x = denoised + sigmas[i + 1] * noise + else: + _, c_in = get_scalings(sigmas[i]) + denoised = model(x * c_in, sigmas[i]) + if sigmas[i + 1] == 0: + # Denoising step + x = denoised + else: + # DPM-Solver++(2M) SDE + t, s = -sigmas[i].log(), -sigmas[i + 1].log() + h = s - t + eta_h = eta * h + + x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + \ + (-h - eta_h).expm1().neg() * denoised + + if old_denoised is not None: + r = h_last / h + if solver_type == 'heun': + x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * \ + (1 / r) * (denoised - old_denoised) + elif solver_type == 'midpoint': + x = x + 0.5 * (-h - eta_h).expm1().neg() * \ + (1 / r) * (denoised - old_denoised) + + x = x + noise_sampler( + sigmas[i], + sigmas[i + 1] + ) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise + + old_denoised = denoised + h_last = h + return x + + +class GaussianDiffusion(object): + + def __init__(self, sigmas, prediction_type='eps'): + assert prediction_type in {'x0', 'eps', 'v'} + self.sigmas = sigmas.float() # noise coefficients + self.alphas = torch.sqrt(1 - sigmas ** 2).float() # signal coefficients + self.num_timesteps = len(sigmas) + self.prediction_type = prediction_type + + def diffuse(self, x0, t, noise=None): + """ + Add Gaussian noise to signal x0 according to: + q(x_t | x_0) = N(x_t | alpha_t x_0, sigma_t^2 I). + """ + noise = torch.randn_like(x0) if noise is None else noise + xt = _i(self.alphas, t, x0) * x0 + _i(self.sigmas, t, x0) * noise + return xt + + def denoise( + self, + xt, + t, + s, + model, + model_kwargs={}, + guide_scale=None, + guide_rescale=None, + clamp=None, + percentile=None + ): + """ + Apply one step of denoising from the posterior distribution q(x_s | x_t, x0). + Since x0 is not available, estimate the denoising results using the learned + distribution p(x_s | x_t, \hat{x}_0 == f(x_t)). + """ + s = t - 1 if s is None else s + + # hyperparams + sigmas = _i(self.sigmas, t, xt) + alphas = _i(self.alphas, t, xt) + alphas_s = _i(self.alphas, s.clamp(0), xt) + alphas_s[s < 0] = 1. + sigmas_s = torch.sqrt(1 - alphas_s ** 2) + + # precompute variables + betas = 1 - (alphas / alphas_s) ** 2 + coef1 = betas * alphas_s / sigmas ** 2 + coef2 = (alphas * sigmas_s ** 2) / (alphas_s * sigmas ** 2) + var = betas * (sigmas_s / sigmas) ** 2 + log_var = torch.log(var).clamp_(-20, 20) + + # prediction + if guide_scale is None: + assert isinstance(model_kwargs, dict) + out = model(xt, t=t, **model_kwargs) + else: + # classifier-free guidance (arXiv:2207.12598) + # model_kwargs[0]: conditional kwargs + # model_kwargs[1]: non-conditional kwargs + assert isinstance(model_kwargs, list) and len(model_kwargs) == 2 + y_out = model(xt, t=t, **model_kwargs[0]) + if guide_scale == 1.: + out = y_out + else: + u_out = model(xt, t=t, **model_kwargs[1]) + out = u_out + guide_scale * (y_out - u_out) + + # rescale the output according to arXiv:2305.08891 + if guide_rescale is not None: + assert guide_rescale >= 0 and guide_rescale <= 1 + ratio = (y_out.flatten(1).std(dim=1) / ( + out.flatten(1).std(dim=1) + 1e-12 + )).view((-1, ) + (1, ) * (y_out.ndim - 1)) + out *= guide_rescale * ratio + (1 - guide_rescale) * 1.0 + + # compute x0 + if self.prediction_type == 'x0': + x0 = out + elif self.prediction_type == 'eps': + x0 = (xt - sigmas * out) / alphas + elif self.prediction_type == 'v': + x0 = alphas * xt - sigmas * out + else: + raise NotImplementedError( + f'prediction_type {self.prediction_type} not implemented' + ) + + # restrict the range of x0 + if percentile is not None: + # NOTE: percentile should only be used when data is within range [-1, 1] + assert percentile > 0 and percentile <= 1 + s = torch.quantile(x0.flatten(1).abs(), percentile, dim=1) + s = s.clamp_(1.0).view((-1, ) + (1, ) * (xt.ndim - 1)) + x0 = torch.min(s, torch.max(-s, x0)) / s + elif clamp is not None: + x0 = x0.clamp(-clamp, clamp) + + # recompute eps using the restricted x0 + eps = (xt - alphas * x0) / sigmas + + # compute mu (mean of posterior distribution) using the restricted x0 + mu = coef1 * x0 + coef2 * xt + return mu, var, log_var, x0, eps + + @torch.no_grad() + def sample( + self, + noise, + model, + model_kwargs={}, + condition_fn=None, + guide_scale=None, + guide_rescale=None, + clamp=None, + percentile=None, + solver='euler_a', + steps=20, + t_max=None, + t_min=None, + discretization=None, + discard_penultimate_step=None, + return_intermediate=None, + show_progress=False, + seed=-1, + **kwargs + ): + # sanity check + assert isinstance(steps, (int, torch.LongTensor)) + assert t_max is None or (t_max > 0 and t_max <= self.num_timesteps - 1) + assert t_min is None or (t_min >= 0 and t_min < self.num_timesteps - 1) + assert discretization in (None, 'leading', 'linspace', 'trailing') + assert discard_penultimate_step in (None, True, False) + assert return_intermediate in (None, 'x0', 'xt') + + # function of diffusion solver + solver_fn = { + # 'heun': sample_heun, + 'dpmpp_2m_sde': sample_dpmpp_2m_sde + }[solver] + + # options + schedule = 'karras' if 'karras' in solver else None + discretization = discretization or 'linspace' + seed = seed if seed >= 0 else random.randint(0, 2 ** 31) + if isinstance(steps, torch.LongTensor): + discard_penultimate_step = False + if discard_penultimate_step is None: + discard_penultimate_step = True if solver in ( + 'dpm2', + 'dpm2_ancestral', + 'dpmpp_2m_sde', + 'dpm2_karras', + 'dpm2_ancestral_karras', + 'dpmpp_2m_sde_karras' + ) else False + + # function for denoising xt to get x0 + intermediates = [] + def model_fn(xt, sigma): + # denoising + t = self._sigma_to_t(sigma).repeat(len(xt)).round().long() + x0 = self.denoise( + xt, t, None, model, model_kwargs, guide_scale, guide_rescale, clamp, + percentile + )[-2] + + # collect intermediate outputs + if return_intermediate == 'xt': + intermediates.append(xt) + elif return_intermediate == 'x0': + intermediates.append(x0) + return x0 + + # get timesteps + if isinstance(steps, int): + steps += 1 if discard_penultimate_step else 0 + t_max = self.num_timesteps - 1 if t_max is None else t_max + t_min = 0 if t_min is None else t_min + + # discretize timesteps + if discretization == 'leading': + steps = torch.arange( + t_min, t_max + 1, (t_max - t_min + 1) / steps + ).flip(0) + elif discretization == 'linspace': + steps = torch.linspace(t_max, t_min, steps) + elif discretization == 'trailing': + steps = torch.arange(t_max, t_min - 1, -((t_max - t_min + 1) / steps)) + else: + raise NotImplementedError( + f'{discretization} discretization not implemented' + ) + steps = steps.clamp_(t_min, t_max) + steps = torch.as_tensor(steps, dtype=torch.float32, device=noise.device) + + # get sigmas + sigmas = self._t_to_sigma(steps) + sigmas = torch.cat([sigmas, sigmas.new_zeros([1])]) + if schedule == 'karras': + if sigmas[0] == float('inf'): + sigmas = karras_schedule( + n=len(steps) - 1, + sigma_min=sigmas[sigmas > 0].min().item(), + sigma_max=sigmas[sigmas < float('inf')].max().item(), + rho=7. + ).to(sigmas) + sigmas = torch.cat([ + sigmas.new_tensor([float('inf')]), sigmas, sigmas.new_zeros([1]) + ]) + else: + sigmas = karras_schedule( + n=len(steps), + sigma_min=sigmas[sigmas > 0].min().item(), + sigma_max=sigmas.max().item(), + rho=7. + ).to(sigmas) + sigmas = torch.cat([sigmas, sigmas.new_zeros([1])]) + if discard_penultimate_step: + sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) + + # sampling + x0 = solver_fn( + noise, + model_fn, + sigmas, + show_progress=show_progress, + **kwargs + ) + return (x0, intermediates) if return_intermediate is not None else x0 + + @torch.no_grad() + def ddim_reverse_sample( + self, + xt, + t, + model, + model_kwargs={}, + clamp=None, + percentile=None, + guide_scale=None, + guide_rescale=None, + ddim_timesteps=20, + reverse_steps=600 + ): + r"""Sample from p(x_{t+1} | x_t) using DDIM reverse ODE (deterministic). + """ + stride = reverse_steps // ddim_timesteps + + # predict distribution of p(x_{t-1} | x_t) + _, _, _, x0, eps = self.denoise( + xt, t, None, model, model_kwargs, guide_scale, guide_rescale, clamp, + percentile + ) + # derive variables + s = (t + stride).clamp(0, reverse_steps-1) + # hyperparams + sigmas = _i(self.sigmas, t, xt) + alphas = _i(self.alphas, t, xt) + alphas_s = _i(self.alphas, s.clamp(0), xt) + alphas_s[s < 0] = 1. + sigmas_s = torch.sqrt(1 - alphas_s ** 2) + + # reverse sample + mu = alphas_s * x0 + sigmas_s * eps + return mu, x0 + + @torch.no_grad() + def ddim_reverse_sample_loop( + self, + x0, + model, + model_kwargs={}, + clamp=None, + percentile=None, + guide_scale=None, + guide_rescale=None, + ddim_timesteps=20, + reverse_steps=600 + ): + # prepare input + b = x0.size(0) + xt = x0 + + # reconstruction steps + steps = torch.arange(0, reverse_steps, reverse_steps // ddim_timesteps) + for step in steps: + t = torch.full((b, ), step, dtype=torch.long, device=xt.device) + xt, _ = self.ddim_reverse_sample(xt, t, model, model_kwargs, clamp, percentile, guide_scale, guide_rescale, ddim_timesteps, reverse_steps) + return xt + + def _sigma_to_t(self, sigma): + if sigma == float('inf'): + t = torch.full_like(sigma, len(self.sigmas) - 1) + else: + log_sigmas = torch.sqrt( + self.sigmas ** 2 / (1 - self.sigmas ** 2) + ).log().to(sigma) + log_sigma = sigma.log() + dists = log_sigma - log_sigmas[:, None] + low_idx = dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp( + max=log_sigmas.shape[0] - 2 + ) + high_idx = low_idx + 1 + low, high = log_sigmas[low_idx], log_sigmas[high_idx] + w = (low - log_sigma) / (low - high) + w = w.clamp(0, 1) + t = (1 - w) * low_idx + w * high_idx + t = t.view(sigma.shape) + if t.ndim == 0: + t = t.unsqueeze(0) + return t + + def _t_to_sigma(self, t): + t = t.float() + low_idx, high_idx, w = t.floor().long(), t.ceil().long(), t.frac() + log_sigmas = torch.sqrt(self.sigmas ** 2 / (1 - self.sigmas ** 2)).log().to(t) + log_sigma = (1 - w) * log_sigmas[low_idx] + w * log_sigmas[high_idx] + log_sigma[torch.isnan(log_sigma) | torch.isinf(log_sigma)] = float('inf') + return log_sigma.exp() + + def prev_step(self, model_out, t, xt, inference_steps=50): + prev_t = t - self.num_timesteps // inference_steps + + sigmas = _i(self.sigmas, t, xt) + alphas = _i(self.alphas, t, xt) + alphas_prev = _i(self.alphas, prev_t.clamp(0), xt) + alphas_prev[prev_t < 0] = 1. + sigmas_prev = torch.sqrt(1 - alphas_prev ** 2) + + x0 = alphas * xt - sigmas * model_out + eps = (xt - alphas * x0) / sigmas + prev_sample = alphas_prev * x0 + sigmas_prev * eps + return prev_sample + + def next_step(self, model_out, t, xt, inference_steps=50): + t, next_t = min(t - self.num_timesteps // inference_steps, 999), t + + sigmas = _i(self.sigmas, t, xt) + alphas = _i(self.alphas, t, xt) + alphas_next = _i(self.alphas, next_t.clamp(0), xt) + alphas_next[next_t < 0] = 1. + sigmas_next = torch.sqrt(1 - alphas_next ** 2) + + x0 = alphas * xt - sigmas * model_out + eps = (xt - alphas * x0) / sigmas + next_sample = alphas_next * x0 + sigmas_next * eps + return next_sample + + def get_noise_pred_single(self, xt, t, model, model_kwargs): + assert isinstance(model_kwargs, dict) + out = model(xt, t=t, **model_kwargs) + return out + + diff --git a/animation/UniAnimate/tools/modules/diffusions/losses.py b/animation/UniAnimate/tools/modules/diffusions/losses.py new file mode 100644 index 0000000..c78383b --- /dev/null +++ b/animation/UniAnimate/tools/modules/diffusions/losses.py @@ -0,0 +1,28 @@ +import torch +import math + +__all__ = ['kl_divergence', 'discretized_gaussian_log_likelihood'] + +def kl_divergence(mu1, logvar1, mu2, logvar2): + return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) + ((mu1 - mu2) ** 2) * torch.exp(-logvar2)) + +def standard_normal_cdf(x): + r"""A fast approximation of the cumulative distribution function of the standard normal. + """ + return 0.5 * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3)))) + +def discretized_gaussian_log_likelihood(x0, mean, log_scale): + assert x0.shape == mean.shape == log_scale.shape + cx = x0 - mean + inv_stdv = torch.exp(-log_scale) + cdf_plus = standard_normal_cdf(inv_stdv * (cx + 1.0 / 255.0)) + cdf_min = standard_normal_cdf(inv_stdv * (cx - 1.0 / 255.0)) + log_cdf_plus = torch.log(cdf_plus.clamp(min=1e-12)) + log_one_minus_cdf_min = torch.log((1.0 - cdf_min).clamp(min=1e-12)) + cdf_delta = cdf_plus - cdf_min + log_probs = torch.where( + x0 < -0.999, + log_cdf_plus, + torch.where(x0 > 0.999, log_one_minus_cdf_min, torch.log(cdf_delta.clamp(min=1e-12)))) + assert log_probs.shape == x0.shape + return log_probs diff --git a/animation/UniAnimate/tools/modules/diffusions/schedules.py b/animation/UniAnimate/tools/modules/diffusions/schedules.py new file mode 100644 index 0000000..48b9236 --- /dev/null +++ b/animation/UniAnimate/tools/modules/diffusions/schedules.py @@ -0,0 +1,166 @@ +import math +import torch + + +def beta_schedule(schedule='cosine', + num_timesteps=1000, + zero_terminal_snr=False, + **kwargs): + # compute betas + betas = { + # 'logsnr_cosine_interp': logsnr_cosine_interp_schedule, + 'linear': linear_schedule, + 'linear_sd': linear_sd_schedule, + 'quadratic': quadratic_schedule, + 'cosine': cosine_schedule + }[schedule](num_timesteps, **kwargs) + + if zero_terminal_snr and abs(betas.max() - 1.0) > 0.0001: + betas = rescale_zero_terminal_snr(betas) + + return betas + + +def sigma_schedule(schedule='cosine', + num_timesteps=1000, + zero_terminal_snr=False, + **kwargs): + # compute betas + betas = { + 'logsnr_cosine_interp': logsnr_cosine_interp_schedule, + 'linear': linear_schedule, + 'linear_sd': linear_sd_schedule, + 'quadratic': quadratic_schedule, + 'cosine': cosine_schedule + }[schedule](num_timesteps, **kwargs) + if schedule == 'logsnr_cosine_interp': + sigma = betas + else: + sigma = betas_to_sigmas(betas) + if zero_terminal_snr and abs(sigma.max() - 1.0) > 0.0001: + sigma = rescale_zero_terminal_snr(sigma) + + return sigma + + +def linear_schedule(num_timesteps, init_beta, last_beta, **kwargs): + scale = 1000.0 / num_timesteps + init_beta = init_beta or scale * 0.0001 + ast_beta = last_beta or scale * 0.02 + return torch.linspace(init_beta, last_beta, num_timesteps, dtype=torch.float64) + +def logsnr_cosine_interp_schedule( + num_timesteps, + scale_min=2, + scale_max=4, + logsnr_min=-15, + logsnr_max=15, + **kwargs): + return logsnrs_to_sigmas( + _logsnr_cosine_interp(num_timesteps, logsnr_min, logsnr_max, scale_min, scale_max)) + +def linear_sd_schedule(num_timesteps, init_beta, last_beta, **kwargs): + return torch.linspace(init_beta ** 0.5, last_beta ** 0.5, num_timesteps, dtype=torch.float64) ** 2 + + +def quadratic_schedule(num_timesteps, init_beta, last_beta, **kwargs): + init_beta = init_beta or 0.0015 + last_beta = last_beta or 0.0195 + return torch.linspace(init_beta ** 0.5, last_beta ** 0.5, num_timesteps, dtype=torch.float64) ** 2 + + +def cosine_schedule(num_timesteps, cosine_s=0.008, **kwargs): + betas = [] + for step in range(num_timesteps): + t1 = step / num_timesteps + t2 = (step + 1) / num_timesteps + fn = lambda u: math.cos((u + cosine_s) / (1 + cosine_s) * math.pi / 2) ** 2 + betas.append(min(1.0 - fn(t2) / fn(t1), 0.999)) + return torch.tensor(betas, dtype=torch.float64) + + +# def cosine_schedule(n, cosine_s=0.008, **kwargs): +# ramp = torch.linspace(0, 1, n + 1) +# square_alphas = torch.cos((ramp + cosine_s) / (1 + cosine_s) * torch.pi / 2) ** 2 +# betas = (1 - square_alphas[1:] / square_alphas[:-1]).clamp(max=0.999) +# return betas_to_sigmas(betas) + + +def betas_to_sigmas(betas): + return torch.sqrt(1 - torch.cumprod(1 - betas, dim=0)) + + +def sigmas_to_betas(sigmas): + square_alphas = 1 - sigmas**2 + betas = 1 - torch.cat( + [square_alphas[:1], square_alphas[1:] / square_alphas[:-1]]) + return betas + + + +def sigmas_to_logsnrs(sigmas): + square_sigmas = sigmas**2 + return torch.log(square_sigmas / (1 - square_sigmas)) + + +def _logsnr_cosine(n, logsnr_min=-15, logsnr_max=15): + t_min = math.atan(math.exp(-0.5 * logsnr_min)) + t_max = math.atan(math.exp(-0.5 * logsnr_max)) + t = torch.linspace(1, 0, n) + logsnrs = -2 * torch.log(torch.tan(t_min + t * (t_max - t_min))) + return logsnrs + + +def _logsnr_cosine_shifted(n, logsnr_min=-15, logsnr_max=15, scale=2): + logsnrs = _logsnr_cosine(n, logsnr_min, logsnr_max) + logsnrs += 2 * math.log(1 / scale) + return logsnrs + +def karras_schedule(n, sigma_min=0.002, sigma_max=80.0, rho=7.0): + ramp = torch.linspace(1, 0, n) + min_inv_rho = sigma_min**(1 / rho) + max_inv_rho = sigma_max**(1 / rho) + sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho))**rho + sigmas = torch.sqrt(sigmas**2 / (1 + sigmas**2)) + return sigmas + +def _logsnr_cosine_interp(n, + logsnr_min=-15, + logsnr_max=15, + scale_min=2, + scale_max=4): + t = torch.linspace(1, 0, n) + logsnrs_min = _logsnr_cosine_shifted(n, logsnr_min, logsnr_max, scale_min) + logsnrs_max = _logsnr_cosine_shifted(n, logsnr_min, logsnr_max, scale_max) + logsnrs = t * logsnrs_min + (1 - t) * logsnrs_max + return logsnrs + + +def logsnrs_to_sigmas(logsnrs): + return torch.sqrt(torch.sigmoid(-logsnrs)) + + +def rescale_zero_terminal_snr(betas): + """ + Rescale Schedule to Zero Terminal SNR + """ + # Convert betas to alphas_bar_sqrt + alphas = 1 - betas + alphas_bar = alphas.cumprod(0) + alphas_bar_sqrt = alphas_bar.sqrt() + + # Store old values. 8 alphas_bar_sqrt_0 = a + alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() + alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() + # Shift so last timestep is zero. + alphas_bar_sqrt -= alphas_bar_sqrt_T + # Scale so first timestep is back to old value. + alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) + + # Convert alphas_bar_sqrt to betas + alphas_bar = alphas_bar_sqrt ** 2 + alphas = alphas_bar[1:] / alphas_bar[:-1] + alphas = torch.cat([alphas_bar[0:1], alphas]) + betas = 1 - alphas + return betas + diff --git a/animation/UniAnimate/tools/modules/embedding_manager.py b/animation/UniAnimate/tools/modules/embedding_manager.py new file mode 100644 index 0000000..a2d065e --- /dev/null +++ b/animation/UniAnimate/tools/modules/embedding_manager.py @@ -0,0 +1,179 @@ +import torch +from torch import nn +import torch.nn.functional as F +import open_clip + +from functools import partial +from utils.registry_class import EMBEDMANAGER + +DEFAULT_PLACEHOLDER_TOKEN = ["*"] + +PROGRESSIVE_SCALE = 2000 + +per_img_token_list = [ + 'א', 'ב', 'ג', 'ד', 'ה', 'ו', 'ז', 'ח', 'ט', 'י', 'כ', 'ל', 'מ', 'נ', 'ס', 'ע', 'פ', 'צ', 'ק', 'ר', 'ש', 'ת', +] + +def get_clip_token_for_string(string): + tokens = open_clip.tokenize(string) + + return tokens[0, 1] + +def get_embedding_for_clip_token(embedder, token): + return embedder(token.unsqueeze(0))[0] + + +@EMBEDMANAGER.register_class() +class EmbeddingManager(nn.Module): + def __init__( + self, + embedder, + placeholder_strings=None, + initializer_words=None, + per_image_tokens=False, + num_vectors_per_token=1, + progressive_words=False, + temporal_prompt_length=1, + token_dim=1024, + **kwargs + ): + super().__init__() + + self.string_to_token_dict = {} + + self.string_to_param_dict = nn.ParameterDict() + + self.initial_embeddings = nn.ParameterDict() # These should not be optimized + + self.progressive_words = progressive_words + self.progressive_counter = 0 + + self.max_vectors_per_token = num_vectors_per_token + + get_embedding_for_tkn = partial(get_embedding_for_clip_token, embedder.model.token_embedding.cpu()) + + if per_image_tokens: + placeholder_strings.extend(per_img_token_list) + + for idx, placeholder_string in enumerate(placeholder_strings): + + token = get_clip_token_for_string(placeholder_string) + + if initializer_words and idx < len(initializer_words): + init_word_token = get_clip_token_for_string(initializer_words[idx]) + + with torch.no_grad(): + init_word_embedding = get_embedding_for_tkn(init_word_token) + + token_params = torch.nn.Parameter(init_word_embedding.unsqueeze(0).repeat(num_vectors_per_token, 1), requires_grad=True) + self.initial_embeddings[placeholder_string] = torch.nn.Parameter(init_word_embedding.unsqueeze(0).repeat(num_vectors_per_token, 1), requires_grad=False) + else: + token_params = torch.nn.Parameter(torch.rand(size=(num_vectors_per_token, token_dim), requires_grad=True)) + + self.string_to_token_dict[placeholder_string] = token + self.string_to_param_dict[placeholder_string] = token_params + + + def forward( + self, + tokenized_text, + embedded_text, + ): + b, n, device = *tokenized_text.shape, tokenized_text.device + + for placeholder_string, placeholder_token in self.string_to_token_dict.items(): + + placeholder_embedding = self.string_to_param_dict[placeholder_string].to(device) + + if self.max_vectors_per_token == 1: # If there's only one vector per token, we can do a simple replacement + placeholder_idx = torch.where(tokenized_text == placeholder_token.to(device)) + embedded_text[placeholder_idx] = placeholder_embedding + else: # otherwise, need to insert and keep track of changing indices + if self.progressive_words: + self.progressive_counter += 1 + max_step_tokens = 1 + self.progressive_counter // PROGRESSIVE_SCALE + else: + max_step_tokens = self.max_vectors_per_token + + num_vectors_for_token = min(placeholder_embedding.shape[0], max_step_tokens) + + placeholder_rows, placeholder_cols = torch.where(tokenized_text == placeholder_token.to(device)) + + if placeholder_rows.nelement() == 0: + continue + + sorted_cols, sort_idx = torch.sort(placeholder_cols, descending=True) + sorted_rows = placeholder_rows[sort_idx] + + for idx in range(len(sorted_rows)): + row = sorted_rows[idx] + col = sorted_cols[idx] + + new_token_row = torch.cat([tokenized_text[row][:col], placeholder_token.repeat(num_vectors_for_token).to(device), tokenized_text[row][col + 1:]], axis=0)[:n] + new_embed_row = torch.cat([embedded_text[row][:col], placeholder_embedding[:num_vectors_for_token], embedded_text[row][col + 1:]], axis=0)[:n] + + embedded_text[row] = new_embed_row + tokenized_text[row] = new_token_row + + return embedded_text + + def forward_with_text_img( + self, + tokenized_text, + embedded_text, + embedded_img, + ): + device = tokenized_text.device + for placeholder_string, placeholder_token in self.string_to_token_dict.items(): + placeholder_embedding = self.string_to_param_dict[placeholder_string].to(device) + placeholder_idx = torch.where(tokenized_text == placeholder_token.to(device)) + embedded_text[placeholder_idx] = embedded_text[placeholder_idx] + embedded_img + placeholder_embedding + return embedded_text + + def forward_with_text( + self, + tokenized_text, + embedded_text + ): + device = tokenized_text.device + for placeholder_string, placeholder_token in self.string_to_token_dict.items(): + placeholder_embedding = self.string_to_param_dict[placeholder_string].to(device) + placeholder_idx = torch.where(tokenized_text == placeholder_token.to(device)) + embedded_text[placeholder_idx] = embedded_text[placeholder_idx] + placeholder_embedding + return embedded_text + + def save(self, ckpt_path): + torch.save({"string_to_token": self.string_to_token_dict, + "string_to_param": self.string_to_param_dict}, ckpt_path) + + def load(self, ckpt_path): + ckpt = torch.load(ckpt_path, map_location='cpu') + + string_to_token = ckpt["string_to_token"] + string_to_param = ckpt["string_to_param"] + for string, token in string_to_token.items(): + self.string_to_token_dict[string] = token + for string, param in string_to_param.items(): + self.string_to_param_dict[string] = param + + def get_embedding_norms_squared(self): + all_params = torch.cat(list(self.string_to_param_dict.values()), axis=0) # num_placeholders x embedding_dim + param_norm_squared = (all_params * all_params).sum(axis=-1) # num_placeholders + + return param_norm_squared + + def embedding_parameters(self): + return self.string_to_param_dict.parameters() + + def embedding_to_coarse_loss(self): + + loss = 0. + num_embeddings = len(self.initial_embeddings) + + for key in self.initial_embeddings: + optimized = self.string_to_param_dict[key] + coarse = self.initial_embeddings[key].clone().to(optimized.device) + + loss = loss + (optimized - coarse) @ (optimized - coarse).T / num_embeddings + + return loss \ No newline at end of file diff --git a/animation/UniAnimate/tools/modules/unet/__init__.py b/animation/UniAnimate/tools/modules/unet/__init__.py new file mode 100644 index 0000000..9bbb085 --- /dev/null +++ b/animation/UniAnimate/tools/modules/unet/__init__.py @@ -0,0 +1,2 @@ +from .unet_unianimate import * + diff --git a/animation/UniAnimate/tools/modules/unet/mha_flash.py b/animation/UniAnimate/tools/modules/unet/mha_flash.py new file mode 100644 index 0000000..c16663a --- /dev/null +++ b/animation/UniAnimate/tools/modules/unet/mha_flash.py @@ -0,0 +1,103 @@ +import torch +import torch.nn as nn +import torch.cuda.amp as amp +import torch.nn.functional as F +import math +import os +import time +import numpy as np +import random + +# from flash_attn.flash_attention import FlashAttention +class FlashAttentionBlock(nn.Module): + + def __init__(self, dim, context_dim=None, num_heads=None, head_dim=None, batch_size=4): + # consider head_dim first, then num_heads + num_heads = dim // head_dim if head_dim else num_heads + head_dim = dim // num_heads + assert num_heads * head_dim == dim + super(FlashAttentionBlock, self).__init__() + self.dim = dim + self.context_dim = context_dim + self.num_heads = num_heads + self.head_dim = head_dim + self.scale = math.pow(head_dim, -0.25) + + # layers + self.norm = nn.GroupNorm(32, dim) + self.to_qkv = nn.Conv2d(dim, dim * 3, 1) + if context_dim is not None: + self.context_kv = nn.Linear(context_dim, dim * 2) + self.proj = nn.Conv2d(dim, dim, 1) + + if self.head_dim <= 128 and (self.head_dim % 8) == 0: + new_scale = math.pow(head_dim, -0.5) + self.flash_attn = FlashAttention(softmax_scale=None, attention_dropout=0.0) + + # zero out the last layer params + nn.init.zeros_(self.proj.weight) + # self.apply(self._init_weight) + + + def _init_weight(self, module): + if isinstance(module, nn.Linear): + module.weight.data.normal_(mean=0.0, std=0.15) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Conv2d): + module.weight.data.normal_(mean=0.0, std=0.15) + if module.bias is not None: + module.bias.data.zero_() + + def forward(self, x, context=None): + r"""x: [B, C, H, W]. + context: [B, L, C] or None. + """ + identity = x + b, c, h, w, n, d = *x.size(), self.num_heads, self.head_dim + + # compute query, key, value + x = self.norm(x) + q, k, v = self.to_qkv(x).view(b, n * 3, d, h * w).chunk(3, dim=1) + if context is not None: + ck, cv = self.context_kv(context).reshape(b, -1, n * 2, d).permute(0, 2, 3, 1).chunk(2, dim=1) + k = torch.cat([ck, k], dim=-1) + v = torch.cat([cv, v], dim=-1) + cq = torch.zeros([b, n, d, 4], dtype=q.dtype, device=q.device) + q = torch.cat([q, cq], dim=-1) + + qkv = torch.cat([q,k,v], dim=1) + origin_dtype = qkv.dtype + qkv = qkv.permute(0, 3, 1, 2).reshape(b, -1, 3, n, d).half().contiguous() + out, _ = self.flash_attn(qkv) + out.to(origin_dtype) + + if context is not None: + out = out[:, :-4, :, :] + out = out.permute(0, 2, 3, 1).reshape(b, c, h, w) + + # output + x = self.proj(out) + return x + identity + +if __name__ == '__main__': + batch_size = 8 + flash_net = FlashAttentionBlock(dim=1280, context_dim=512, num_heads=None, head_dim=64, batch_size=batch_size).cuda() + + x = torch.randn([batch_size, 1280, 32, 32], dtype=torch.float32).cuda() + context = torch.randn([batch_size, 4, 512], dtype=torch.float32).cuda() + # context = None + flash_net.eval() + + with amp.autocast(enabled=True): + # warm up + for i in range(5): + y = flash_net(x, context) + torch.cuda.synchronize() + s1 = time.time() + for i in range(10): + y = flash_net(x, context) + torch.cuda.synchronize() + s2 = time.time() + + print(f'Average cost time {(s2-s1)*1000/10} ms') \ No newline at end of file diff --git a/animation/UniAnimate/tools/modules/unet/unet_unianimate.py b/animation/UniAnimate/tools/modules/unet/unet_unianimate.py new file mode 100644 index 0000000..3c343d2 --- /dev/null +++ b/animation/UniAnimate/tools/modules/unet/unet_unianimate.py @@ -0,0 +1,659 @@ +import math +import torch +import xformers +import xformers.ops +import torch.nn as nn +from einops import rearrange +import torch.nn.functional as F +from rotary_embedding_torch import RotaryEmbedding +from fairscale.nn.checkpoint import checkpoint_wrapper + +from .util import * +# from .mha_flash import FlashAttentionBlock +from utils.registry_class import MODEL + + +USE_TEMPORAL_TRANSFORMER = True + + + +class PreNormattention(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.fn = fn + def forward(self, x, **kwargs): + return self.fn(self.norm(x), **kwargs) + x + +class PreNormattention_qkv(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.fn = fn + def forward(self, q, k, v, **kwargs): + return self.fn(self.norm(q), self.norm(k), self.norm(v), **kwargs) + q + +class Attention(nn.Module): + def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.): + super().__init__() + inner_dim = dim_head * heads + project_out = not (heads == 1 and dim_head == dim) + + self.heads = heads + self.scale = dim_head ** -0.5 + + self.attend = nn.Softmax(dim = -1) + self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, dim), + nn.Dropout(dropout) + ) if project_out else nn.Identity() + + def forward(self, x): + b, n, _, h = *x.shape, self.heads + qkv = self.to_qkv(x).chunk(3, dim = -1) + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv) + + dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale + + attn = self.attend(dots) + + out = einsum('b h i j, b h j d -> b h i d', attn, v) + out = rearrange(out, 'b h n d -> b n (h d)') + return self.to_out(out) + + +class Attention_qkv(nn.Module): + def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.): + super().__init__() + inner_dim = dim_head * heads + project_out = not (heads == 1 and dim_head == dim) + + self.heads = heads + self.scale = dim_head ** -0.5 + + self.attend = nn.Softmax(dim = -1) + self.to_q = nn.Linear(dim, inner_dim, bias = False) + self.to_k = nn.Linear(dim, inner_dim, bias = False) + self.to_v = nn.Linear(dim, inner_dim, bias = False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, dim), + nn.Dropout(dropout) + ) if project_out else nn.Identity() + + def forward(self, q, k, v): + b, n, _, h = *q.shape, self.heads + bk = k.shape[0] + + q = self.to_q(q) + k = self.to_k(k) + v = self.to_v(v) + q = rearrange(q, 'b n (h d) -> b h n d', h = h) + k = rearrange(k, 'b n (h d) -> b h n d', b=bk, h = h) + v = rearrange(v, 'b n (h d) -> b h n d', b=bk, h = h) + + dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale + + attn = self.attend(dots) + + out = einsum('b h i j, b h j d -> b h i d', attn, v) + out = rearrange(out, 'b h n d -> b n (h d)') + return self.to_out(out) + +class PostNormattention(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.fn = fn + def forward(self, x, **kwargs): + return self.norm(self.fn(x, **kwargs) + x) + + + + +class Transformer_v2(nn.Module): + def __init__(self, heads=8, dim=2048, dim_head_k=256, dim_head_v=256, dropout_atte = 0.05, mlp_dim=2048, dropout_ffn = 0.05, depth=1): + super().__init__() + self.layers = nn.ModuleList([]) + self.depth = depth + for _ in range(depth): + self.layers.append(nn.ModuleList([ + PreNormattention(dim, Attention(dim, heads = heads, dim_head = dim_head_k, dropout = dropout_atte)), + FeedForward(dim, mlp_dim, dropout = dropout_ffn), + ])) + def forward(self, x): + for attn, ff in self.layers[:1]: + x = attn(x) + x = ff(x) + x + if self.depth > 1: + for attn, ff in self.layers[1:]: + x = attn(x) + x = ff(x) + x + return x + + +class DropPath(nn.Module): + r"""DropPath but without rescaling and supports optional all-zero and/or all-keep. + """ + def __init__(self, p): + super(DropPath, self).__init__() + self.p = p + + def forward(self, *args, zero=None, keep=None): + if not self.training: + return args[0] if len(args) == 1 else args + + # params + x = args[0] + b = x.size(0) + n = (torch.rand(b) < self.p).sum() + + # non-zero and non-keep mask + mask = x.new_ones(b, dtype=torch.bool) + if keep is not None: + mask[keep] = False + if zero is not None: + mask[zero] = False + + # drop-path index + index = torch.where(mask)[0] + index = index[torch.randperm(len(index))[:n]] + if zero is not None: + index = torch.cat([index, torch.where(zero)[0]], dim=0) + + # drop-path multiplier + multiplier = x.new_ones(b) + multiplier[index] = 0.0 + output = tuple(u * self.broadcast(multiplier, u) for u in args) + return output[0] if len(args) == 1 else output + + def broadcast(self, src, dst): + assert src.size(0) == dst.size(0) + shape = (dst.size(0), ) + (1, ) * (dst.ndim - 1) + return src.view(shape) + + + + +@MODEL.register_class() +class UNetSD_UniAnimate(nn.Module): + + def __init__(self, + config=None, + in_dim=4, + dim=512, + y_dim=512, + context_dim=1024, + hist_dim = 156, + concat_dim = 8, + out_dim=6, + dim_mult=[1, 2, 3, 4], + num_heads=None, + head_dim=64, + num_res_blocks=3, + attn_scales=[1 / 2, 1 / 4, 1 / 8], + use_scale_shift_norm=True, + dropout=0.1, + temporal_attn_times=1, + temporal_attention = True, + use_checkpoint=False, + use_image_dataset=False, + use_fps_condition= False, + use_sim_mask = False, + misc_dropout = 0.5, + training=True, + inpainting=True, + p_all_zero=0.1, + p_all_keep=0.1, + zero_y = None, + black_image_feature = None, + adapter_transformer_layers = 1, + num_tokens=4, + **kwargs + ): + embed_dim = dim * 4 + num_heads=num_heads if num_heads else dim//32 + super(UNetSD_UniAnimate, self).__init__() + self.zero_y = zero_y + self.black_image_feature = black_image_feature + self.cfg = config + self.in_dim = in_dim + self.dim = dim + self.y_dim = y_dim + self.context_dim = context_dim + self.num_tokens = num_tokens + self.hist_dim = hist_dim + self.concat_dim = concat_dim + self.embed_dim = embed_dim + self.out_dim = out_dim + self.dim_mult = dim_mult + + self.num_heads = num_heads + + self.head_dim = head_dim + self.num_res_blocks = num_res_blocks + self.attn_scales = attn_scales + self.use_scale_shift_norm = use_scale_shift_norm + self.temporal_attn_times = temporal_attn_times + self.temporal_attention = temporal_attention + self.use_checkpoint = use_checkpoint + self.use_image_dataset = use_image_dataset + self.use_fps_condition = use_fps_condition + self.use_sim_mask = use_sim_mask + self.training=training + self.inpainting = inpainting + self.video_compositions = self.cfg.video_compositions + self.misc_dropout = misc_dropout + self.p_all_zero = p_all_zero + self.p_all_keep = p_all_keep + + use_linear_in_temporal = False + transformer_depth = 1 + disabled_sa = False + # params + enc_dims = [dim * u for u in [1] + dim_mult] + dec_dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] + shortcut_dims = [] + scale = 1.0 + self.resolution = config.resolution + + + # embeddings + self.time_embed = nn.Sequential( + nn.Linear(dim, embed_dim), + nn.SiLU(), + nn.Linear(embed_dim, embed_dim)) + if 'image' in self.video_compositions: + self.pre_image_condition = nn.Sequential( + nn.Linear(self.context_dim, self.context_dim), + nn.SiLU(), + nn.Linear(self.context_dim, self.context_dim*self.num_tokens)) + + + if 'local_image' in self.video_compositions: + self.local_image_embedding = nn.Sequential( + nn.Conv2d(3, concat_dim * 4, 3, padding=1), + nn.SiLU(), + nn.AdaptiveAvgPool2d((self.resolution[1]//2, self.resolution[0]//2)), + nn.Conv2d(concat_dim * 4, concat_dim * 4, 3, stride=2, padding=1), + nn.SiLU(), + nn.Conv2d(concat_dim * 4, concat_dim, 3, stride=2, padding=1)) + self.local_image_embedding_after = Transformer_v2(heads=2, dim=concat_dim, dim_head_k=concat_dim, dim_head_v=concat_dim, dropout_atte = 0.05, mlp_dim=concat_dim, dropout_ffn = 0.05, depth=adapter_transformer_layers) + + if 'dwpose' in self.video_compositions: + self.dwpose_embedding = nn.Sequential( + nn.Conv2d(3, concat_dim * 4, 3, padding=1), + nn.SiLU(), + nn.AdaptiveAvgPool2d((self.resolution[1]//2, self.resolution[0]//2)), + nn.Conv2d(concat_dim * 4, concat_dim * 4, 3, stride=2, padding=1), + nn.SiLU(), + nn.Conv2d(concat_dim * 4, concat_dim, 3, stride=2, padding=1)) + self.dwpose_embedding_after = Transformer_v2(heads=2, dim=concat_dim, dim_head_k=concat_dim, dim_head_v=concat_dim, dropout_atte = 0.05, mlp_dim=concat_dim, dropout_ffn = 0.05, depth=adapter_transformer_layers) + + if 'randomref_pose' in self.video_compositions: + randomref_dim = 4 + self.randomref_pose2_embedding = nn.Sequential( + nn.Conv2d(3, concat_dim * 4, 3, padding=1), + nn.SiLU(), + nn.AdaptiveAvgPool2d((self.resolution[1]//2, self.resolution[0]//2)), + nn.Conv2d(concat_dim * 4, concat_dim * 4, 3, stride=2, padding=1), + nn.SiLU(), + nn.Conv2d(concat_dim * 4, concat_dim+randomref_dim, 3, stride=2, padding=1)) + self.randomref_pose2_embedding_after = Transformer_v2(heads=2, dim=concat_dim+randomref_dim, dim_head_k=concat_dim+randomref_dim, dim_head_v=concat_dim+randomref_dim, dropout_atte = 0.05, mlp_dim=concat_dim+randomref_dim, dropout_ffn = 0.05, depth=adapter_transformer_layers) + + if 'randomref' in self.video_compositions: + randomref_dim = 4 + self.randomref_embedding2 = nn.Sequential( + nn.Conv2d(randomref_dim, concat_dim * 4, 3, padding=1), + nn.SiLU(), + nn.Conv2d(concat_dim * 4, concat_dim * 4, 3, stride=1, padding=1), + nn.SiLU(), + nn.Conv2d(concat_dim * 4, concat_dim+randomref_dim, 3, stride=1, padding=1)) + self.randomref_embedding_after2 = Transformer_v2(heads=2, dim=concat_dim+randomref_dim, dim_head_k=concat_dim+randomref_dim, dim_head_v=concat_dim+randomref_dim, dropout_atte = 0.05, mlp_dim=concat_dim+randomref_dim, dropout_ffn = 0.05, depth=adapter_transformer_layers) + + ### Condition Dropout + self.misc_dropout = DropPath(misc_dropout) + + + if temporal_attention and not USE_TEMPORAL_TRANSFORMER: + self.rotary_emb = RotaryEmbedding(min(32, head_dim)) + self.time_rel_pos_bias = RelativePositionBias(heads = num_heads, max_distance = 32) # realistically will not be able to generate that many frames of video... yet + + if self.use_fps_condition: + self.fps_embedding = nn.Sequential( + nn.Linear(dim, embed_dim), + nn.SiLU(), + nn.Linear(embed_dim, embed_dim)) + nn.init.zeros_(self.fps_embedding[-1].weight) + nn.init.zeros_(self.fps_embedding[-1].bias) + + # encoder + self.input_blocks = nn.ModuleList() + self.pre_image = nn.Sequential() + init_block = nn.ModuleList([nn.Conv2d(self.in_dim + concat_dim, dim, 3, padding=1)]) + + #### need an initial temporal attention? + if temporal_attention: + if USE_TEMPORAL_TRANSFORMER: + init_block.append(TemporalTransformer(dim, num_heads, head_dim, depth=transformer_depth, context_dim=context_dim, + disable_self_attn=disabled_sa, use_linear=use_linear_in_temporal, multiply_zero=use_image_dataset)) + else: + init_block.append(TemporalAttentionMultiBlock(dim, num_heads, head_dim, rotary_emb=self.rotary_emb, temporal_attn_times=temporal_attn_times, use_image_dataset=use_image_dataset)) + + self.input_blocks.append(init_block) + shortcut_dims.append(dim) + for i, (in_dim, out_dim) in enumerate(zip(enc_dims[:-1], enc_dims[1:])): + for j in range(num_res_blocks): + + block = nn.ModuleList([ResBlock(in_dim, embed_dim, dropout, out_channels=out_dim, use_scale_shift_norm=False, use_image_dataset=use_image_dataset,)]) + + if scale in attn_scales: + block.append( + SpatialTransformer( + out_dim, out_dim // head_dim, head_dim, depth=1, context_dim=self.context_dim, + disable_self_attn=False, use_linear=True + ) + ) + if self.temporal_attention: + if USE_TEMPORAL_TRANSFORMER: + block.append(TemporalTransformer(out_dim, out_dim // head_dim, head_dim, depth=transformer_depth, context_dim=context_dim, + disable_self_attn=disabled_sa, use_linear=use_linear_in_temporal, multiply_zero=use_image_dataset)) + else: + block.append(TemporalAttentionMultiBlock(out_dim, num_heads, head_dim, rotary_emb = self.rotary_emb, use_image_dataset=use_image_dataset, use_sim_mask=use_sim_mask, temporal_attn_times=temporal_attn_times)) + in_dim = out_dim + self.input_blocks.append(block) + shortcut_dims.append(out_dim) + + # downsample + if i != len(dim_mult) - 1 and j == num_res_blocks - 1: + downsample = Downsample( + out_dim, True, dims=2, out_channels=out_dim + ) + shortcut_dims.append(out_dim) + scale /= 2.0 + self.input_blocks.append(downsample) + + # middle + self.middle_block = nn.ModuleList([ + ResBlock(out_dim, embed_dim, dropout, use_scale_shift_norm=False, use_image_dataset=use_image_dataset,), + SpatialTransformer( + out_dim, out_dim // head_dim, head_dim, depth=1, context_dim=self.context_dim, + disable_self_attn=False, use_linear=True + )]) + + if self.temporal_attention: + if USE_TEMPORAL_TRANSFORMER: + self.middle_block.append( + TemporalTransformer( + out_dim, out_dim // head_dim, head_dim, depth=transformer_depth, context_dim=context_dim, + disable_self_attn=disabled_sa, use_linear=use_linear_in_temporal, + multiply_zero=use_image_dataset, + ) + ) + else: + self.middle_block.append(TemporalAttentionMultiBlock(out_dim, num_heads, head_dim, rotary_emb = self.rotary_emb, use_image_dataset=use_image_dataset, use_sim_mask=use_sim_mask, temporal_attn_times=temporal_attn_times)) + + self.middle_block.append(ResBlock(out_dim, embed_dim, dropout, use_scale_shift_norm=False)) + + + # decoder + self.output_blocks = nn.ModuleList() + for i, (in_dim, out_dim) in enumerate(zip(dec_dims[:-1], dec_dims[1:])): + for j in range(num_res_blocks + 1): + + block = nn.ModuleList([ResBlock(in_dim + shortcut_dims.pop(), embed_dim, dropout, out_dim, use_scale_shift_norm=False, use_image_dataset=use_image_dataset, )]) + if scale in attn_scales: + block.append( + SpatialTransformer( + out_dim, out_dim // head_dim, head_dim, depth=1, context_dim=1024, + disable_self_attn=False, use_linear=True + ) + ) + if self.temporal_attention: + if USE_TEMPORAL_TRANSFORMER: + block.append( + TemporalTransformer( + out_dim, out_dim // head_dim, head_dim, depth=transformer_depth, context_dim=context_dim, + disable_self_attn=disabled_sa, use_linear=use_linear_in_temporal, multiply_zero=use_image_dataset + ) + ) + else: + block.append(TemporalAttentionMultiBlock(out_dim, num_heads, head_dim, rotary_emb =self.rotary_emb, use_image_dataset=use_image_dataset, use_sim_mask=use_sim_mask, temporal_attn_times=temporal_attn_times)) + in_dim = out_dim + + # upsample + if i != len(dim_mult) - 1 and j == num_res_blocks: + upsample = Upsample(out_dim, True, dims=2.0, out_channels=out_dim) + scale *= 2.0 + block.append(upsample) + self.output_blocks.append(block) + + # head + self.out = nn.Sequential( + nn.GroupNorm(32, out_dim), + nn.SiLU(), + nn.Conv2d(out_dim, self.out_dim, 3, padding=1)) + + # zero out the last layer params + nn.init.zeros_(self.out[-1].weight) + + def forward(self, + x, + t, + y = None, + depth = None, + image = None, + motion = None, + local_image = None, + single_sketch = None, + masked = None, + canny = None, + sketch = None, + dwpose = None, + randomref = None, + histogram = None, + fps = None, + video_mask = None, + focus_present_mask = None, + prob_focus_present = 0., # probability at which a given batch sample will focus on the present (0. is all off, 1. is completely arrested attention across time) + mask_last_frame_num = 0 # mask last frame num + ): + + + assert self.inpainting or masked is None, 'inpainting is not supported' + + batch, c, f, h, w= x.shape + frames = f + device = x.device + self.batch = batch + + #### image and video joint training, if mask_last_frame_num is set, prob_focus_present will be ignored + if mask_last_frame_num > 0: + focus_present_mask = None + video_mask[-mask_last_frame_num:] = False + else: + focus_present_mask = default(focus_present_mask, lambda: prob_mask_like((batch,), prob_focus_present, device = device)) + + if self.temporal_attention and not USE_TEMPORAL_TRANSFORMER: + time_rel_pos_bias = self.time_rel_pos_bias(x.shape[2], device = x.device) + else: + time_rel_pos_bias = None + + + # all-zero and all-keep masks + zero = torch.zeros(batch, dtype=torch.bool).to(x.device) + keep = torch.zeros(batch, dtype=torch.bool).to(x.device) + if self.training: + nzero = (torch.rand(batch) < self.p_all_zero).sum() + nkeep = (torch.rand(batch) < self.p_all_keep).sum() + index = torch.randperm(batch) + zero[index[0:nzero]] = True + keep[index[nzero:nzero + nkeep]] = True + assert not (zero & keep).any() + misc_dropout = partial(self.misc_dropout, zero = zero, keep = keep) + + + concat = x.new_zeros(batch, self.concat_dim, f, h, w) + + + # local_image_embedding (first frame) + if local_image is not None: + local_image = rearrange(local_image, 'b c f h w -> (b f) c h w') + local_image = self.local_image_embedding(local_image) + + h = local_image.shape[2] + local_image = self.local_image_embedding_after(rearrange(local_image, '(b f) c h w -> (b h w) f c', b = batch)) + local_image = rearrange(local_image, '(b h w) f c -> b c f h w', b = batch, h = h) + + concat = concat + misc_dropout(local_image) + + if dwpose is not None: + if 'randomref_pose' in self.video_compositions: + dwpose_random_ref = dwpose[:,:,:1].clone() + dwpose = dwpose[:,:,1:] + dwpose = rearrange(dwpose, 'b c f h w -> (b f) c h w') + dwpose = self.dwpose_embedding(dwpose) + + h = dwpose.shape[2] + dwpose = self.dwpose_embedding_after(rearrange(dwpose, '(b f) c h w -> (b h w) f c', b = batch)) + dwpose = rearrange(dwpose, '(b h w) f c -> b c f h w', b = batch, h = h) + concat = concat + misc_dropout(dwpose) + + randomref_b = x.new_zeros(batch, self.concat_dim+4, 1, h, w) + if randomref is not None: + randomref = rearrange(randomref[:,:,:1,], 'b c f h w -> (b f) c h w') + randomref = self.randomref_embedding2(randomref) + + h = randomref.shape[2] + randomref = self.randomref_embedding_after2(rearrange(randomref, '(b f) c h w -> (b h w) f c', b = batch)) + if 'randomref_pose' in self.video_compositions: + dwpose_random_ref = rearrange(dwpose_random_ref, 'b c f h w -> (b f) c h w') + dwpose_random_ref = self.randomref_pose2_embedding(dwpose_random_ref) + dwpose_random_ref = self.randomref_pose2_embedding_after(rearrange(dwpose_random_ref, '(b f) c h w -> (b h w) f c', b = batch)) + randomref = randomref + dwpose_random_ref + + randomref_a = rearrange(randomref, '(b h w) f c -> b c f h w', b = batch, h = h) + randomref_b = randomref_b + randomref_a + + + x = torch.cat([randomref_b, torch.cat([x, concat], dim=1)], dim=2) + x = rearrange(x, 'b c f h w -> (b f) c h w') + x = self.pre_image(x) + x = rearrange(x, '(b f) c h w -> b c f h w', b = batch) + + # embeddings + if self.use_fps_condition and fps is not None: + e = self.time_embed(sinusoidal_embedding(t, self.dim)) + self.fps_embedding(sinusoidal_embedding(fps, self.dim)) + else: + e = self.time_embed(sinusoidal_embedding(t, self.dim)) + + context = x.new_zeros(batch, 0, self.context_dim) + + + if image is not None: + y_context = self.zero_y.repeat(batch, 1, 1) + context = torch.cat([context, y_context], dim=1) + + image_context = misc_dropout(self.pre_image_condition(image).view(-1, self.num_tokens, self.context_dim)) # torch.cat([y[:,:-1,:], self.pre_image_condition(y[:,-1:,:]) ], dim=1) + context = torch.cat([context, image_context], dim=1) + else: + y_context = self.zero_y.repeat(batch, 1, 1) + context = torch.cat([context, y_context], dim=1) + image_context = torch.zeros_like(self.zero_y.repeat(batch, 1, 1))[:,:self.num_tokens] + context = torch.cat([context, image_context], dim=1) + + # repeat f times for spatial e and context + e = e.repeat_interleave(repeats=f+1, dim=0) + context = context.repeat_interleave(repeats=f+1, dim=0) + + + + ## always in shape (b f) c h w, except for temporal layer + x = rearrange(x, 'b c f h w -> (b f) c h w') + # encoder + xs = [] + for block in self.input_blocks: + x = self._forward_single(block, x, e, context, time_rel_pos_bias, focus_present_mask, video_mask) + xs.append(x) + + # middle + for block in self.middle_block: + x = self._forward_single(block, x, e, context, time_rel_pos_bias,focus_present_mask, video_mask) + + # decoder + for block in self.output_blocks: + x = torch.cat([x, xs.pop()], dim=1) + x = self._forward_single(block, x, e, context, time_rel_pos_bias,focus_present_mask, video_mask, reference=xs[-1] if len(xs) > 0 else None) + + # head + x = self.out(x) + + # reshape back to (b c f h w) + x = rearrange(x, '(b f) c h w -> b c f h w', b = batch) + return x[:,:,1:] + + def _forward_single(self, module, x, e, context, time_rel_pos_bias, focus_present_mask, video_mask, reference=None): + if isinstance(module, ResidualBlock): + module = checkpoint_wrapper(module) if self.use_checkpoint else module + x = x.contiguous() + x = module(x, e, reference) + elif isinstance(module, ResBlock): + module = checkpoint_wrapper(module) if self.use_checkpoint else module + x = x.contiguous() + x = module(x, e, self.batch) + elif isinstance(module, SpatialTransformer): + module = checkpoint_wrapper(module) if self.use_checkpoint else module + x = module(x, context) + elif isinstance(module, TemporalTransformer): + module = checkpoint_wrapper(module) if self.use_checkpoint else module + x = rearrange(x, '(b f) c h w -> b c f h w', b = self.batch) + x = module(x, context) + x = rearrange(x, 'b c f h w -> (b f) c h w') + elif isinstance(module, CrossAttention): + module = checkpoint_wrapper(module) if self.use_checkpoint else module + x = module(x, context) + elif isinstance(module, MemoryEfficientCrossAttention): + module = checkpoint_wrapper(module) if self.use_checkpoint else module + x = module(x, context) + elif isinstance(module, BasicTransformerBlock): + module = checkpoint_wrapper(module) if self.use_checkpoint else module + x = module(x, context) + elif isinstance(module, FeedForward): + x = module(x, context) + elif isinstance(module, Upsample): + x = module(x) + elif isinstance(module, Downsample): + x = module(x) + elif isinstance(module, Resample): + x = module(x, reference) + elif isinstance(module, TemporalAttentionBlock): + module = checkpoint_wrapper(module) if self.use_checkpoint else module + x = rearrange(x, '(b f) c h w -> b c f h w', b = self.batch) + x = module(x, time_rel_pos_bias, focus_present_mask, video_mask) + x = rearrange(x, 'b c f h w -> (b f) c h w') + elif isinstance(module, TemporalAttentionMultiBlock): + module = checkpoint_wrapper(module) if self.use_checkpoint else module + x = rearrange(x, '(b f) c h w -> b c f h w', b = self.batch) + x = module(x, time_rel_pos_bias, focus_present_mask, video_mask) + x = rearrange(x, 'b c f h w -> (b f) c h w') + elif isinstance(module, InitTemporalConvBlock): + module = checkpoint_wrapper(module) if self.use_checkpoint else module + x = rearrange(x, '(b f) c h w -> b c f h w', b = self.batch) + x = module(x) + x = rearrange(x, 'b c f h w -> (b f) c h w') + elif isinstance(module, TemporalConvBlock): + module = checkpoint_wrapper(module) if self.use_checkpoint else module + x = rearrange(x, '(b f) c h w -> b c f h w', b = self.batch) + x = module(x) + x = rearrange(x, 'b c f h w -> (b f) c h w') + elif isinstance(module, nn.ModuleList): + for block in module: + x = self._forward_single(block, x, e, context, time_rel_pos_bias, focus_present_mask, video_mask, reference) + else: + x = module(x) + return x + + + diff --git a/animation/UniAnimate/tools/modules/unet/util.py b/animation/UniAnimate/tools/modules/unet/util.py new file mode 100644 index 0000000..2903715 --- /dev/null +++ b/animation/UniAnimate/tools/modules/unet/util.py @@ -0,0 +1,1741 @@ +import math +import torch +import xformers +import open_clip +import xformers.ops +import torch.nn as nn +from torch import einsum +from einops import rearrange +from functools import partial +import torch.nn.functional as F +import torch.nn.init as init +from rotary_embedding_torch import RotaryEmbedding +from fairscale.nn.checkpoint import checkpoint_wrapper + +# from .mha_flash import FlashAttentionBlock +from utils.registry_class import MODEL + + +### load all keys started with prefix and replace them with new_prefix +def load_Block(state, prefix, new_prefix=None): + if new_prefix is None: + new_prefix = prefix + + state_dict = {} + state = {key:value for key,value in state.items() if prefix in key} + for key,value in state.items(): + new_key = key.replace(prefix, new_prefix) + state_dict[new_key]=value + return state_dict + + +def load_2d_pretrained_state_dict(state,cfg): + + new_state_dict = {} + + dim = cfg.unet_dim + num_res_blocks = cfg.unet_res_blocks + temporal_attention = cfg.temporal_attention + temporal_conv = cfg.temporal_conv + dim_mult = cfg.unet_dim_mult + attn_scales = cfg.unet_attn_scales + + # params + enc_dims = [dim * u for u in [1] + dim_mult] + dec_dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]] + shortcut_dims = [] + scale = 1.0 + + #embeddings + state_dict = load_Block(state,prefix=f'time_embedding') + new_state_dict.update(state_dict) + state_dict = load_Block(state,prefix=f'y_embedding') + new_state_dict.update(state_dict) + state_dict = load_Block(state,prefix=f'context_embedding') + new_state_dict.update(state_dict) + + encoder_idx = 0 + ### init block + state_dict = load_Block(state,prefix=f'encoder.{encoder_idx}',new_prefix=f'encoder.{encoder_idx}.0') + new_state_dict.update(state_dict) + encoder_idx += 1 + + shortcut_dims.append(dim) + for i, (in_dim, out_dim) in enumerate(zip(enc_dims[:-1], enc_dims[1:])): + for j in range(num_res_blocks): + # residual (+attention) blocks + idx = 0 + idx_ = 0 + # residual (+attention) blocks + state_dict = load_Block(state,prefix=f'encoder.{encoder_idx}.{idx}',new_prefix=f'encoder.{encoder_idx}.{idx_}') + new_state_dict.update(state_dict) + idx += 1 + idx_ = 2 + + if scale in attn_scales: + # block.append(AttentionBlock(out_dim, context_dim, num_heads, head_dim)) + state_dict = load_Block(state,prefix=f'encoder.{encoder_idx}.{idx}',new_prefix=f'encoder.{encoder_idx}.{idx_}') + new_state_dict.update(state_dict) + # if temporal_attention: + # block.append(TemporalAttentionBlock(out_dim, num_heads, head_dim, rotary_emb = self.rotary_emb)) + in_dim = out_dim + encoder_idx += 1 + shortcut_dims.append(out_dim) + + # downsample + if i != len(dim_mult) - 1 and j == num_res_blocks - 1: + # downsample = ResidualBlock(out_dim, embed_dim, out_dim, use_scale_shift_norm, 0.5, dropout) + state_dict = load_Block(state,prefix=f'encoder.{encoder_idx}',new_prefix=f'encoder.{encoder_idx}.0') + new_state_dict.update(state_dict) + + shortcut_dims.append(out_dim) + scale /= 2.0 + encoder_idx += 1 + + # middle + # self.middle = nn.ModuleList([ + # ResidualBlock(out_dim, embed_dim, out_dim, use_scale_shift_norm, 'none'), + # TemporalConvBlock(out_dim), + # AttentionBlock(out_dim, context_dim, num_heads, head_dim)]) + # if temporal_attention: + # self.middle.append(TemporalAttentionBlock(out_dim, num_heads, head_dim, rotary_emb = self.rotary_emb)) + # elif temporal_conv: + # self.middle.append(TemporalConvBlock(out_dim,dropout=dropout)) + # self.middle.append(ResidualBlock(out_dim, embed_dim, out_dim, use_scale_shift_norm, 'none')) + # self.middle.append(TemporalConvBlock(out_dim)) + + + # middle + middle_idx = 0 + # self.middle = nn.ModuleList([ + # ResidualBlock(out_dim, embed_dim, out_dim, use_scale_shift_norm, 1.0, dropout), + # AttentionBlock(out_dim, context_dim, num_heads, head_dim)]) + state_dict = load_Block(state,prefix=f'middle.{middle_idx}') + new_state_dict.update(state_dict) + middle_idx += 2 + + state_dict = load_Block(state,prefix=f'middle.1',new_prefix=f'middle.{middle_idx}') + new_state_dict.update(state_dict) + middle_idx += 1 + + for _ in range(cfg.temporal_attn_times): + # self.middle.append(TemporalAttentionBlock(out_dim, num_heads, head_dim, rotary_emb = self.rotary_emb)) + middle_idx += 1 + + # self.middle.append(ResidualBlock(out_dim, embed_dim, out_dim, use_scale_shift_norm, 1.0, dropout)) + state_dict = load_Block(state,prefix=f'middle.2',new_prefix=f'middle.{middle_idx}') + new_state_dict.update(state_dict) + middle_idx += 2 + + + decoder_idx = 0 + for i, (in_dim, out_dim) in enumerate(zip(dec_dims[:-1], dec_dims[1:])): + for j in range(num_res_blocks + 1): + idx = 0 + idx_ = 0 + # residual (+attention) blocks + # block = nn.ModuleList([ResidualBlock(in_dim + shortcut_dims.pop(), embed_dim, out_dim, use_scale_shift_norm, 1.0, dropout)]) + state_dict = load_Block(state,prefix=f'decoder.{decoder_idx}.{idx}',new_prefix=f'decoder.{decoder_idx}.{idx_}') + new_state_dict.update(state_dict) + idx += 1 + idx_ += 2 + if scale in attn_scales: + # block.append(AttentionBlock(out_dim, context_dim, num_heads, head_dim)) + state_dict = load_Block(state,prefix=f'decoder.{decoder_idx}.{idx}',new_prefix=f'decoder.{decoder_idx}.{idx_}') + new_state_dict.update(state_dict) + idx += 1 + idx_ += 1 + for _ in range(cfg.temporal_attn_times): + # block.append(TemporalAttentionBlock(out_dim, num_heads, head_dim, rotary_emb = self.rotary_emb)) + idx_ +=1 + + in_dim = out_dim + + # upsample + if i != len(dim_mult) - 1 and j == num_res_blocks: + + # upsample = ResidualBlock(out_dim, embed_dim, out_dim, use_scale_shift_norm, 2.0, dropout) + state_dict = load_Block(state,prefix=f'decoder.{decoder_idx}.{idx}',new_prefix=f'decoder.{decoder_idx}.{idx_}') + new_state_dict.update(state_dict) + idx += 1 + idx_ += 2 + + scale *= 2.0 + # block.append(upsample) + # self.decoder.append(block) + decoder_idx += 1 + + # head + # self.head = nn.Sequential( + # nn.GroupNorm(32, out_dim), + # nn.SiLU(), + # nn.Conv3d(out_dim, self.out_dim, (1,3,3), padding=(0,1,1))) + state_dict = load_Block(state,prefix=f'head') + new_state_dict.update(state_dict) + + return new_state_dict + +def sinusoidal_embedding(timesteps, dim): + # check input + half = dim // 2 + timesteps = timesteps.float() + + # compute sinusoidal embedding + sinusoid = torch.outer( + timesteps, + torch.pow(10000, -torch.arange(half).to(timesteps).div(half))) + x = torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1) + if dim % 2 != 0: + x = torch.cat([x, torch.zeros_like(x[:, :1])], dim=1) + return x + +def exists(x): + return x is not None + +def default(val, d): + if exists(val): + return val + return d() if callable(d) else d + +def prob_mask_like(shape, prob, device): + if prob == 1: + return torch.ones(shape, device = device, dtype = torch.bool) + elif prob == 0: + return torch.zeros(shape, device = device, dtype = torch.bool) + else: + mask = torch.zeros(shape, device = device).float().uniform_(0, 1) < prob + ### aviod mask all, which will cause find_unused_parameters error + if mask.all(): + mask[0]=False + return mask + + +class MemoryEfficientCrossAttention(nn.Module): + # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 + def __init__(self, query_dim, max_bs=4096, context_dim=None, heads=8, dim_head=64, dropout=0.0): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.max_bs = max_bs + self.heads = heads + self.dim_head = dim_head + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)) + self.attention_op: Optional[Any] = None + + def forward(self, x, context=None, mask=None): + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + v = self.to_v(context) + + b, _, _ = q.shape + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (q, k, v), + ) + + # actually compute the attention, what we cannot get enough of + if q.shape[0] > self.max_bs: + q_list = torch.chunk(q, q.shape[0] // self.max_bs, dim=0) + k_list = torch.chunk(k, k.shape[0] // self.max_bs, dim=0) + v_list = torch.chunk(v, v.shape[0] // self.max_bs, dim=0) + out_list = [] + for q_1, k_1, v_1 in zip(q_list, k_list, v_list): + out = xformers.ops.memory_efficient_attention( + q_1, k_1, v_1, attn_bias=None, op=self.attention_op) + out_list.append(out) + out = torch.cat(out_list, dim=0) + else: + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op) + + if exists(mask): + raise NotImplementedError + out = ( + out.unsqueeze(0) + .reshape(b, self.heads, out.shape[1], self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b, out.shape[1], self.heads * self.dim_head) + ) + return self.to_out(out) + +class RelativePositionBias(nn.Module): + def __init__( + self, + heads = 8, + num_buckets = 32, + max_distance = 128 + ): + super().__init__() + self.num_buckets = num_buckets + self.max_distance = max_distance + self.relative_attention_bias = nn.Embedding(num_buckets, heads) + + @staticmethod + def _relative_position_bucket(relative_position, num_buckets = 32, max_distance = 128): + ret = 0 + n = -relative_position + + num_buckets //= 2 + ret += (n < 0).long() * num_buckets + n = torch.abs(n) + + max_exact = num_buckets // 2 + is_small = n < max_exact + + val_if_large = max_exact + ( + torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact) + ).long() + val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1)) + + ret += torch.where(is_small, n, val_if_large) + return ret + + def forward(self, n, device): + q_pos = torch.arange(n, dtype = torch.long, device = device) + k_pos = torch.arange(n, dtype = torch.long, device = device) + rel_pos = rearrange(k_pos, 'j -> 1 j') - rearrange(q_pos, 'i -> i 1') + rp_bucket = self._relative_position_bucket(rel_pos, num_buckets = self.num_buckets, max_distance = self.max_distance) + values = self.relative_attention_bias(rp_bucket) + return rearrange(values, 'i j h -> h i j') + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + NEW: use_linear for more efficiency instead of the 1x1 convs + """ + def __init__(self, in_channels, n_heads, d_head, + depth=1, dropout=0., context_dim=None, + disable_self_attn=False, use_linear=False, + use_checkpoint=True): + super().__init__() + if exists(context_dim) and not isinstance(context_dim, list): + context_dim = [context_dim] + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + if not use_linear: + self.proj_in = nn.Conv2d(in_channels, + inner_dim, + kernel_size=1, + stride=1, + padding=0) + else: + self.proj_in = nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = nn.ModuleList( + [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], + disable_self_attn=disable_self_attn, checkpoint=use_checkpoint) + for d in range(depth)] + ) + if not use_linear: + self.proj_out = zero_module(nn.Conv2d(inner_dim, + in_channels, + kernel_size=1, + stride=1, + padding=0)) + else: + self.proj_out = zero_module(nn.Linear(in_channels, inner_dim)) + self.use_linear = use_linear + + def forward(self, x, context=None): + # note: if no context is given, cross-attention defaults to self-attention + if not isinstance(context, list): + context = [context] + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, 'b c h w -> b (h w) c').contiguous() + if self.use_linear: + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + x = block(x, context=context[i]) + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous() + if not self.use_linear: + x = self.proj_out(x) + return x + x_in + + +class SpatialTransformerWithAdapter(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + NEW: use_linear for more efficiency instead of the 1x1 convs + """ + def __init__(self, in_channels, n_heads, d_head, + depth=1, dropout=0., context_dim=None, + disable_self_attn=False, use_linear=False, + use_checkpoint=True, + adapter_list=[], adapter_position_list=['', 'parallel', ''], + adapter_hidden_dim=None): + super().__init__() + if exists(context_dim) and not isinstance(context_dim, list): + context_dim = [context_dim] + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + if not use_linear: + self.proj_in = nn.Conv2d(in_channels, + inner_dim, + kernel_size=1, + stride=1, + padding=0) + else: + self.proj_in = nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = nn.ModuleList( + [BasicTransformerBlockWithAdapter(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], + disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, + adapter_list=adapter_list, adapter_position_list=adapter_position_list, + adapter_hidden_dim=adapter_hidden_dim) + for d in range(depth)] + ) + if not use_linear: + self.proj_out = zero_module(nn.Conv2d(inner_dim, + in_channels, + kernel_size=1, + stride=1, + padding=0)) + else: + self.proj_out = zero_module(nn.Linear(in_channels, inner_dim)) + self.use_linear = use_linear + + def forward(self, x, context=None): + # note: if no context is given, cross-attention defaults to self-attention + if not isinstance(context, list): + context = [context] + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, 'b c h w -> b (h w) c').contiguous() + if self.use_linear: + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + x = block(x, context=context[i]) + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous() + if not self.use_linear: + x = self.proj_out(x) + return x + x_in + +import os +_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32") + +class CrossAttention(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.scale = dim_head ** -0.5 + self.heads = heads + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, query_dim), + nn.Dropout(dropout) + ) + + def forward(self, x, context=None, mask=None): + h = self.heads + + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + v = self.to_v(context) + + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) + + # force cast to fp32 to avoid overflowing + if _ATTN_PRECISION =="fp32": + with torch.autocast(enabled=False, device_type = 'cuda'): + q, k = q.float(), k.float() + sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale + else: + sim = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale + + del q, k + + if exists(mask): + mask = rearrange(mask, 'b ... -> b (...)') + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + sim = sim.softmax(dim=-1) + + out = torch.einsum('b i j, b j d -> b i d', sim, v) + out = rearrange(out, '(b h) n d -> b n (h d)', h=h) + return self.to_out(out) + + +class Adapter(nn.Module): + def __init__(self, in_dim, hidden_dim, condition_dim=None): + super().__init__() + self.down_linear = nn.Linear(in_dim, hidden_dim) + self.up_linear = nn.Linear(hidden_dim, in_dim) + self.condition_dim = condition_dim + if condition_dim is not None: + self.condition_linear = nn.Linear(condition_dim, in_dim) + + init.zeros_(self.up_linear.weight) + init.zeros_(self.up_linear.bias) + + def forward(self, x, condition=None, condition_lam=1): + x_in = x + if self.condition_dim is not None and condition is not None: + x = x + condition_lam * self.condition_linear(condition) + x = self.down_linear(x) + x = F.gelu(x) + x = self.up_linear(x) + x += x_in + return x + + +class MemoryEfficientCrossAttention_attemask(nn.Module): + # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223 + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.heads = heads + self.dim_head = dim_head + + self.to_q = nn.Linear(query_dim, inner_dim, bias=False) + self.to_k = nn.Linear(context_dim, inner_dim, bias=False) + self.to_v = nn.Linear(context_dim, inner_dim, bias=False) + + self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout)) + self.attention_op: Optional[Any] = None + + def forward(self, x, context=None, mask=None): + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + v = self.to_v(context) + + b, _, _ = q.shape + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, t.shape[1], self.heads, self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b * self.heads, t.shape[1], self.dim_head) + .contiguous(), + (q, k, v), + ) + + # actually compute the attention, what we cannot get enough of + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=xformers.ops.LowerTriangularMask(), op=self.attention_op) + + if exists(mask): + raise NotImplementedError + out = ( + out.unsqueeze(0) + .reshape(b, self.heads, out.shape[1], self.dim_head) + .permute(0, 2, 1, 3) + .reshape(b, out.shape[1], self.heads * self.dim_head) + ) + return self.to_out(out) + + + +class BasicTransformerBlock_attemask(nn.Module): + # ATTENTION_MODES = { + # "softmax": CrossAttention, # vanilla attention + # "softmax-xformers": MemoryEfficientCrossAttention + # } + def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, + disable_self_attn=False): + super().__init__() + # attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax" + # assert attn_mode in self.ATTENTION_MODES + # attn_cls = CrossAttention + attn_cls = MemoryEfficientCrossAttention_attemask + self.disable_self_attn = disable_self_attn + self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, + context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim, + heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + self.checkpoint = checkpoint + + def forward_(self, x, context=None): + return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) + + def forward(self, x, context=None): + x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x + x = self.attn2(self.norm2(x), context=context) + x + x = self.ff(self.norm3(x)) + x + return x + + +class BasicTransformerBlockWithAdapter(nn.Module): + # ATTENTION_MODES = { + # "softmax": CrossAttention, # vanilla attention + # "softmax-xformers": MemoryEfficientCrossAttention + # } + def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, disable_self_attn=False, + adapter_list=[], adapter_position_list=['parallel', 'parallel', 'parallel'], adapter_hidden_dim=None, adapter_condition_dim=None + ): + super().__init__() + # attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax" + # assert attn_mode in self.ATTENTION_MODES + # attn_cls = CrossAttention + attn_cls = MemoryEfficientCrossAttention + self.disable_self_attn = disable_self_attn + self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, + context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim, + heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + self.checkpoint = checkpoint + # adapter + self.adapter_list = adapter_list + self.adapter_position_list = adapter_position_list + hidden_dim = dim//2 if not adapter_hidden_dim else adapter_hidden_dim + if "self_attention" in adapter_list: + self.attn_adapter = Adapter(dim, hidden_dim, adapter_condition_dim) + if "cross_attention" in adapter_list: + self.cross_attn_adapter = Adapter(dim, hidden_dim, adapter_condition_dim) + if "feedforward" in adapter_list: + self.ff_adapter = Adapter(dim, hidden_dim, adapter_condition_dim) + + + def forward_(self, x, context=None, adapter_condition=None, adapter_condition_lam=1): + return checkpoint(self._forward, (x, context, adapter_condition, adapter_condition_lam), self.parameters(), self.checkpoint) + + def forward(self, x, context=None, adapter_condition=None, adapter_condition_lam=1): + if "self_attention" in self.adapter_list: + if self.adapter_position_list[0] == 'parallel': + # parallel + x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + self.attn_adapter(x, adapter_condition, adapter_condition_lam) + elif self.adapter_position_list[0] == 'serial': + # serial + x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x + x = self.attn_adapter(x, adapter_condition, adapter_condition_lam) + else: + x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x + + if "cross_attention" in self.adapter_list: + if self.adapter_position_list[1] == 'parallel': + # parallel + x = self.attn2(self.norm2(x), context=context) + self.cross_attn_adapter(x, adapter_condition, adapter_condition_lam) + elif self.adapter_position_list[1] == 'serial': + x = self.attn2(self.norm2(x), context=context) + x + x = self.cross_attn_adapter(x, adapter_condition, adapter_condition_lam) + else: + x = self.attn2(self.norm2(x), context=context) + x + + if "feedforward" in self.adapter_list: + if self.adapter_position_list[2] == 'parallel': + x = self.ff(self.norm3(x)) + self.ff_adapter(x, adapter_condition, adapter_condition_lam) + elif self.adapter_position_list[2] == 'serial': + x = self.ff(self.norm3(x)) + x + x = self.ff_adapter(x, adapter_condition, adapter_condition_lam) + else: + x = self.ff(self.norm3(x)) + x + + return x + +class BasicTransformerBlock(nn.Module): + # ATTENTION_MODES = { + # "softmax": CrossAttention, # vanilla attention + # "softmax-xformers": MemoryEfficientCrossAttention + # } + def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, + disable_self_attn=False): + super().__init__() + # attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax" + # assert attn_mode in self.ATTENTION_MODES + # attn_cls = CrossAttention + attn_cls = MemoryEfficientCrossAttention + self.disable_self_attn = disable_self_attn + self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout, + context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn + self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff) + self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim, + heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + self.norm3 = nn.LayerNorm(dim) + self.checkpoint = checkpoint + + def forward_(self, x, context=None): + return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint) + + def forward(self, x, context=None): + x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x + x = self.attn2(self.norm2(x), context=context) + x + x = self.ff(self.norm3(x)) + x + return x + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=padding) + + def forward(self, x): + assert x.shape[1] == self.channels + if self.dims == 3: + x = F.interpolate( + x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" + ) + else: + x = F.interpolate(x, scale_factor=2, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + + +class UpsampleSR600(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = nn.Conv2d(self.channels, self.out_channels, 3, padding=padding) + + def forward(self, x): + assert x.shape[1] == self.channels + if self.dims == 3: + x = F.interpolate( + x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" + ) + else: + x = F.interpolate(x, scale_factor=2, mode="nearest") + # TODO: to match input_blocks, remove elements of two sides + x = x[..., 1:-1, :] + if self.use_conv: + x = self.conv(x) + return x + + +class ResBlock(nn.Module): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param use_checkpoint: if True, use gradient checkpointing on this module. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + up=False, + down=False, + use_temporal_conv=True, + use_image_dataset=False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_scale_shift_norm = use_scale_shift_norm + self.use_temporal_conv = use_temporal_conv + + self.in_layers = nn.Sequential( + nn.GroupNorm(32, channels), + nn.SiLU(), + nn.Conv2d(channels, self.out_channels, 3, padding=1), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims) + self.x_upd = Upsample(channels, False, dims) + elif down: + self.h_upd = Downsample(channels, False, dims) + self.x_upd = Downsample(channels, False, dims) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.emb_layers = nn.Sequential( + nn.SiLU(), + nn.Linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, + ), + ) + self.out_layers = nn.Sequential( + nn.GroupNorm(32, self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = conv_nd( + dims, channels, self.out_channels, 3, padding=1 + ) + else: + self.skip_connection = nn.Conv2d(channels, self.out_channels, 1) + + if self.use_temporal_conv: + self.temopral_conv = TemporalConvBlock_v2(self.out_channels, self.out_channels, dropout=0.1, use_image_dataset=use_image_dataset) + # self.temopral_conv_2 = TemporalConvBlock(self.out_channels, self.out_channels, dropout=0.1, use_image_dataset=use_image_dataset) + + def forward(self, x, emb, batch_size): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + return self._forward(x, emb, batch_size) + + def _forward(self, x, emb, batch_size): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = th.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + h = self.skip_connection(x) + h + + if self.use_temporal_conv: + h = rearrange(h, '(b f) c h w -> b c f h w', b=batch_size) + h = self.temopral_conv(h) + # h = self.temopral_conv_2(h) + h = rearrange(h, 'b c f h w -> (b f) c h w') + return h + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = nn.Conv2d(self.channels, self.out_channels, 3, stride=stride, padding=padding) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + +class Resample(nn.Module): + + def __init__(self, in_dim, out_dim, mode): + assert mode in ['none', 'upsample', 'downsample'] + super(Resample, self).__init__() + self.in_dim = in_dim + self.out_dim = out_dim + self.mode = mode + + def forward(self, x, reference=None): + if self.mode == 'upsample': + assert reference is not None + x = F.interpolate(x, size=reference.shape[-2:], mode='nearest') + elif self.mode == 'downsample': + x = F.adaptive_avg_pool2d(x, output_size=tuple(u // 2 for u in x.shape[-2:])) + return x + +class ResidualBlock(nn.Module): + + def __init__(self, in_dim, embed_dim, out_dim, use_scale_shift_norm=True, + mode='none', dropout=0.0): + super(ResidualBlock, self).__init__() + self.in_dim = in_dim + self.embed_dim = embed_dim + self.out_dim = out_dim + self.use_scale_shift_norm = use_scale_shift_norm + self.mode = mode + + # layers + self.layer1 = nn.Sequential( + nn.GroupNorm(32, in_dim), + nn.SiLU(), + nn.Conv2d(in_dim, out_dim, 3, padding=1)) + self.resample = Resample(in_dim, in_dim, mode) + self.embedding = nn.Sequential( + nn.SiLU(), + nn.Linear(embed_dim, out_dim * 2 if use_scale_shift_norm else out_dim)) + self.layer2 = nn.Sequential( + nn.GroupNorm(32, out_dim), + nn.SiLU(), + nn.Dropout(dropout), + nn.Conv2d(out_dim, out_dim, 3, padding=1)) + self.shortcut = nn.Identity() if in_dim == out_dim else nn.Conv2d(in_dim, out_dim, 1) + + # zero out the last layer params + nn.init.zeros_(self.layer2[-1].weight) + + def forward(self, x, e, reference=None): + identity = self.resample(x, reference) + x = self.layer1[-1](self.resample(self.layer1[:-1](x), reference)) + e = self.embedding(e).unsqueeze(-1).unsqueeze(-1).type(x.dtype) + if self.use_scale_shift_norm: + scale, shift = e.chunk(2, dim=1) + x = self.layer2[0](x) * (1 + scale) + shift + x = self.layer2[1:](x) + else: + x = x + e + x = self.layer2(x) + x = x + self.shortcut(identity) + return x + +class AttentionBlock(nn.Module): + + def __init__(self, dim, context_dim=None, num_heads=None, head_dim=None): + # consider head_dim first, then num_heads + num_heads = dim // head_dim if head_dim else num_heads + head_dim = dim // num_heads + assert num_heads * head_dim == dim + super(AttentionBlock, self).__init__() + self.dim = dim + self.context_dim = context_dim + self.num_heads = num_heads + self.head_dim = head_dim + self.scale = math.pow(head_dim, -0.25) + + # layers + self.norm = nn.GroupNorm(32, dim) + self.to_qkv = nn.Conv2d(dim, dim * 3, 1) + if context_dim is not None: + self.context_kv = nn.Linear(context_dim, dim * 2) + self.proj = nn.Conv2d(dim, dim, 1) + + # zero out the last layer params + nn.init.zeros_(self.proj.weight) + + def forward(self, x, context=None): + r"""x: [B, C, H, W]. + context: [B, L, C] or None. + """ + identity = x + b, c, h, w, n, d = *x.size(), self.num_heads, self.head_dim + + # compute query, key, value + x = self.norm(x) + q, k, v = self.to_qkv(x).view(b, n * 3, d, h * w).chunk(3, dim=1) + if context is not None: + ck, cv = self.context_kv(context).reshape(b, -1, n * 2, d).permute(0, 2, 3, 1).chunk(2, dim=1) + k = torch.cat([ck, k], dim=-1) + v = torch.cat([cv, v], dim=-1) + + # compute attention + attn = torch.matmul(q.transpose(-1, -2) * self.scale, k * self.scale) + attn = F.softmax(attn, dim=-1) + + # gather context + x = torch.matmul(v, attn.transpose(-1, -2)) + x = x.reshape(b, c, h, w) + + # output + x = self.proj(x) + return x + identity + + +class TemporalAttentionBlock(nn.Module): + def __init__( + self, + dim, + heads = 4, + dim_head = 32, + rotary_emb = None, + use_image_dataset = False, + use_sim_mask = False + ): + super().__init__() + # consider num_heads first, as pos_bias needs fixed num_heads + # heads = dim // dim_head if dim_head else heads + dim_head = dim // heads + assert heads * dim_head == dim + self.use_image_dataset = use_image_dataset + self.use_sim_mask = use_sim_mask + + self.scale = dim_head ** -0.5 + self.heads = heads + hidden_dim = dim_head * heads + + self.norm = nn.GroupNorm(32, dim) + self.rotary_emb = rotary_emb + self.to_qkv = nn.Linear(dim, hidden_dim * 3)#, bias = False) + self.to_out = nn.Linear(hidden_dim, dim)#, bias = False) + + # nn.init.zeros_(self.to_out.weight) + # nn.init.zeros_(self.to_out.bias) + + def forward( + self, + x, + pos_bias = None, + focus_present_mask = None, + video_mask = None + ): + + identity = x + n, height, device = x.shape[2], x.shape[-2], x.device + + x = self.norm(x) + x = rearrange(x, 'b c f h w -> b (h w) f c') + + qkv = self.to_qkv(x).chunk(3, dim = -1) + + if exists(focus_present_mask) and focus_present_mask.all(): + # if all batch samples are focusing on present + # it would be equivalent to passing that token's values （v=qkv[-1]） through to the output + values = qkv[-1] + out = self.to_out(values) + out = rearrange(out, 'b (h w) f c -> b c f h w', h = height) + + return out + identity + + # split out heads + # q, k, v = rearrange_many(qkv, '... n (h d) -> ... h n d', h = self.heads) + # shape [b (hw) h n c/h], n=f + q= rearrange(qkv[0], '... n (h d) -> ... h n d', h = self.heads) + k= rearrange(qkv[1], '... n (h d) -> ... h n d', h = self.heads) + v= rearrange(qkv[2], '... n (h d) -> ... h n d', h = self.heads) + + + # scale + + q = q * self.scale + + # rotate positions into queries and keys for time attention + if exists(self.rotary_emb): + q = self.rotary_emb.rotate_queries_or_keys(q) + k = self.rotary_emb.rotate_queries_or_keys(k) + + # similarity + # shape [b (hw) h n n], n=f + sim = torch.einsum('... h i d, ... h j d -> ... h i j', q, k) + + # relative positional bias + + if exists(pos_bias): + # print(sim.shape,pos_bias.shape) + sim = sim + pos_bias + + if (focus_present_mask is None and video_mask is not None): + #video_mask: [B, n] + mask = video_mask[:, None, :] * video_mask[:, :, None] # [b,n,n] + mask = mask.unsqueeze(1).unsqueeze(1) #[b,1,1,n,n] + sim = sim.masked_fill(~mask, -torch.finfo(sim.dtype).max) + elif exists(focus_present_mask) and not (~focus_present_mask).all(): + attend_all_mask = torch.ones((n, n), device = device, dtype = torch.bool) + attend_self_mask = torch.eye(n, device = device, dtype = torch.bool) + + mask = torch.where( + rearrange(focus_present_mask, 'b -> b 1 1 1 1'), + rearrange(attend_self_mask, 'i j -> 1 1 1 i j'), + rearrange(attend_all_mask, 'i j -> 1 1 1 i j'), + ) + + sim = sim.masked_fill(~mask, -torch.finfo(sim.dtype).max) + + if self.use_sim_mask: + sim_mask = torch.tril(torch.ones((n, n), device = device, dtype = torch.bool), diagonal=0) + sim = sim.masked_fill(~sim_mask, -torch.finfo(sim.dtype).max) + + # numerical stability + sim = sim - sim.amax(dim = -1, keepdim = True).detach() + attn = sim.softmax(dim = -1) + + # aggregate values + + out = torch.einsum('... h i j, ... h j d -> ... h i d', attn, v) + out = rearrange(out, '... h n d -> ... n (h d)') + out = self.to_out(out) + + out = rearrange(out, 'b (h w) f c -> b c f h w', h = height) + + if self.use_image_dataset: + out = identity + 0*out + else: + out = identity + out + return out + +class TemporalTransformer(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + """ + def __init__(self, in_channels, n_heads, d_head, + depth=1, dropout=0., context_dim=None, + disable_self_attn=False, use_linear=False, + use_checkpoint=True, only_self_att=True, multiply_zero=False): + super().__init__() + self.multiply_zero = multiply_zero + self.only_self_att = only_self_att + self.use_adaptor = False + if self.only_self_att: + context_dim = None + if not isinstance(context_dim, list): + context_dim = [context_dim] + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + if not use_linear: + self.proj_in = nn.Conv1d(in_channels, + inner_dim, + kernel_size=1, + stride=1, + padding=0) + else: + self.proj_in = nn.Linear(in_channels, inner_dim) + if self.use_adaptor: + self.adaptor_in = nn.Linear(frames, frames) + + self.transformer_blocks = nn.ModuleList( + [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], + checkpoint=use_checkpoint) + for d in range(depth)] + ) + if not use_linear: + self.proj_out = zero_module(nn.Conv1d(inner_dim, + in_channels, + kernel_size=1, + stride=1, + padding=0)) + else: + self.proj_out = zero_module(nn.Linear(in_channels, inner_dim)) + if self.use_adaptor: + self.adaptor_out = nn.Linear(frames, frames) + self.use_linear = use_linear + + def forward(self, x, context=None): + # note: if no context is given, cross-attention defaults to self-attention + if self.only_self_att: + context = None + if not isinstance(context, list): + context = [context] + b, c, f, h, w = x.shape + x_in = x + x = self.norm(x) + + if not self.use_linear: + x = rearrange(x, 'b c f h w -> (b h w) c f').contiguous() + x = self.proj_in(x) + # [16384, 16, 320] + if self.use_linear: + x = rearrange(x, '(b f) c h w -> b (h w) f c', f=self.frames).contiguous() + x = self.proj_in(x) + + if self.only_self_att: + x = rearrange(x, 'bhw c f -> bhw f c').contiguous() + for i, block in enumerate(self.transformer_blocks): + x = block(x) + x = rearrange(x, '(b hw) f c -> b hw f c', b=b).contiguous() + else: + x = rearrange(x, '(b hw) c f -> b hw f c', b=b).contiguous() + for i, block in enumerate(self.transformer_blocks): + # context[i] = repeat(context[i], '(b f) l con -> b (f r) l con', r=(h*w)//self.frames, f=self.frames).contiguous() + context[i] = rearrange(context[i], '(b f) l con -> b f l con', f=self.frames).contiguous() + # calculate each batch one by one (since number in shape could not greater then 65,535 for some package) + for j in range(b): + context_i_j = repeat(context[i][j], 'f l con -> (f r) l con', r=(h*w)//self.frames, f=self.frames).contiguous() + x[j] = block(x[j], context=context_i_j) + + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, 'b (h w) f c -> b f c h w', h=h, w=w).contiguous() + if not self.use_linear: + # x = rearrange(x, 'bhw f c -> bhw c f').contiguous() + x = rearrange(x, 'b hw f c -> (b hw) c f').contiguous() + x = self.proj_out(x) + x = rearrange(x, '(b h w) c f -> b c f h w', b=b, h=h, w=w).contiguous() + + if self.multiply_zero: + x = 0.0 * x + x_in + else: + x = x + x_in + return x + + +class TemporalTransformerWithAdapter(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + """ + def __init__(self, in_channels, n_heads, d_head, + depth=1, dropout=0., context_dim=None, + disable_self_attn=False, use_linear=False, + use_checkpoint=True, only_self_att=True, multiply_zero=False, + adapter_list=[], adapter_position_list=['parallel', 'parallel', 'parallel'], + adapter_hidden_dim=None, adapter_condition_dim=None): + super().__init__() + self.multiply_zero = multiply_zero + self.only_self_att = only_self_att + self.use_adaptor = False + if self.only_self_att: + context_dim = None + if not isinstance(context_dim, list): + context_dim = [context_dim] + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + if not use_linear: + self.proj_in = nn.Conv1d(in_channels, + inner_dim, + kernel_size=1, + stride=1, + padding=0) + else: + self.proj_in = nn.Linear(in_channels, inner_dim) + if self.use_adaptor: + self.adaptor_in = nn.Linear(frames, frames) + + self.transformer_blocks = nn.ModuleList( + [BasicTransformerBlockWithAdapter(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], + checkpoint=use_checkpoint, adapter_list=adapter_list, adapter_position_list=adapter_position_list, + adapter_hidden_dim=adapter_hidden_dim, adapter_condition_dim=adapter_condition_dim) + for d in range(depth)] + ) + if not use_linear: + self.proj_out = zero_module(nn.Conv1d(inner_dim, + in_channels, + kernel_size=1, + stride=1, + padding=0)) + else: + self.proj_out = zero_module(nn.Linear(in_channels, inner_dim)) + if self.use_adaptor: + self.adaptor_out = nn.Linear(frames, frames) + self.use_linear = use_linear + + def forward(self, x, context=None, adapter_condition=None, adapter_condition_lam=1): + # note: if no context is given, cross-attention defaults to self-attention + if self.only_self_att: + context = None + if not isinstance(context, list): + context = [context] + b, c, f, h, w = x.shape + x_in = x + x = self.norm(x) + + if not self.use_linear: + x = rearrange(x, 'b c f h w -> (b h w) c f').contiguous() + x = self.proj_in(x) + # [16384, 16, 320] + if self.use_linear: + x = rearrange(x, '(b f) c h w -> b (h w) f c', f=self.frames).contiguous() + x = self.proj_in(x) + + if adapter_condition is not None: + b_cond, f_cond, c_cond = adapter_condition.shape + adapter_condition = adapter_condition.unsqueeze(1).unsqueeze(1).repeat(1, h, w, 1, 1) + adapter_condition = adapter_condition.reshape(b_cond*h*w, f_cond, c_cond) + + if self.only_self_att: + x = rearrange(x, 'bhw c f -> bhw f c').contiguous() + for i, block in enumerate(self.transformer_blocks): + x = block(x, adapter_condition=adapter_condition, adapter_condition_lam=adapter_condition_lam) + x = rearrange(x, '(b hw) f c -> b hw f c', b=b).contiguous() + else: + x = rearrange(x, '(b hw) c f -> b hw f c', b=b).contiguous() + for i, block in enumerate(self.transformer_blocks): + # context[i] = repeat(context[i], '(b f) l con -> b (f r) l con', r=(h*w)//self.frames, f=self.frames).contiguous() + context[i] = rearrange(context[i], '(b f) l con -> b f l con', f=self.frames).contiguous() + # calculate each batch one by one (since number in shape could not greater then 65,535 for some package) + for j in range(b): + context_i_j = repeat(context[i][j], 'f l con -> (f r) l con', r=(h*w)//self.frames, f=self.frames).contiguous() + x[j] = block(x[j], context=context_i_j) + + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, 'b (h w) f c -> b f c h w', h=h, w=w).contiguous() + if not self.use_linear: + # x = rearrange(x, 'bhw f c -> bhw c f').contiguous() + x = rearrange(x, 'b hw f c -> (b hw) c f').contiguous() + x = self.proj_out(x) + x = rearrange(x, '(b h w) c f -> b c f h w', b=b, h=h, w=w).contiguous() + + if self.multiply_zero: + x = 0.0 * x + x_in + else: + x = x + x_in + return x + +class Attention(nn.Module): + def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.): + super().__init__() + inner_dim = dim_head * heads + project_out = not (heads == 1 and dim_head == dim) + + self.heads = heads + self.scale = dim_head ** -0.5 + + self.attend = nn.Softmax(dim = -1) + self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False) + + self.to_out = nn.Sequential( + nn.Linear(inner_dim, dim), + nn.Dropout(dropout) + ) if project_out else nn.Identity() + + def forward(self, x): + b, n, _, h = *x.shape, self.heads + qkv = self.to_qkv(x).chunk(3, dim = -1) + q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv) + + dots = torch.einsum('b h i d, b h j d -> b h i j', q, k) * self.scale + + attn = self.attend(dots) + + out = torch.einsum('b h i j, b h j d -> b h i d', attn, v) + out = rearrange(out, 'b h n d -> b n (h d)') + return self.to_out(out) + +class PreNormattention(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.fn = fn + def forward(self, x, **kwargs): + return self.fn(self.norm(x), **kwargs) + x + +class TransformerV2(nn.Module): + def __init__(self, heads=8, dim=2048, dim_head_k=256, dim_head_v=256, dropout_atte = 0.05, mlp_dim=2048, dropout_ffn = 0.05, depth=1): + super().__init__() + self.layers = nn.ModuleList([]) + self.depth = depth + for _ in range(depth): + self.layers.append(nn.ModuleList([ + PreNormattention(dim, Attention(dim, heads = heads, dim_head = dim_head_k, dropout = dropout_atte)), + FeedForward(dim, mlp_dim, dropout = dropout_ffn), + ])) + def forward(self, x): + # if self.depth + for attn, ff in self.layers[:1]: + x = attn(x) + x = ff(x) + x + if self.depth > 1: + for attn, ff in self.layers[1:]: + x = attn(x) + x = ff(x) + x + return x + +class TemporalTransformer_attemask(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + """ + def __init__(self, in_channels, n_heads, d_head, + depth=1, dropout=0., context_dim=None, + disable_self_attn=False, use_linear=False, + use_checkpoint=True, only_self_att=True, multiply_zero=False): + super().__init__() + self.multiply_zero = multiply_zero + self.only_self_att = only_self_att + self.use_adaptor = False + if self.only_self_att: + context_dim = None + if not isinstance(context_dim, list): + context_dim = [context_dim] + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True) + if not use_linear: + self.proj_in = nn.Conv1d(in_channels, + inner_dim, + kernel_size=1, + stride=1, + padding=0) + else: + self.proj_in = nn.Linear(in_channels, inner_dim) + if self.use_adaptor: + self.adaptor_in = nn.Linear(frames, frames) + + self.transformer_blocks = nn.ModuleList( + [BasicTransformerBlock_attemask(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], + checkpoint=use_checkpoint) + for d in range(depth)] + ) + if not use_linear: + self.proj_out = zero_module(nn.Conv1d(inner_dim, + in_channels, + kernel_size=1, + stride=1, + padding=0)) + else: + self.proj_out = zero_module(nn.Linear(in_channels, inner_dim)) + if self.use_adaptor: + self.adaptor_out = nn.Linear(frames, frames) + self.use_linear = use_linear + + def forward(self, x, context=None): + # note: if no context is given, cross-attention defaults to self-attention + if self.only_self_att: + context = None + if not isinstance(context, list): + context = [context] + b, c, f, h, w = x.shape + x_in = x + x = self.norm(x) + + if not self.use_linear: + x = rearrange(x, 'b c f h w -> (b h w) c f').contiguous() + x = self.proj_in(x) + # [16384, 16, 320] + if self.use_linear: + x = rearrange(x, '(b f) c h w -> b (h w) f c', f=self.frames).contiguous() + x = self.proj_in(x) + + if self.only_self_att: + x = rearrange(x, 'bhw c f -> bhw f c').contiguous() + for i, block in enumerate(self.transformer_blocks): + x = block(x) + x = rearrange(x, '(b hw) f c -> b hw f c', b=b).contiguous() + else: + x = rearrange(x, '(b hw) c f -> b hw f c', b=b).contiguous() + for i, block in enumerate(self.transformer_blocks): + # context[i] = repeat(context[i], '(b f) l con -> b (f r) l con', r=(h*w)//self.frames, f=self.frames).contiguous() + context[i] = rearrange(context[i], '(b f) l con -> b f l con', f=self.frames).contiguous() + # calculate each batch one by one (since number in shape could not greater then 65,535 for some package) + for j in range(b): + context_i_j = repeat(context[i][j], 'f l con -> (f r) l con', r=(h*w)//self.frames, f=self.frames).contiguous() + x[j] = block(x[j], context=context_i_j) + + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, 'b (h w) f c -> b f c h w', h=h, w=w).contiguous() + if not self.use_linear: + # x = rearrange(x, 'bhw f c -> bhw c f').contiguous() + x = rearrange(x, 'b hw f c -> (b hw) c f').contiguous() + x = self.proj_out(x) + x = rearrange(x, '(b h w) c f -> b c f h w', b=b, h=h, w=w).contiguous() + + if self.multiply_zero: + x = 0.0 * x + x_in + else: + x = x + x_in + return x + +class TemporalAttentionMultiBlock(nn.Module): + def __init__( + self, + dim, + heads=4, + dim_head=32, + rotary_emb=None, + use_image_dataset=False, + use_sim_mask=False, + temporal_attn_times=1, + ): + super().__init__() + self.att_layers = nn.ModuleList( + [TemporalAttentionBlock(dim, heads, dim_head, rotary_emb, use_image_dataset, use_sim_mask) + for _ in range(temporal_attn_times)] + ) + + def forward( + self, + x, + pos_bias = None, + focus_present_mask = None, + video_mask = None + ): + for layer in self.att_layers: + x = layer(x, pos_bias, focus_present_mask, video_mask) + return x + + +class InitTemporalConvBlock(nn.Module): + + def __init__(self, in_dim, out_dim=None, dropout=0.0,use_image_dataset=False): + super(InitTemporalConvBlock, self).__init__() + if out_dim is None: + out_dim = in_dim#int(1.5*in_dim) + self.in_dim = in_dim + self.out_dim = out_dim + self.use_image_dataset = use_image_dataset + + # conv layers + self.conv = nn.Sequential( + nn.GroupNorm(32, out_dim), + nn.SiLU(), + nn.Dropout(dropout), + nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding = (1, 0, 0))) + + # zero out the last layer params,so the conv block is identity + # nn.init.zeros_(self.conv1[-1].weight) + # nn.init.zeros_(self.conv1[-1].bias) + nn.init.zeros_(self.conv[-1].weight) + nn.init.zeros_(self.conv[-1].bias) + + def forward(self, x): + identity = x + x = self.conv(x) + if self.use_image_dataset: + x = identity + 0*x + else: + x = identity + x + return x + +class TemporalConvBlock(nn.Module): + + def __init__(self, in_dim, out_dim=None, dropout=0.0, use_image_dataset= False): + super(TemporalConvBlock, self).__init__() + if out_dim is None: + out_dim = in_dim#int(1.5*in_dim) + self.in_dim = in_dim + self.out_dim = out_dim + self.use_image_dataset = use_image_dataset + + # conv layers + self.conv1 = nn.Sequential( + nn.GroupNorm(32, in_dim), + nn.SiLU(), + nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding = (1, 0, 0))) + self.conv2 = nn.Sequential( + nn.GroupNorm(32, out_dim), + nn.SiLU(), + nn.Dropout(dropout), + nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding = (1, 0, 0))) + + # zero out the last layer params,so the conv block is identity + # nn.init.zeros_(self.conv1[-1].weight) + # nn.init.zeros_(self.conv1[-1].bias) + nn.init.zeros_(self.conv2[-1].weight) + nn.init.zeros_(self.conv2[-1].bias) + + def forward(self, x): + identity = x + x = self.conv1(x) + x = self.conv2(x) + if self.use_image_dataset: + x = identity + 0*x + else: + x = identity + x + return x + +class TemporalConvBlock_v2(nn.Module): + def __init__(self, in_dim, out_dim=None, dropout=0.0, use_image_dataset=False): + super(TemporalConvBlock_v2, self).__init__() + if out_dim is None: + out_dim = in_dim # int(1.5*in_dim) + self.in_dim = in_dim + self.out_dim = out_dim + self.use_image_dataset = use_image_dataset + + # conv layers + self.conv1 = nn.Sequential( + nn.GroupNorm(32, in_dim), + nn.SiLU(), + nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding = (1, 0, 0))) + self.conv2 = nn.Sequential( + nn.GroupNorm(32, out_dim), + nn.SiLU(), + nn.Dropout(dropout), + nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding = (1, 0, 0))) + self.conv3 = nn.Sequential( + nn.GroupNorm(32, out_dim), + nn.SiLU(), + nn.Dropout(dropout), + nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding = (1, 0, 0))) + self.conv4 = nn.Sequential( + nn.GroupNorm(32, out_dim), + nn.SiLU(), + nn.Dropout(dropout), + nn.Conv3d(out_dim, in_dim, (3, 1, 1), padding = (1, 0, 0))) + + # zero out the last layer params,so the conv block is identity + nn.init.zeros_(self.conv4[-1].weight) + nn.init.zeros_(self.conv4[-1].bias) + + def forward(self, x): + identity = x + x = self.conv1(x) + x = self.conv2(x) + x = self.conv3(x) + x = self.conv4(x) + + if self.use_image_dataset: + x = identity + 0.0 * x + else: + x = identity + x + return x + + +class DropPath(nn.Module): + r"""DropPath but without rescaling and supports optional all-zero and/or all-keep. + """ + def __init__(self, p): + super(DropPath, self).__init__() + self.p = p + + def forward(self, *args, zero=None, keep=None): + if not self.training: + return args[0] if len(args) == 1 else args + + # params + x = args[0] + b = x.size(0) + n = (torch.rand(b) < self.p).sum() + + # non-zero and non-keep mask + mask = x.new_ones(b, dtype=torch.bool) + if keep is not None: + mask[keep] = False + if zero is not None: + mask[zero] = False + + # drop-path index + index = torch.where(mask)[0] + index = index[torch.randperm(len(index))[:n]] + if zero is not None: + index = torch.cat([index, torch.where(zero)[0]], dim=0) + + # drop-path multiplier + multiplier = x.new_ones(b) + multiplier[index] = 0.0 + output = tuple(u * self.broadcast(multiplier, u) for u in args) + return output[0] if len(args) == 1 else output + + def broadcast(self, src, dst): + assert src.size(0) == dst.size(0) + shape = (dst.size(0), ) + (1, ) * (dst.ndim - 1) + return src.view(shape) + + + diff --git a/animation/UniAnimate/utils/__init__.py b/animation/UniAnimate/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/animation/UniAnimate/utils/assign_cfg.py b/animation/UniAnimate/utils/assign_cfg.py new file mode 100644 index 0000000..e911a68 --- /dev/null +++ b/animation/UniAnimate/utils/assign_cfg.py @@ -0,0 +1,78 @@ +import os, yaml +from copy import deepcopy, copy + + +# def get prior and ldm config +def assign_prior_mudule_cfg(cfg): + ''' + ''' + # + prior_cfg = deepcopy(cfg) + vldm_cfg = deepcopy(cfg) + + with open(cfg.prior_cfg, 'r') as f: + _cfg_update = yaml.load(f.read(), Loader=yaml.SafeLoader) + # _cfg_update = _cfg_update.cfg_dict + for k, v in _cfg_update.items(): + if isinstance(v, dict) and k in cfg: + prior_cfg[k].update(v) + else: + prior_cfg[k] = v + + with open(cfg.vldm_cfg, 'r') as f: + _cfg_update = yaml.load(f.read(), Loader=yaml.SafeLoader) + # _cfg_update = _cfg_update.cfg_dict + for k, v in _cfg_update.items(): + if isinstance(v, dict) and k in cfg: + vldm_cfg[k].update(v) + else: + vldm_cfg[k] = v + + return prior_cfg, vldm_cfg + + +# def get prior and ldm config +def assign_vldm_vsr_mudule_cfg(cfg): + ''' + ''' + # + vldm_cfg = deepcopy(cfg) + vsr_cfg = deepcopy(cfg) + + with open(cfg.vldm_cfg, 'r') as f: + _cfg_update = yaml.load(f.read(), Loader=yaml.SafeLoader) + # _cfg_update = _cfg_update.cfg_dict + for k, v in _cfg_update.items(): + if isinstance(v, dict) and k in cfg: + vldm_cfg[k].update(v) + else: + vldm_cfg[k] = v + + with open(cfg.vsr_cfg, 'r') as f: + _cfg_update = yaml.load(f.read(), Loader=yaml.SafeLoader) + # _cfg_update = _cfg_update.cfg_dict + for k, v in _cfg_update.items(): + if isinstance(v, dict) and k in cfg: + vsr_cfg[k].update(v) + else: + vsr_cfg[k] = v + + return vldm_cfg, vsr_cfg + + +# def get prior and ldm config +def assign_signle_cfg(cfg, _cfg_update, tname): + ''' + ''' + # + vldm_cfg = deepcopy(cfg) + if os.path.exists(_cfg_update[tname]): + with open(_cfg_update[tname], 'r') as f: + _cfg_update = yaml.load(f.read(), Loader=yaml.SafeLoader) + # _cfg_update = _cfg_update.cfg_dict + for k, v in _cfg_update.items(): + if isinstance(v, dict) and k in cfg: + vldm_cfg[k].update(v) + else: + vldm_cfg[k] = v + return vldm_cfg \ No newline at end of file diff --git a/animation/UniAnimate/utils/config.py b/animation/UniAnimate/utils/config.py new file mode 100644 index 0000000..11ec422 --- /dev/null +++ b/animation/UniAnimate/utils/config.py @@ -0,0 +1,230 @@ +import os +import yaml +import json +import copy +import argparse + +import utils.logging as logging +logger = logging.get_logger(__name__) + +class Config(object): + def __init__(self, load=True, cfg_dict=None, cfg_level=None): + self._level = "cfg" + ("." + cfg_level if cfg_level is not None else "") + if load: + self.args = self._parse_args() + logger.info("Loading config from {}.".format(self.args.cfg_file)) + self.need_initialization = True + cfg_base = self._load_yaml(self.args) # self._initialize_cfg() + cfg_dict = self._load_yaml(self.args) + cfg_dict = self._merge_cfg_from_base(cfg_base, cfg_dict) + cfg_dict = self._update_from_args(cfg_dict) + self.cfg_dict = cfg_dict + self._update_dict(cfg_dict) + + def _parse_args(self): + parser = argparse.ArgumentParser( + description="Argparser for configuring [code base name to think of] codebase" + ) + parser.add_argument( + "--cfg", + dest="cfg_file", + help="Path to the configuration file", + default='configs/UniAnimate_infer.yaml' + ) + parser.add_argument( + "--init_method", + help="Initialization method, includes TCP or shared file-system", + default="tcp://localhost:9999", + type=str, + ) + parser.add_argument( + '--debug', + action='store_true', + default=False, + help='Into debug information' + ) + parser.add_argument( + "opts", + help="other configurations", + default=None, + nargs=argparse.REMAINDER) + return parser.parse_args() + + def _path_join(self, path_list): + path = "" + for p in path_list: + path+= p + '/' + return path[:-1] + + def _update_from_args(self, cfg_dict): + args = self.args + for var in vars(args): + cfg_dict[var] = getattr(args, var) + return cfg_dict + + def _initialize_cfg(self): + if self.need_initialization: + self.need_initialization = False + if os.path.exists('./configs/base.yaml'): + with open("./configs/base.yaml", 'r') as f: + cfg = yaml.load(f.read(), Loader=yaml.SafeLoader) + else: + with open(os.path.realpath(__file__).split('/')[-3] + "/configs/base.yaml", 'r') as f: + cfg = yaml.load(f.read(), Loader=yaml.SafeLoader) + return cfg + + def _load_yaml(self, args, file_name=""): + assert args.cfg_file is not None + if not file_name == "": # reading from base file + with open(file_name, 'r') as f: + cfg = yaml.load(f.read(), Loader=yaml.SafeLoader) + else: + if os.getcwd().split("/")[-1] == args.cfg_file.split("/")[0]: + args.cfg_file = args.cfg_file.replace(os.getcwd().split("/")[-1], "./") + with open(args.cfg_file, 'r') as f: + cfg = yaml.load(f.read(), Loader=yaml.SafeLoader) + file_name = args.cfg_file + + if "_BASE_RUN" not in cfg.keys() and "_BASE_MODEL" not in cfg.keys() and "_BASE" not in cfg.keys(): + # return cfg if the base file is being accessed + cfg = self._merge_cfg_from_command_update(args, cfg) + return cfg + + if "_BASE" in cfg.keys(): + if cfg["_BASE"][1] == '.': + prev_count = cfg["_BASE"].count('..') + cfg_base_file = self._path_join(file_name.split('/')[:(-1-cfg["_BASE"].count('..'))] + cfg["_BASE"].split('/')[prev_count:]) + else: + cfg_base_file = cfg["_BASE"].replace( + "./", + args.cfg_file.replace(args.cfg_file.split('/')[-1], "") + ) + cfg_base = self._load_yaml(args, cfg_base_file) + cfg = self._merge_cfg_from_base(cfg_base, cfg) + else: + if "_BASE_RUN" in cfg.keys(): + if cfg["_BASE_RUN"][1] == '.': + prev_count = cfg["_BASE_RUN"].count('..') + cfg_base_file = self._path_join(file_name.split('/')[:(-1-prev_count)] + cfg["_BASE_RUN"].split('/')[prev_count:]) + else: + cfg_base_file = cfg["_BASE_RUN"].replace( + "./", + args.cfg_file.replace(args.cfg_file.split('/')[-1], "") + ) + cfg_base = self._load_yaml(args, cfg_base_file) + cfg = self._merge_cfg_from_base(cfg_base, cfg, preserve_base=True) + if "_BASE_MODEL" in cfg.keys(): + if cfg["_BASE_MODEL"][1] == '.': + prev_count = cfg["_BASE_MODEL"].count('..') + cfg_base_file = self._path_join(file_name.split('/')[:(-1-cfg["_BASE_MODEL"].count('..'))] + cfg["_BASE_MODEL"].split('/')[prev_count:]) + else: + cfg_base_file = cfg["_BASE_MODEL"].replace( + "./", + args.cfg_file.replace(args.cfg_file.split('/')[-1], "") + ) + cfg_base = self._load_yaml(args, cfg_base_file) + cfg = self._merge_cfg_from_base(cfg_base, cfg) + cfg = self._merge_cfg_from_command(args, cfg) + return cfg + + def _merge_cfg_from_base(self, cfg_base, cfg_new, preserve_base=False): + for k,v in cfg_new.items(): + if k in cfg_base.keys(): + if isinstance(v, dict): + self._merge_cfg_from_base(cfg_base[k], v) + else: + cfg_base[k] = v + else: + if "BASE" not in k or preserve_base: + cfg_base[k] = v + return cfg_base + + def _merge_cfg_from_command_update(self, args, cfg): + if len(args.opts) == 0: + return cfg + + assert len(args.opts) % 2 == 0, 'Override list {} has odd length: {}.'.format( + args.opts, len(args.opts) + ) + keys = args.opts[0::2] + vals = args.opts[1::2] + + for key, val in zip(keys, vals): + cfg[key] = val + + return cfg + + def _merge_cfg_from_command(self, args, cfg): + assert len(args.opts) % 2 == 0, 'Override list {} has odd length: {}.'.format( + args.opts, len(args.opts) + ) + keys = args.opts[0::2] + vals = args.opts[1::2] + + # maximum supported depth 3 + for idx, key in enumerate(keys): + key_split = key.split('.') + assert len(key_split) <= 4, 'Key depth error. \nMaximum depth: 3\n Get depth: {}'.format( + len(key_split) + ) + assert key_split[0] in cfg.keys(), 'Non-existant key: {}.'.format( + key_split[0] + ) + if len(key_split) == 2: + assert key_split[1] in cfg[key_split[0]].keys(), 'Non-existant key: {}.'.format( + key + ) + elif len(key_split) == 3: + assert key_split[1] in cfg[key_split[0]].keys(), 'Non-existant key: {}.'.format( + key + ) + assert key_split[2] in cfg[key_split[0]][key_split[1]].keys(), 'Non-existant key: {}.'.format( + key + ) + elif len(key_split) == 4: + assert key_split[1] in cfg[key_split[0]].keys(), 'Non-existant key: {}.'.format( + key + ) + assert key_split[2] in cfg[key_split[0]][key_split[1]].keys(), 'Non-existant key: {}.'.format( + key + ) + assert key_split[3] in cfg[key_split[0]][key_split[1]][key_split[2]].keys(), 'Non-existant key: {}.'.format( + key + ) + if len(key_split) == 1: + cfg[key_split[0]] = vals[idx] + elif len(key_split) == 2: + cfg[key_split[0]][key_split[1]] = vals[idx] + elif len(key_split) == 3: + cfg[key_split[0]][key_split[1]][key_split[2]] = vals[idx] + elif len(key_split) == 4: + cfg[key_split[0]][key_split[1]][key_split[2]][key_split[3]] = vals[idx] + return cfg + + def _update_dict(self, cfg_dict): + def recur(key, elem): + if type(elem) is dict: + return key, Config(load=False, cfg_dict=elem, cfg_level=key) + else: + if type(elem) is str and elem[1:3]=="e-": + elem = float(elem) + return key, elem + dic = dict(recur(k, v) for k, v in cfg_dict.items()) + self.__dict__.update(dic) + + def get_args(self): + return self.args + + def __repr__(self): + return "{}\n".format(self.dump()) + + def dump(self): + return json.dumps(self.cfg_dict, indent=2) + + def deep_copy(self): + return copy.deepcopy(self) + +if __name__ == '__main__': + # debug + cfg = Config(load=True) + print(cfg.DATA) \ No newline at end of file diff --git a/animation/UniAnimate/utils/distributed.py b/animation/UniAnimate/utils/distributed.py new file mode 100644 index 0000000..cba28ba --- /dev/null +++ b/animation/UniAnimate/utils/distributed.py @@ -0,0 +1,430 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. + +import torch +import torch.nn.functional as F +import torch.distributed as dist +import functools +import pickle +import numpy as np +from collections import OrderedDict +from torch.autograd import Function + +__all__ = ['is_dist_initialized', + 'get_world_size', + 'get_rank', + 'new_group', + 'destroy_process_group', + 'barrier', + 'broadcast', + 'all_reduce', + 'reduce', + 'gather', + 'all_gather', + 'reduce_dict', + 'get_global_gloo_group', + 'generalized_all_gather', + 'generalized_gather', + 'scatter', + 'reduce_scatter', + 'send', + 'recv', + 'isend', + 'irecv', + 'shared_random_seed', + 'diff_all_gather', + 'diff_all_reduce', + 'diff_scatter', + 'diff_copy', + 'spherical_kmeans', + 'sinkhorn'] + +#-------------------------------- Distributed operations --------------------------------# + +def is_dist_initialized(): + return dist.is_available() and dist.is_initialized() + +def get_world_size(group=None): + return dist.get_world_size(group) if is_dist_initialized() else 1 + +def get_rank(group=None): + return dist.get_rank(group) if is_dist_initialized() else 0 + +def new_group(ranks=None, **kwargs): + if is_dist_initialized(): + return dist.new_group(ranks, **kwargs) + return None + +def destroy_process_group(): + if is_dist_initialized(): + dist.destroy_process_group() + +def barrier(group=None, **kwargs): + if get_world_size(group) > 1: + dist.barrier(group, **kwargs) + +def broadcast(tensor, src, group=None, **kwargs): + if get_world_size(group) > 1: + return dist.broadcast(tensor, src, group, **kwargs) + +def all_reduce(tensor, op=dist.ReduceOp.SUM, group=None, **kwargs): + if get_world_size(group) > 1: + return dist.all_reduce(tensor, op, group, **kwargs) + +def reduce(tensor, dst, op=dist.ReduceOp.SUM, group=None, **kwargs): + if get_world_size(group) > 1: + return dist.reduce(tensor, dst, op, group, **kwargs) + +def gather(tensor, dst=0, group=None, **kwargs): + rank = get_rank() # global rank + world_size = get_world_size(group) + if world_size == 1: + return [tensor] + tensor_list = [torch.empty_like(tensor) for _ in range(world_size)] if rank == dst else None + dist.gather(tensor, tensor_list, dst, group, **kwargs) + return tensor_list + +def all_gather(tensor, uniform_size=True, group=None, **kwargs): + world_size = get_world_size(group) + if world_size == 1: + return [tensor] + assert tensor.is_contiguous(), 'ops.all_gather requires the tensor to be contiguous()' + + if uniform_size: + tensor_list = [torch.empty_like(tensor) for _ in range(world_size)] + dist.all_gather(tensor_list, tensor, group, **kwargs) + return tensor_list + else: + # collect tensor shapes across GPUs + shape = tuple(tensor.shape) + shape_list = generalized_all_gather(shape, group) + + # flatten the tensor + tensor = tensor.reshape(-1) + size = int(np.prod(shape)) + size_list = [int(np.prod(u)) for u in shape_list] + max_size = max(size_list) + + # pad to maximum size + if size != max_size: + padding = tensor.new_zeros(max_size - size) + tensor = torch.cat([tensor, padding], dim=0) + + # all_gather + tensor_list = [torch.empty_like(tensor) for _ in range(world_size)] + dist.all_gather(tensor_list, tensor, group, **kwargs) + + # reshape tensors + tensor_list = [t[:n].view(s) for t, n, s in zip( + tensor_list, size_list, shape_list)] + return tensor_list + +@torch.no_grad() +def reduce_dict(input_dict, group=None, reduction='mean', **kwargs): + assert reduction in ['mean', 'sum'] + world_size = get_world_size(group) + if world_size == 1: + return input_dict + + # ensure that the orders of keys are consistent across processes + if isinstance(input_dict, OrderedDict): + keys = list(input_dict.keys) + else: + keys = sorted(input_dict.keys()) + vals = [input_dict[key] for key in keys] + vals = torch.stack(vals, dim=0) + dist.reduce(vals, dst=0, group=group, **kwargs) + if dist.get_rank(group) == 0 and reduction == 'mean': + vals /= world_size + dist.broadcast(vals, src=0, group=group, **kwargs) + reduced_dict = type(input_dict)([ + (key, val) for key, val in zip(keys, vals)]) + return reduced_dict + +@functools.lru_cache() +def get_global_gloo_group(): + backend = dist.get_backend() + assert backend in ['gloo', 'nccl'] + if backend == 'nccl': + return dist.new_group(backend='gloo') + else: + return dist.group.WORLD + +def _serialize_to_tensor(data, group): + backend = dist.get_backend(group) + assert backend in ['gloo', 'nccl'] + device = torch.device('cpu' if backend == 'gloo' else 'cuda') + + buffer = pickle.dumps(data) + if len(buffer) > 1024 ** 3: + logger = logging.getLogger(__name__) + logger.warning( + 'Rank {} trying to all-gather {:.2f} GB of data on device' + '{}'.format(get_rank(), len(buffer) / (1024 ** 3), device)) + storage = torch.ByteStorage.from_buffer(buffer) + tensor = torch.ByteTensor(storage).to(device=device) + return tensor + +def _pad_to_largest_tensor(tensor, group): + world_size = dist.get_world_size(group=group) + assert world_size >= 1, \ + 'gather/all_gather must be called from ranks within' \ + 'the give group!' + local_size = torch.tensor( + [tensor.numel()], dtype=torch.int64, device=tensor.device) + size_list = [torch.zeros( + [1], dtype=torch.int64, device=tensor.device) + for _ in range(world_size)] + + # gather tensors and compute the maximum size + dist.all_gather(size_list, local_size, group=group) + size_list = [int(size.item()) for size in size_list] + max_size = max(size_list) + + # pad tensors to the same size + if local_size != max_size: + padding = torch.zeros( + (max_size - local_size, ), + dtype=torch.uint8, device=tensor.device) + tensor = torch.cat((tensor, padding), dim=0) + return size_list, tensor + +def generalized_all_gather(data, group=None): + if get_world_size(group) == 1: + return [data] + if group is None: + group = get_global_gloo_group() + + tensor = _serialize_to_tensor(data, group) + size_list, tensor = _pad_to_largest_tensor(tensor, group) + max_size = max(size_list) + + # receiving tensors from all ranks + tensor_list = [torch.empty( + (max_size, ), dtype=torch.uint8, device=tensor.device) + for _ in size_list] + dist.all_gather(tensor_list, tensor, group=group) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + return data_list + +def generalized_gather(data, dst=0, group=None): + world_size = get_world_size(group) + if world_size == 1: + return [data] + if group is None: + group = get_global_gloo_group() + rank = dist.get_rank() # global rank + + tensor = _serialize_to_tensor(data, group) + size_list, tensor = _pad_to_largest_tensor(tensor, group) + + # receiving tensors from all ranks to dst + if rank == dst: + max_size = max(size_list) + tensor_list = [torch.empty( + (max_size, ), dtype=torch.uint8, device=tensor.device) + for _ in size_list] + dist.gather(tensor, tensor_list, dst=dst, group=group) + + data_list = [] + for size, tensor in zip(size_list, tensor_list): + buffer = tensor.cpu().numpy().tobytes()[:size] + data_list.append(pickle.loads(buffer)) + return data_list + else: + dist.gather(tensor, [], dst=dst, group=group) + return [] + +def scatter(data, scatter_list=None, src=0, group=None, **kwargs): + r"""NOTE: only supports CPU tensor communication. + """ + if get_world_size(group) > 1: + return dist.scatter(data, scatter_list, src, group, **kwargs) + +def reduce_scatter(output, input_list, op=dist.ReduceOp.SUM, group=None, **kwargs): + if get_world_size(group) > 1: + return dist.reduce_scatter(output, input_list, op, group, **kwargs) + +def send(tensor, dst, group=None, **kwargs): + if get_world_size(group) > 1: + assert tensor.is_contiguous(), 'ops.send requires the tensor to be contiguous()' + return dist.send(tensor, dst, group, **kwargs) + +def recv(tensor, src=None, group=None, **kwargs): + if get_world_size(group) > 1: + assert tensor.is_contiguous(), 'ops.recv requires the tensor to be contiguous()' + return dist.recv(tensor, src, group, **kwargs) + +def isend(tensor, dst, group=None, **kwargs): + if get_world_size(group) > 1: + assert tensor.is_contiguous(), 'ops.isend requires the tensor to be contiguous()' + return dist.isend(tensor, dst, group, **kwargs) + +def irecv(tensor, src=None, group=None, **kwargs): + if get_world_size(group) > 1: + assert tensor.is_contiguous(), 'ops.irecv requires the tensor to be contiguous()' + return dist.irecv(tensor, src, group, **kwargs) + +def shared_random_seed(group=None): + seed = np.random.randint(2 ** 31) + all_seeds = generalized_all_gather(seed, group) + return all_seeds[0] + +#-------------------------------- Differentiable operations --------------------------------# + +def _all_gather(x): + if not (dist.is_available() and dist.is_initialized()) or dist.get_world_size() == 1: + return x + rank = dist.get_rank() + world_size = dist.get_world_size() + tensors = [torch.empty_like(x) for _ in range(world_size)] + tensors[rank] = x + dist.all_gather(tensors, x) + return torch.cat(tensors, dim=0).contiguous() + +def _all_reduce(x): + if not (dist.is_available() and dist.is_initialized()) or dist.get_world_size() == 1: + return x + dist.all_reduce(x) + return x + +def _split(x): + if not (dist.is_available() and dist.is_initialized()) or dist.get_world_size() == 1: + return x + rank = dist.get_rank() + world_size = dist.get_world_size() + return x.chunk(world_size, dim=0)[rank].contiguous() + +class DiffAllGather(Function): + r"""Differentiable all-gather. + """ + @staticmethod + def symbolic(graph, input): + return _all_gather(input) + + @staticmethod + def forward(ctx, input): + return _all_gather(input) + + @staticmethod + def backward(ctx, grad_output): + return _split(grad_output) + +class DiffAllReduce(Function): + r"""Differentiable all-reducd. + """ + @staticmethod + def symbolic(graph, input): + return _all_reduce(input) + + @staticmethod + def forward(ctx, input): + return _all_reduce(input) + + @staticmethod + def backward(ctx, grad_output): + return grad_output + +class DiffScatter(Function): + r"""Differentiable scatter. + """ + @staticmethod + def symbolic(graph, input): + return _split(input) + + @staticmethod + def symbolic(ctx, input): + return _split(input) + + @staticmethod + def backward(ctx, grad_output): + return _all_gather(grad_output) + +class DiffCopy(Function): + r"""Differentiable copy that reduces all gradients during backward. + """ + @staticmethod + def symbolic(graph, input): + return input + + @staticmethod + def forward(ctx, input): + return input + + @staticmethod + def backward(ctx, grad_output): + return _all_reduce(grad_output) + +diff_all_gather = DiffAllGather.apply +diff_all_reduce = DiffAllReduce.apply +diff_scatter = DiffScatter.apply +diff_copy = DiffCopy.apply + +#-------------------------------- Distributed algorithms --------------------------------# + +@torch.no_grad() +def spherical_kmeans(feats, num_clusters, num_iters=10): + k, n, c = num_clusters, *feats.size() + ones = feats.new_ones(n, dtype=torch.long) + + # distributed settings + rank = get_rank() + world_size = get_world_size() + + # init clusters + rand_inds = torch.randperm(n)[:int(np.ceil(k / world_size))] + clusters = torch.cat(all_gather(feats[rand_inds]), dim=0)[:k] + + # variables + new_clusters = feats.new_zeros(k, c) + counts = feats.new_zeros(k, dtype=torch.long) + + # iterative Expectation-Maximization + for step in range(num_iters + 1): + # Expectation step + simmat = torch.mm(feats, clusters.t()) + scores, assigns = simmat.max(dim=1) + if step == num_iters: + break + + # Maximization step + new_clusters.zero_().scatter_add_(0, assigns.unsqueeze(1).repeat(1, c), feats) + all_reduce(new_clusters) + + counts.zero_() + counts.index_add_(0, assigns, ones) + all_reduce(counts) + + mask = (counts > 0) + clusters[mask] = new_clusters[mask] / counts[mask].view(-1, 1) + clusters = F.normalize(clusters, p=2, dim=1) + return clusters, assigns, scores + +@torch.no_grad() +def sinkhorn(Q, eps=0.5, num_iters=3): + # normalize Q + Q = torch.exp(Q / eps).t() + sum_Q = Q.sum() + all_reduce(sum_Q) + Q /= sum_Q + + # variables + n, m = Q.size() + u = Q.new_zeros(n) + r = Q.new_ones(n) / n + c = Q.new_ones(m) / (m * get_world_size()) + + # iterative update + cur_sum = Q.sum(dim=1) + all_reduce(cur_sum) + for i in range(num_iters): + u = cur_sum + Q *= (r / u).unsqueeze(1) + Q *= (c / Q.sum(dim=0)).unsqueeze(0) + cur_sum = Q.sum(dim=1) + all_reduce(cur_sum) + return (Q / Q.sum(dim=0, keepdim=True)).t().float() diff --git a/animation/UniAnimate/utils/logging.py b/animation/UniAnimate/utils/logging.py new file mode 100644 index 0000000..400a779 --- /dev/null +++ b/animation/UniAnimate/utils/logging.py @@ -0,0 +1,90 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. + +"""Logging.""" + +import builtins +import decimal +import functools +import logging +import os +import sys +import simplejson +# from fvcore.common.file_io import PathManager + +import utils.distributed as du + + +def _suppress_print(): + """ + Suppresses printing from the current process. + """ + + def print_pass(*objects, sep=" ", end="\n", file=sys.stdout, flush=False): + pass + + builtins.print = print_pass + + +# @functools.lru_cache(maxsize=None) +# def _cached_log_stream(filename): +# return PathManager.open(filename, "a") + + +def setup_logging(cfg, log_file): + """ + Sets up the logging for multiple processes. Only enable the logging for the + master process, and suppress logging for the non-master processes. + """ + if du.is_master_proc(): + # Enable logging for the master process. + logging.root.handlers = [] + else: + # Suppress logging for non-master processes. + _suppress_print() + + logger = logging.getLogger() + logger.setLevel(logging.INFO) + logger.propagate = False + plain_formatter = logging.Formatter( + "[%(asctime)s][%(levelname)s] %(name)s: %(lineno)4d: %(message)s", + datefmt="%m/%d %H:%M:%S", + ) + + if du.is_master_proc(): + ch = logging.StreamHandler(stream=sys.stdout) + ch.setLevel(logging.DEBUG) + ch.setFormatter(plain_formatter) + logger.addHandler(ch) + + if log_file is not None and du.is_master_proc(du.get_world_size()): + filename = os.path.join(cfg.OUTPUT_DIR, log_file) + fh = logging.FileHandler(filename) + fh.setLevel(logging.DEBUG) + fh.setFormatter(plain_formatter) + logger.addHandler(fh) + + +def get_logger(name): + """ + Retrieve the logger with the specified name or, if name is None, return a + logger which is the root logger of the hierarchy. + Args: + name (string): name of the logger. + """ + return logging.getLogger(name) + + +def log_json_stats(stats): + """ + Logs json stats. + Args: + stats (dict): a dictionary of statistical information to log. + """ + stats = { + k: decimal.Decimal("{:.6f}".format(v)) if isinstance(v, float) else v + for k, v in stats.items() + } + json_stats = simplejson.dumps(stats, sort_keys=True, use_decimal=True) + logger = get_logger(__name__) + logger.info("{:s}".format(json_stats)) diff --git a/animation/UniAnimate/utils/mp4_to_gif.py b/animation/UniAnimate/utils/mp4_to_gif.py new file mode 100644 index 0000000..5c85921 --- /dev/null +++ b/animation/UniAnimate/utils/mp4_to_gif.py @@ -0,0 +1,16 @@ +import os + + + +# source_mp4_dir = "outputs/UniAnimate_infer" +# target_gif_dir = "outputs/UniAnimate_infer_gif" + +source_mp4_dir = "outputs/UniAnimate_infer_long" +target_gif_dir = "outputs/UniAnimate_infer_long_gif" + +os.makedirs(target_gif_dir, exist_ok=True) +for video in os.listdir(source_mp4_dir): + video_dir = os.path.join(source_mp4_dir, video) + gif_dir = os.path.join(target_gif_dir, video.replace(".mp4", ".gif")) + cmd = f'ffmpeg -i {video_dir} {gif_dir}' + os.system(cmd) \ No newline at end of file diff --git a/animation/UniAnimate/utils/multi_port.py b/animation/UniAnimate/utils/multi_port.py new file mode 100644 index 0000000..1542056 --- /dev/null +++ b/animation/UniAnimate/utils/multi_port.py @@ -0,0 +1,9 @@ +import socket +from contextlib import closing + +def find_free_port(): + """ https://stackoverflow.com/questions/1365265/on-localhost-how-do-i-pick-a-free-port-number """ + with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s: + s.bind(('', 0)) + s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + return str(s.getsockname()[1]) \ No newline at end of file diff --git a/animation/UniAnimate/utils/optim/__init__.py b/animation/UniAnimate/utils/optim/__init__.py new file mode 100644 index 0000000..ffb9c49 --- /dev/null +++ b/animation/UniAnimate/utils/optim/__init__.py @@ -0,0 +1,2 @@ +from .lr_scheduler import * +from .adafactor import * diff --git a/animation/UniAnimate/utils/optim/adafactor.py b/animation/UniAnimate/utils/optim/adafactor.py new file mode 100644 index 0000000..63fee95 --- /dev/null +++ b/animation/UniAnimate/utils/optim/adafactor.py @@ -0,0 +1,230 @@ +import math +import torch +from torch.optim import Optimizer +from torch.optim.lr_scheduler import LambdaLR + +__all__ = ['Adafactor'] + +class Adafactor(Optimizer): + """ + AdaFactor pytorch implementation can be used as a drop in replacement for Adam original fairseq code: + https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py + Paper: *Adafactor: Adaptive Learning Rates with Sublinear Memory Cost* https://arxiv.org/abs/1804.04235 Note that + this optimizer internally adjusts the learning rate depending on the `scale_parameter`, `relative_step` and + `warmup_init` options. To use a manual (external) learning rate schedule you should set `scale_parameter=False` and + `relative_step=False`. + Arguments: + params (`Iterable[nn.parameter.Parameter]`): + Iterable of parameters to optimize or dictionaries defining parameter groups. + lr (`float`, *optional*): + The external learning rate. + eps (`Tuple[float, float]`, *optional*, defaults to (1e-30, 1e-3)): + Regularization constants for square gradient and parameter scale respectively + clip_threshold (`float`, *optional*, defaults 1.0): + Threshold of root mean square of final gradient update + decay_rate (`float`, *optional*, defaults to -0.8): + Coefficient used to compute running averages of square + beta1 (`float`, *optional*): + Coefficient used for computing running averages of gradient + weight_decay (`float`, *optional*, defaults to 0): + Weight decay (L2 penalty) + scale_parameter (`bool`, *optional*, defaults to `True`): + If True, learning rate is scaled by root mean square + relative_step (`bool`, *optional*, defaults to `True`): + If True, time-dependent learning rate is computed instead of external learning rate + warmup_init (`bool`, *optional*, defaults to `False`): + Time-dependent learning rate computation depends on whether warm-up initialization is being used + This implementation handles low-precision (FP16, bfloat) values, but we have not thoroughly tested. + Recommended T5 finetuning settings (https://discuss.huggingface.co/t/t5-finetuning-tips/684/3): + - Training without LR warmup or clip_threshold is not recommended. + - use scheduled LR warm-up to fixed LR + - use clip_threshold=1.0 (https://arxiv.org/abs/1804.04235) + - Disable relative updates + - Use scale_parameter=False + - Additional optimizer operations like gradient clipping should not be used alongside Adafactor + Example: + ```python + Adafactor(model.parameters(), scale_parameter=False, relative_step=False, warmup_init=False, lr=1e-3) + ``` + Others reported the following combination to work well: + ```python + Adafactor(model.parameters(), scale_parameter=True, relative_step=True, warmup_init=True, lr=None) + ``` + When using `lr=None` with [`Trainer`] you will most likely need to use [`~optimization.AdafactorSchedule`] + scheduler as following: + ```python + from transformers.optimization import Adafactor, AdafactorSchedule + optimizer = Adafactor(model.parameters(), scale_parameter=True, relative_step=True, warmup_init=True, lr=None) + lr_scheduler = AdafactorSchedule(optimizer) + trainer = Trainer(..., optimizers=(optimizer, lr_scheduler)) + ``` + Usage: + ```python + # replace AdamW with Adafactor + optimizer = Adafactor( + model.parameters(), + lr=1e-3, + eps=(1e-30, 1e-3), + clip_threshold=1.0, + decay_rate=-0.8, + beta1=None, + weight_decay=0.0, + relative_step=False, + scale_parameter=False, + warmup_init=False, + ) + ```""" + + def __init__( + self, + params, + lr=None, + eps=(1e-30, 1e-3), + clip_threshold=1.0, + decay_rate=-0.8, + beta1=None, + weight_decay=0.0, + scale_parameter=True, + relative_step=True, + warmup_init=False, + ): + r"""require_version("torch>=1.5.0") # add_ with alpha + """ + if lr is not None and relative_step: + raise ValueError("Cannot combine manual `lr` and `relative_step=True` options") + if warmup_init and not relative_step: + raise ValueError("`warmup_init=True` requires `relative_step=True`") + + defaults = dict( + lr=lr, + eps=eps, + clip_threshold=clip_threshold, + decay_rate=decay_rate, + beta1=beta1, + weight_decay=weight_decay, + scale_parameter=scale_parameter, + relative_step=relative_step, + warmup_init=warmup_init, + ) + super().__init__(params, defaults) + + @staticmethod + def _get_lr(param_group, param_state): + rel_step_sz = param_group["lr"] + if param_group["relative_step"]: + min_step = 1e-6 * param_state["step"] if param_group["warmup_init"] else 1e-2 + rel_step_sz = min(min_step, 1.0 / math.sqrt(param_state["step"])) + param_scale = 1.0 + if param_group["scale_parameter"]: + param_scale = max(param_group["eps"][1], param_state["RMS"]) + return param_scale * rel_step_sz + + @staticmethod + def _get_options(param_group, param_shape): + factored = len(param_shape) >= 2 + use_first_moment = param_group["beta1"] is not None + return factored, use_first_moment + + @staticmethod + def _rms(tensor): + return tensor.norm(2) / (tensor.numel() ** 0.5) + + @staticmethod + def _approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col): + # copy from fairseq's adafactor implementation: + # https://github.com/huggingface/transformers/blob/8395f14de6068012787d83989c3627c3df6a252b/src/transformers/optimization.py#L505 + r_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_().unsqueeze(-1) + c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt() + return torch.mul(r_factor, c_factor) + + def step(self, closure=None): + """ + Performs a single optimization step + Arguments: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + for p in group["params"]: + if p.grad is None: + continue + grad = p.grad.data + if grad.dtype in {torch.float16, torch.bfloat16}: + grad = grad.float() + if grad.is_sparse: + raise RuntimeError("Adafactor does not support sparse gradients.") + + state = self.state[p] + grad_shape = grad.shape + + factored, use_first_moment = self._get_options(group, grad_shape) + # State Initialization + if len(state) == 0: + state["step"] = 0 + + if use_first_moment: + # Exponential moving average of gradient values + state["exp_avg"] = torch.zeros_like(grad) + if factored: + state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad) + state["exp_avg_sq_col"] = torch.zeros(grad_shape[:-2] + grad_shape[-1:]).to(grad) + else: + state["exp_avg_sq"] = torch.zeros_like(grad) + + state["RMS"] = 0 + else: + if use_first_moment: + state["exp_avg"] = state["exp_avg"].to(grad) + if factored: + state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad) + state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad) + else: + state["exp_avg_sq"] = state["exp_avg_sq"].to(grad) + + p_data_fp32 = p.data + if p.data.dtype in {torch.float16, torch.bfloat16}: + p_data_fp32 = p_data_fp32.float() + + state["step"] += 1 + state["RMS"] = self._rms(p_data_fp32) + lr = self._get_lr(group, state) + + beta2t = 1.0 - math.pow(state["step"], group["decay_rate"]) + update = (grad**2) + group["eps"][0] + if factored: + exp_avg_sq_row = state["exp_avg_sq_row"] + exp_avg_sq_col = state["exp_avg_sq_col"] + + exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=(1.0 - beta2t)) + exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=(1.0 - beta2t)) + + # Approximation of exponential moving average of square of gradient + update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col) + update.mul_(grad) + else: + exp_avg_sq = state["exp_avg_sq"] + + exp_avg_sq.mul_(beta2t).add_(update, alpha=(1.0 - beta2t)) + update = exp_avg_sq.rsqrt().mul_(grad) + + update.div_((self._rms(update) / group["clip_threshold"]).clamp_(min=1.0)) + update.mul_(lr) + + if use_first_moment: + exp_avg = state["exp_avg"] + exp_avg.mul_(group["beta1"]).add_(update, alpha=(1 - group["beta1"])) + update = exp_avg + + if group["weight_decay"] != 0: + p_data_fp32.add_(p_data_fp32, alpha=(-group["weight_decay"] * lr)) + + p_data_fp32.add_(-update) + + if p.data.dtype in {torch.float16, torch.bfloat16}: + p.data.copy_(p_data_fp32) + + return loss diff --git a/animation/UniAnimate/utils/optim/lr_scheduler.py b/animation/UniAnimate/utils/optim/lr_scheduler.py new file mode 100644 index 0000000..20eba6d --- /dev/null +++ b/animation/UniAnimate/utils/optim/lr_scheduler.py @@ -0,0 +1,58 @@ +import math +from torch.optim.lr_scheduler import _LRScheduler + +__all__ = ['AnnealingLR'] + +class AnnealingLR(_LRScheduler): + + def __init__(self, optimizer, base_lr, warmup_steps, total_steps, decay_mode='cosine', min_lr=0.0, last_step=-1): + assert decay_mode in ['linear', 'cosine', 'none'] + self.optimizer = optimizer + self.base_lr = base_lr + self.warmup_steps = warmup_steps + self.total_steps = total_steps + self.decay_mode = decay_mode + self.min_lr = min_lr + self.current_step = last_step + 1 + self.step(self.current_step) + + def get_lr(self): + if self.warmup_steps > 0 and self.current_step <= self.warmup_steps: + return self.base_lr * self.current_step / self.warmup_steps + else: + ratio = (self.current_step - self.warmup_steps) / (self.total_steps - self.warmup_steps) + ratio = min(1.0, max(0.0, ratio)) + if self.decay_mode == 'linear': + return self.base_lr * (1 - ratio) + elif self.decay_mode == 'cosine': + return self.base_lr * (math.cos(math.pi * ratio) + 1.0) / 2.0 + else: + return self.base_lr + + def step(self, current_step=None): + if current_step is None: + current_step = self.current_step + 1 + self.current_step = current_step + new_lr = max(self.min_lr, self.get_lr()) + if isinstance(self.optimizer, list): + for o in self.optimizer: + for group in o.param_groups: + group['lr'] = new_lr + else: + for group in self.optimizer.param_groups: + group['lr'] = new_lr + + def state_dict(self): + return { + 'base_lr': self.base_lr, + 'warmup_steps': self.warmup_steps, + 'total_steps': self.total_steps, + 'decay_mode': self.decay_mode, + 'current_step': self.current_step} + + def load_state_dict(self, state_dict): + self.base_lr = state_dict['base_lr'] + self.warmup_steps = state_dict['warmup_steps'] + self.total_steps = state_dict['total_steps'] + self.decay_mode = state_dict['decay_mode'] + self.current_step = state_dict['current_step'] diff --git a/animation/UniAnimate/utils/registry.py b/animation/UniAnimate/utils/registry.py new file mode 100644 index 0000000..22f8c94 --- /dev/null +++ b/animation/UniAnimate/utils/registry.py @@ -0,0 +1,167 @@ +# Copyright 2021 Alibaba Group Holding Limited. All Rights Reserved. + +# Registry class & build_from_config function partially modified from +# https://github.com/open-mmlab/mmcv/blob/master/mmcv/utils/registry.py +# Copyright 2018-2020 Open-MMLab. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import copy +import inspect +import warnings + + +def build_from_config(cfg, registry, **kwargs): + """ Default builder function. + + Args: + cfg (dict): A dict which contains parameters passes to target class or function. + Must contains key 'type', indicates the target class or function name. + registry (Registry): An registry to search target class or function. + kwargs (dict, optional): Other params not in config dict. + + Returns: + Target class object or object returned by invoking function. + + Raises: + TypeError: + KeyError: + Exception: + """ + if not isinstance(cfg, dict): + raise TypeError(f"config must be type dict, got {type(cfg)}") + if "type" not in cfg: + raise KeyError(f"config must contain key type, got {cfg}") + if not isinstance(registry, Registry): + raise TypeError(f"registry must be type Registry, got {type(registry)}") + + cfg = copy.deepcopy(cfg) + + req_type = cfg.pop("type") + req_type_entry = req_type + if isinstance(req_type, str): + req_type_entry = registry.get(req_type) + if req_type_entry is None: + try: + print(f"For Windows users, we explicitly import registry function {req_type} !!!") + from tools.inferences.inference_unianimate_entrance import inference_unianimate_entrance + from tools.inferences.inference_unianimate_long_entrance import inference_unianimate_long_entrance + from tools.modules.diffusions.diffusion_ddim import DiffusionDDIM + from tools.modules.diffusions.diffusion_ddim import DiffusionDDIMLong + from tools.modules.autoencoder import AutoencoderKL + from tools.modules.clip_embedder import FrozenOpenCLIPTextVisualEmbedder + from tools.modules.unet.unet_unianimate import UNetSD_UniAnimate + + req_type_entry = eval(req_type) + except: + raise KeyError(f"{req_type} not found in {registry.name} registry") + + if kwargs is not None: + cfg.update(kwargs) + + if inspect.isclass(req_type_entry): + try: + return req_type_entry(**cfg) + except Exception as e: + raise Exception(f"Failed to init class {req_type_entry}, with {e}") + elif inspect.isfunction(req_type_entry): + try: + return req_type_entry(**cfg) + except Exception as e: + raise Exception(f"Failed to invoke function {req_type_entry}, with {e}") + else: + raise TypeError(f"type must be str or class, got {type(req_type_entry)}") + + +class Registry(object): + """ A registry maps key to classes or functions. + + Example: + >>> MODELS = Registry('MODELS') + >>> @MODELS.register_class() + >>> class ResNet(object): + >>> pass + >>> resnet = MODELS.build(dict(type="ResNet")) + >>> + >>> import torchvision + >>> @MODELS.register_function("InceptionV3") + >>> def get_inception_v3(pretrained=False, progress=True): + >>> return torchvision.models.inception_v3(pretrained=pretrained, progress=progress) + >>> inception_v3 = MODELS.build(dict(type='InceptionV3', pretrained=True)) + + Args: + name (str): Registry name. + build_func (func, None): Instance construct function. Default is build_from_config. + allow_types (tuple): Indicates how to construct the instance, by constructing class or invoking function. + """ + + def __init__(self, name, build_func=None, allow_types=("class", "function")): + self.name = name + self.allow_types = allow_types + self.class_map = {} + self.func_map = {} + self.build_func = build_func or build_from_config + + def get(self, req_type): + return self.class_map.get(req_type) or self.func_map.get(req_type) + + def build(self, *args, **kwargs): + return self.build_func(*args, **kwargs, registry=self) + + def register_class(self, name=None): + def _register(cls): + if not inspect.isclass(cls): + raise TypeError(f"Module must be type class, got {type(cls)}") + if "class" not in self.allow_types: + raise TypeError(f"Register {self.name} only allows type {self.allow_types}, got class") + module_name = name or cls.__name__ + if module_name in self.class_map: + warnings.warn(f"Class {module_name} already registered by {self.class_map[module_name]}, " + f"will be replaced by {cls}") + self.class_map[module_name] = cls + return cls + + return _register + + def register_function(self, name=None): + def _register(func): + if not inspect.isfunction(func): + raise TypeError(f"Registry must be type function, got {type(func)}") + if "function" not in self.allow_types: + raise TypeError(f"Registry {self.name} only allows type {self.allow_types}, got function") + func_name = name or func.__name__ + if func_name in self.class_map: + warnings.warn(f"Function {func_name} already registered by {self.func_map[func_name]}, " + f"will be replaced by {func}") + self.func_map[func_name] = func + return func + + return _register + + def _list(self): + keys = sorted(list(self.class_map.keys()) + list(self.func_map.keys())) + descriptions = [] + for key in keys: + if key in self.class_map: + descriptions.append(f"{key}: {self.class_map[key]}") + else: + descriptions.append( + f"{key}: ") + return "\n".join(descriptions) + + def __repr__(self): + description = self._list() + description = '\n'.join(['\t' + s for s in description.split('\n')]) + return f"{self.__class__.__name__} [{self.name}], \n" + description + + diff --git a/animation/UniAnimate/utils/registry_class.py b/animation/UniAnimate/utils/registry_class.py new file mode 100644 index 0000000..b35c169 --- /dev/null +++ b/animation/UniAnimate/utils/registry_class.py @@ -0,0 +1,19 @@ +from .registry import Registry, build_from_config + +def build_func(cfg, registry, **kwargs): + """ + Except for config, if passing a list of dataset config, then return the concat type of it + """ + return build_from_config(cfg, registry, **kwargs) + +AUTO_ENCODER = Registry("AUTO_ENCODER", build_func=build_func) +DATASETS = Registry("DATASETS", build_func=build_func) +DIFFUSION = Registry("DIFFUSION", build_func=build_func) +DISTRIBUTION = Registry("DISTRIBUTION", build_func=build_func) +EMBEDDER = Registry("EMBEDDER", build_func=build_func) +ENGINE = Registry("ENGINE", build_func=build_func) +INFER_ENGINE = Registry("INFER_ENGINE", build_func=build_func) +MODEL = Registry("MODEL", build_func=build_func) +PRETRAIN = Registry("PRETRAIN", build_func=build_func) +VISUAL = Registry("VISUAL", build_func=build_func) +EMBEDMANAGER = Registry("EMBEDMANAGER", build_func=build_func) diff --git a/animation/UniAnimate/utils/seed.py b/animation/UniAnimate/utils/seed.py new file mode 100644 index 0000000..d3656cd --- /dev/null +++ b/animation/UniAnimate/utils/seed.py @@ -0,0 +1,11 @@ +import torch +import random +import numpy as np + + +def setup_seed(seed): + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + np.random.seed(seed) + random.seed(seed) + torch.backends.cudnn.deterministic = True \ No newline at end of file diff --git a/animation/UniAnimate/utils/transforms.py b/animation/UniAnimate/utils/transforms.py new file mode 100644 index 0000000..923cb2b --- /dev/null +++ b/animation/UniAnimate/utils/transforms.py @@ -0,0 +1,353 @@ +import torch +import torchvision.transforms.functional as F +import random +import math +import numpy as np +from PIL import Image, ImageFilter + +__all__ = ['Compose', 'Resize', 'Rescale', 'CenterCrop', 'CenterCropV2', 'CenterCropWide', 'RandomCrop', 'RandomCropV2', 'RandomHFlip',\ + 'GaussianBlur', 'ColorJitter', 'RandomGray', 'ToTensor', 'Normalize', "ResizeRandomCrop", "ExtractResizeRandomCrop", "ExtractResizeAssignCrop"] + + +class Compose(object): + + def __init__(self, transforms): + self.transforms = transforms + + def __getitem__(self, index): + if isinstance(index, slice): + return Compose(self.transforms[index]) + else: + return self.transforms[index] + + def __len__(self): + return len(self.transforms) + + def __call__(self, rgb): + for t in self.transforms: + rgb = t(rgb) + return rgb + +class Resize(object): + + def __init__(self, size=256): + if isinstance(size, int): + size = (size, size) + self.size = size + + def __call__(self, rgb): + if isinstance(rgb, list): + rgb = [u.resize(self.size, Image.BILINEAR) for u in rgb] + else: + rgb = rgb.resize(self.size, Image.BILINEAR) + return rgb + +class Rescale(object): + + def __init__(self, size=256, interpolation=Image.BILINEAR): + self.size = size + self.interpolation = interpolation + + def __call__(self, rgb): + w, h = rgb[0].size + scale = self.size / min(w, h) + out_w, out_h = int(round(w * scale)), int(round(h * scale)) + rgb = [u.resize((out_w, out_h), self.interpolation) for u in rgb] + return rgb + +class CenterCrop(object): + + def __init__(self, size=224): + self.size = size + + def __call__(self, rgb): + w, h = rgb[0].size + assert min(w, h) >= self.size + x1 = (w - self.size) // 2 + y1 = (h - self.size) // 2 + rgb = [u.crop((x1, y1, x1 + self.size, y1 + self.size)) for u in rgb] + return rgb + +class ResizeRandomCrop(object): + + def __init__(self, size=256, size_short=292): + self.size = size + # self.min_area = min_area + self.size_short = size_short + + def __call__(self, rgb): + + # consistent crop between rgb and m + while min(rgb[0].size) >= 2 * self.size_short: + rgb = [u.resize((u.width // 2, u.height // 2), resample=Image.BOX) for u in rgb] + scale = self.size_short / min(rgb[0].size) + rgb = [u.resize((round(scale * u.width), round(scale * u.height)), resample=Image.BICUBIC) for u in rgb] + out_w = self.size + out_h = self.size + w, h = rgb[0].size # (518, 292) + x1 = random.randint(0, w - out_w) + y1 = random.randint(0, h - out_h) + + rgb = [u.crop((x1, y1, x1 + out_w, y1 + out_h)) for u in rgb] + # rgb = [u.resize((self.size, self.size), Image.BILINEAR) for u in rgb] + # # center crop + # x1 = (img[0].width - self.size) // 2 + # y1 = (img[0].height - self.size) // 2 + # img = [u.crop((x1, y1, x1 + self.size, y1 + self.size)) for u in img] + return rgb + + + +class ExtractResizeRandomCrop(object): + + def __init__(self, size=256, size_short=292): + self.size = size + # self.min_area = min_area + self.size_short = size_short + + def __call__(self, rgb): + + # consistent crop between rgb and m + while min(rgb[0].size) >= 2 * self.size_short: + rgb = [u.resize((u.width // 2, u.height // 2), resample=Image.BOX) for u in rgb] + scale = self.size_short / min(rgb[0].size) + rgb = [u.resize((round(scale * u.width), round(scale * u.height)), resample=Image.BICUBIC) for u in rgb] + out_w = self.size + out_h = self.size + w, h = rgb[0].size # (518, 292) + x1 = random.randint(0, w - out_w) + y1 = random.randint(0, h - out_h) + + rgb = [u.crop((x1, y1, x1 + out_w, y1 + out_h)) for u in rgb] + wh = [x1, y1, x1 + out_w, y1 + out_h] + return rgb, wh + + +class ExtractResizeAssignCrop(object): + + def __init__(self, size=256, size_short=292): + self.size = size + # self.min_area = min_area + self.size_short = size_short + + def __call__(self, rgb, wh): + + # consistent crop between rgb and m + while min(rgb[0].size) >= 2 * self.size_short: + rgb = [u.resize((u.width // 2, u.height // 2), resample=Image.BOX) for u in rgb] + scale = self.size_short / min(rgb[0].size) + rgb = [u.resize((round(scale * u.width), round(scale * u.height)), resample=Image.BICUBIC) for u in rgb] + + rgb = [u.crop(wh) for u in rgb] + rgb = [u.resize((self.size, self.size), Image.BILINEAR) for u in rgb] + return rgb + +class CenterCropV2(object): + def __init__(self, size): + self.size = size + + def __call__(self, img): + # fast resize + while min(img[0].size) >= 2 * self.size: + img = [u.resize((u.width // 2, u.height // 2), resample=Image.BOX) for u in img] + scale = self.size / min(img[0].size) + img = [u.resize((round(scale * u.width), round(scale * u.height)), resample=Image.BICUBIC) for u in img] + + # center crop + x1 = (img[0].width - self.size) // 2 + y1 = (img[0].height - self.size) // 2 + img = [u.crop((x1, y1, x1 + self.size, y1 + self.size)) for u in img] + return img + + +class CenterCropWide(object): + def __init__(self, size, interpolation=Image.BOX): + self.size = size + self.interpolation = interpolation + + def __call__(self, img): + if isinstance(img, list): + scale = min(img[0].size[0]/self.size[0], img[0].size[1]/self.size[1]) + img = [u.resize((round(u.width // scale), round(u.height // scale)), resample=self.interpolation) for u in img] + + # center crop + x1 = (img[0].width - self.size[0]) // 2 + y1 = (img[0].height - self.size[1]) // 2 + img = [u.crop((x1, y1, x1 + self.size[0], y1 + self.size[1])) for u in img] + return img + else: + scale = min(img.size[0]/self.size[0], img.size[1]/self.size[1]) + img = img.resize((round(img.width // scale), round(img.height // scale)), resample=self.interpolation) + x1 = (img.width - self.size[0]) // 2 + y1 = (img.height - self.size[1]) // 2 + img = img.crop((x1, y1, x1 + self.size[0], y1 + self.size[1])) + return img + + + +class RandomCrop(object): + + def __init__(self, size=224, min_area=0.4): + self.size = size + self.min_area = min_area + + def __call__(self, rgb): + + # consistent crop between rgb and m + w, h = rgb[0].size + area = w * h + out_w, out_h = float('inf'), float('inf') + while out_w > w or out_h > h: + target_area = random.uniform(self.min_area, 1.0) * area + aspect_ratio = random.uniform(3. / 4., 4. / 3.) + out_w = int(round(math.sqrt(target_area * aspect_ratio))) + out_h = int(round(math.sqrt(target_area / aspect_ratio))) + x1 = random.randint(0, w - out_w) + y1 = random.randint(0, h - out_h) + + rgb = [u.crop((x1, y1, x1 + out_w, y1 + out_h)) for u in rgb] + rgb = [u.resize((self.size, self.size), Image.BILINEAR) for u in rgb] + + return rgb + +class RandomCropV2(object): + + def __init__(self, size=224, min_area=0.4, ratio=(3. / 4., 4. / 3.)): + if isinstance(size, (tuple, list)): + self.size = size + else: + self.size = (size, size) + self.min_area = min_area + self.ratio = ratio + + def _get_params(self, img): + width, height = img.size + area = height * width + + for _ in range(10): + target_area = random.uniform(self.min_area, 1.0) * area + log_ratio = (math.log(self.ratio[0]), math.log(self.ratio[1])) + aspect_ratio = math.exp(random.uniform(*log_ratio)) + + w = int(round(math.sqrt(target_area * aspect_ratio))) + h = int(round(math.sqrt(target_area / aspect_ratio))) + + if 0 < w <= width and 0 < h <= height: + i = random.randint(0, height - h) + j = random.randint(0, width - w) + return i, j, h, w + + # Fallback to central crop + in_ratio = float(width) / float(height) + if (in_ratio < min(self.ratio)): + w = width + h = int(round(w / min(self.ratio))) + elif (in_ratio > max(self.ratio)): + h = height + w = int(round(h * max(self.ratio))) + else: # whole image + w = width + h = height + i = (height - h) // 2 + j = (width - w) // 2 + return i, j, h, w + + def __call__(self, rgb): + i, j, h, w = self._get_params(rgb[0]) + rgb = [F.resized_crop(u, i, j, h, w, self.size) for u in rgb] + return rgb + +class RandomHFlip(object): + + def __init__(self, p=0.5): + self.p = p + + def __call__(self, rgb): + if random.random() < self.p: + rgb = [u.transpose(Image.FLIP_LEFT_RIGHT) for u in rgb] + return rgb + +class GaussianBlur(object): + + def __init__(self, sigmas=[0.1, 2.0], p=0.5): + self.sigmas = sigmas + self.p = p + + def __call__(self, rgb): + if random.random() < self.p: + sigma = random.uniform(*self.sigmas) + rgb = [u.filter(ImageFilter.GaussianBlur(radius=sigma)) for u in rgb] + return rgb + +class ColorJitter(object): + + def __init__(self, brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1, p=0.5): + self.brightness = brightness + self.contrast = contrast + self.saturation = saturation + self.hue = hue + self.p = p + + def __call__(self, rgb): + if random.random() < self.p: + brightness, contrast, saturation, hue = self._random_params() + transforms = [ + lambda f: F.adjust_brightness(f, brightness), + lambda f: F.adjust_contrast(f, contrast), + lambda f: F.adjust_saturation(f, saturation), + lambda f: F.adjust_hue(f, hue)] + random.shuffle(transforms) + for t in transforms: + rgb = [t(u) for u in rgb] + + return rgb + + def _random_params(self): + brightness = random.uniform( + max(0, 1 - self.brightness), 1 + self.brightness) + contrast = random.uniform( + max(0, 1 - self.contrast), 1 + self.contrast) + saturation = random.uniform( + max(0, 1 - self.saturation), 1 + self.saturation) + hue = random.uniform(-self.hue, self.hue) + return brightness, contrast, saturation, hue + +class RandomGray(object): + + def __init__(self, p=0.2): + self.p = p + + def __call__(self, rgb): + if random.random() < self.p: + rgb = [u.convert('L').convert('RGB') for u in rgb] + return rgb + +class ToTensor(object): + + def __call__(self, rgb): + if isinstance(rgb, list): + rgb = torch.stack([F.to_tensor(u) for u in rgb], dim=0) + else: + rgb = F.to_tensor(rgb) + + return rgb + +class Normalize(object): + + def __init__(self, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]): + self.mean = mean + self.std = std + + def __call__(self, rgb): + rgb = rgb.clone() + rgb.clamp_(0, 1) + if not isinstance(self.mean, torch.Tensor): + self.mean = rgb.new_tensor(self.mean).view(-1) + if not isinstance(self.std, torch.Tensor): + self.std = rgb.new_tensor(self.std).view(-1) + if rgb.dim() == 4: + rgb.sub_(self.mean.view(1, -1, 1, 1)).div_(self.std.view(1, -1, 1, 1)) + elif rgb.dim() == 3: + rgb.sub_(self.mean.view(-1, 1, 1)).div_(self.std.view(-1, 1, 1)) + return rgb + diff --git a/animation/UniAnimate/utils/util.py b/animation/UniAnimate/utils/util.py new file mode 100644 index 0000000..b73866c --- /dev/null +++ b/animation/UniAnimate/utils/util.py @@ -0,0 +1,16 @@ +import torch + +def to_device(batch, device, non_blocking=False): + if isinstance(batch, (list, tuple)): + return type(batch)([ + to_device(u, device, non_blocking) + for u in batch]) + elif isinstance(batch, dict): + return type(batch)([ + (k, to_device(v, device, non_blocking)) + for k, v in batch.items()]) + elif isinstance(batch, torch.Tensor) and batch.device != device: + batch = batch.to(device, non_blocking=non_blocking) + else: + return batch + return batch diff --git a/animation/UniAnimate/utils/video_op.py b/animation/UniAnimate/utils/video_op.py new file mode 100644 index 0000000..7c46981 --- /dev/null +++ b/animation/UniAnimate/utils/video_op.py @@ -0,0 +1,359 @@ +import os +import os.path as osp +import sys +import cv2 +import glob +import math +import torch +import gzip +import copy +import time +import json +import pickle +import base64 +import imageio +import hashlib +import requests +import binascii +import zipfile +# import skvideo.io +import numpy as np +from io import BytesIO +import urllib.request +import torch.nn.functional as F +import torchvision.utils as tvutils +from multiprocessing.pool import ThreadPool as Pool +from einops import rearrange +from PIL import Image, ImageDraw, ImageFont + + +def gen_text_image(captions, text_size): + num_char = int(38 * (text_size / text_size)) + font_size = int(text_size / 20) + font = ImageFont.truetype('data/font/DejaVuSans.ttf', size=font_size) + text_image_list = [] + for text in captions: + txt_img = Image.new("RGB", (text_size, text_size), color="white") + draw = ImageDraw.Draw(txt_img) + lines = "\n".join(text[start:start + num_char] for start in range(0, len(text), num_char)) + draw.text((0, 0), lines, fill="black", font=font) + txt_img = np.array(txt_img) + text_image_list.append(txt_img) + text_images = np.stack(text_image_list, axis=0) + text_images = torch.from_numpy(text_images) + return text_images + +@torch.no_grad() +def save_video_refimg_and_text( + local_path, + ref_frame, + gen_video, + captions, + mean=[0.5, 0.5, 0.5], + std=[0.5, 0.5, 0.5], + text_size=256, + nrow=4, + save_fps=8, + retry=5): + ''' + gen_video: BxCxFxHxW + ''' + nrow = max(int(gen_video.size(0) / 2), 1) + vid_mean = torch.tensor(mean, device=gen_video.device).view(1, -1, 1, 1, 1) #ncfhw + vid_std = torch.tensor(std, device=gen_video.device).view(1, -1, 1, 1, 1) #ncfhw + + text_images = gen_text_image(captions, text_size) # Tensor 8x256x256x3 + text_images = text_images.unsqueeze(1) # Tensor 8x1x256x256x3 + text_images = text_images.repeat_interleave(repeats=gen_video.size(2), dim=1) # 8x16x256x256x3 + + ref_frame = ref_frame.unsqueeze(2) + ref_frame = ref_frame.mul_(vid_std).add_(vid_mean) + ref_frame = ref_frame.repeat_interleave(repeats=gen_video.size(2), dim=2) # 8x16x256x256x3 + ref_frame.clamp_(0, 1) + ref_frame = ref_frame * 255.0 + ref_frame = rearrange(ref_frame, 'b c f h w -> b f h w c') + + gen_video = gen_video.mul_(vid_std).add_(vid_mean) # 8x3x16x256x384 + gen_video.clamp_(0, 1) + gen_video = gen_video * 255.0 + + images = rearrange(gen_video, 'b c f h w -> b f h w c') + images = torch.cat([ref_frame, images, text_images], dim=3) + + images = rearrange(images, '(r j) f h w c -> f (r h) (j w) c', r=nrow) + images = [(img.numpy()).astype('uint8') for img in images] + + for _ in [None] * retry: + try: + if len(images) == 1: + local_path = local_path + '.png' + cv2.imwrite(local_path, images[0][:,:,::-1], [int(cv2.IMWRITE_JPEG_QUALITY), 100]) + else: + local_path = local_path + '.mp4' + frame_dir = os.path.join(os.path.dirname(local_path), '%s_frames' % (os.path.basename(local_path))) + os.system(f'rm -rf {frame_dir}'); os.makedirs(frame_dir, exist_ok=True) + for fid, frame in enumerate(images): + tpth = os.path.join(frame_dir, '%04d.png' % (fid+1)) + cv2.imwrite(tpth, frame[:,:,::-1], [int(cv2.IMWRITE_JPEG_QUALITY), 100]) + cmd = f'ffmpeg -y -f image2 -loglevel quiet -framerate {save_fps} -i {frame_dir}/%04d.png -vcodec libx264 -crf 17 -pix_fmt yuv420p {local_path}' + os.system(cmd); os.system(f'rm -rf {frame_dir}') + # os.system(f'rm -rf {local_path}') + exception = None + break + except Exception as e: + exception = e + continue + + +@torch.no_grad() +def save_i2vgen_video( + local_path, + image_id, + gen_video, + captions, + mean=[0.5, 0.5, 0.5], + std=[0.5, 0.5, 0.5], + text_size=256, + retry=5, + save_fps = 8 +): + ''' + Save both the generated video and the input conditions. + ''' + vid_mean = torch.tensor(mean, device=gen_video.device).view(1, -1, 1, 1, 1) #ncfhw + vid_std = torch.tensor(std, device=gen_video.device).view(1, -1, 1, 1, 1) #ncfhw + + text_images = gen_text_image(captions, text_size) # Tensor 1x256x256x3 + text_images = text_images.unsqueeze(1) # Tensor 1x1x256x256x3 + text_images = text_images.repeat_interleave(repeats=gen_video.size(2), dim=1) # 1x16x256x256x3 + + image_id = image_id.unsqueeze(2) # B, C, F, H, W + image_id = image_id.repeat_interleave(repeats=gen_video.size(2), dim=2) # 1x3x32x256x448 + image_id = image_id.mul_(vid_std).add_(vid_mean) # 32x3x256x448 + image_id.clamp_(0, 1) + image_id = image_id * 255.0 + image_id = rearrange(image_id, 'b c f h w -> b f h w c') + + gen_video = gen_video.mul_(vid_std).add_(vid_mean) # 8x3x16x256x384 + gen_video.clamp_(0, 1) + gen_video = gen_video * 255.0 + + images = rearrange(gen_video, 'b c f h w -> b f h w c') + images = torch.cat([image_id, images, text_images], dim=3) + images = images[0] + images = [(img.numpy()).astype('uint8') for img in images] + + exception = None + for _ in [None] * retry: + try: + frame_dir = os.path.join(os.path.dirname(local_path), '%s_frames' % (os.path.basename(local_path))) + os.system(f'rm -rf {frame_dir}'); os.makedirs(frame_dir, exist_ok=True) + for fid, frame in enumerate(images): + tpth = os.path.join(frame_dir, '%04d.png' % (fid+1)) + cv2.imwrite(tpth, frame[:,:,::-1], [int(cv2.IMWRITE_JPEG_QUALITY), 100]) + cmd = f'ffmpeg -y -f image2 -loglevel quiet -framerate {save_fps} -i {frame_dir}/%04d.png -vcodec libx264 -crf 17 -pix_fmt yuv420p {local_path}' + os.system(cmd); os.system(f'rm -rf {frame_dir}') + break + except Exception as e: + exception = e + continue + + if exception is not None: + raise exception + + +@torch.no_grad() +def save_i2vgen_video_safe( + local_path, + gen_video, + captions, + mean=[0.5, 0.5, 0.5], + std=[0.5, 0.5, 0.5], + text_size=256, + retry=5, + save_fps = 8 +): + ''' + Save only the generated video, do not save the related reference conditions, and at the same time perform anomaly detection on the last frame. + ''' + vid_mean = torch.tensor(mean, device=gen_video.device).view(1, -1, 1, 1, 1) #ncfhw + vid_std = torch.tensor(std, device=gen_video.device).view(1, -1, 1, 1, 1) #ncfhw + + gen_video = gen_video.mul_(vid_std).add_(vid_mean) # 8x3x16x256x384 + gen_video.clamp_(0, 1) + gen_video = gen_video * 255.0 + + images = rearrange(gen_video, 'b c f h w -> b f h w c') + images = images[0] + images = [(img.numpy()).astype('uint8') for img in images] + num_image = len(images) + exception = None + for _ in [None] * retry: + try: + if num_image == 1: + local_path = local_path + '.png' + cv2.imwrite(local_path, images[0][:,:,::-1], [int(cv2.IMWRITE_JPEG_QUALITY), 100]) + else: + writer = imageio.get_writer(local_path, fps=save_fps, codec='libx264', quality=8) + for fid, frame in enumerate(images): + if fid == num_image-1: # Fix known bugs. + ratio = (np.sum((frame >= 117) & (frame <= 137)))/(frame.size) + if ratio > 0.4: continue + writer.append_data(frame) + writer.close() + break + except Exception as e: + exception = e + continue + + if exception is not None: + raise exception + + +@torch.no_grad() +def save_t2vhigen_video_safe( + local_path, + gen_video, + captions, + mean=[0.5, 0.5, 0.5], + std=[0.5, 0.5, 0.5], + text_size=256, + retry=5, + save_fps = 8 +): + ''' + Save only the generated video, do not save the related reference conditions, and at the same time perform anomaly detection on the last frame. + ''' + vid_mean = torch.tensor(mean, device=gen_video.device).view(1, -1, 1, 1, 1) #ncfhw + vid_std = torch.tensor(std, device=gen_video.device).view(1, -1, 1, 1, 1) #ncfhw + + gen_video = gen_video.mul_(vid_std).add_(vid_mean) # 8x3x16x256x384 + gen_video.clamp_(0, 1) + gen_video = gen_video * 255.0 + + images = rearrange(gen_video, 'b c f h w -> b f h w c') + images = images[0] + images = [(img.numpy()).astype('uint8') for img in images] + num_image = len(images) + exception = None + for _ in [None] * retry: + try: + if num_image == 1: + local_path = local_path + '.png' + cv2.imwrite(local_path, images[0][:,:,::-1], [int(cv2.IMWRITE_JPEG_QUALITY), 100]) + else: + frame_dir = os.path.join(os.path.dirname(local_path), '%s_frames' % (os.path.basename(local_path))) + os.system(f'rm -rf {frame_dir}'); os.makedirs(frame_dir, exist_ok=True) + for fid, frame in enumerate(images): + if fid == num_image-1: # Fix known bugs. + ratio = (np.sum((frame >= 117) & (frame <= 137)))/(frame.size) + if ratio > 0.4: continue + tpth = os.path.join(frame_dir, '%04d.png' % (fid+1)) + cv2.imwrite(tpth, frame[:,:,::-1], [int(cv2.IMWRITE_JPEG_QUALITY), 100]) + cmd = f'ffmpeg -y -f image2 -loglevel quiet -framerate {save_fps} -i {frame_dir}/%04d.png -vcodec libx264 -crf 17 -pix_fmt yuv420p {local_path}' + os.system(cmd) + os.system(f'rm -rf {frame_dir}') + break + except Exception as e: + exception = e + continue + + if exception is not None: + raise exception + + + + +@torch.no_grad() +def save_video_multiple_conditions_not_gif_horizontal_3col(local_path, video_tensor, model_kwargs, source_imgs, + mean=[0.5,0.5,0.5], std=[0.5,0.5,0.5], nrow=8, retry=5, save_fps=8): + mean=torch.tensor(mean,device=video_tensor.device).view(1,-1,1,1,1)#ncfhw + std=torch.tensor(std,device=video_tensor.device).view(1,-1,1,1,1)#ncfhw + video_tensor = video_tensor.mul_(std).add_(mean) #### unnormalize back to [0,1] + video_tensor.clamp_(0, 1) + + b, c, n, h, w = video_tensor.shape + source_imgs = F.adaptive_avg_pool3d(source_imgs, (n, h, w)) + source_imgs = source_imgs.cpu() + + model_kwargs_channel3 = {} + for key, conditions in model_kwargs[0].items(): + + + if conditions.size(1) == 1: + conditions = torch.cat([conditions, conditions, conditions], dim=1) + conditions = F.adaptive_avg_pool3d(conditions, (n, h, w)) + if conditions.size(1) == 2: + conditions = torch.cat([conditions, conditions[:,:1,]], dim=1) + conditions = F.adaptive_avg_pool3d(conditions, (n, h, w)) + elif conditions.size(1) == 3: + conditions = F.adaptive_avg_pool3d(conditions, (n, h, w)) + elif conditions.size(1) == 4: # means it is a mask. + color = ((conditions[:, 0:3] + 1.)/2.) # .astype(np.float32) + alpha = conditions[:, 3:4] # .astype(np.float32) + conditions = color * alpha + 1.0 * (1.0 - alpha) + conditions = F.adaptive_avg_pool3d(conditions, (n, h, w)) + model_kwargs_channel3[key] = conditions.cpu() if conditions.is_cuda else conditions + + # filename = rand_name(suffix='.gif') + for _ in [None] * retry: + try: + vid_gif = rearrange(video_tensor, '(i j) c f h w -> c f (i h) (j w)', i = nrow) + + cons_list = [rearrange(con, '(i j) c f h w -> c f (i h) (j w)', i = nrow) for _, con in model_kwargs_channel3.items()] + vid_gif = torch.cat(cons_list + [vid_gif,], dim=3) + + vid_gif = vid_gif.permute(1,2,3,0) + + images = vid_gif * 255.0 + images = [(img.numpy()).astype('uint8') for img in images] + if len(images) == 1: + + local_path = local_path.replace('.mp4', '.png') + cv2.imwrite(local_path, images[0][:,:,::-1], [int(cv2.IMWRITE_JPEG_QUALITY), 100]) + # bucket.put_object_from_file(oss_key, local_path) + else: + + outputs = [] + for image_name in images: + x = Image.fromarray(image_name) + outputs.append(x) + from pathlib import Path + save_fmt = Path(local_path).suffix + + if save_fmt == ".mp4": + with imageio.get_writer(local_path, fps=save_fps) as writer: + for img in outputs: + img_array = np.array(img) # Convert PIL Image to numpy array + writer.append_data(img_array) + + elif save_fmt == ".gif": + outputs[0].save( + fp=local_path, + format="GIF", + append_images=outputs[1:], + save_all=True, + duration=(1 / save_fps * 1000), + loop=0, + ) + else: + raise ValueError("Unsupported file type. Use .mp4 or .gif.") + + # fourcc = cv2.VideoWriter_fourcc(*'mp4v') + # fps = save_fps + # image = images[0] + # media_writer = cv2.VideoWriter(local_path, fourcc, fps, (image.shape[1],image.shape[0])) + # for image_name in images: + # im = image_name[:,:,::-1] + # media_writer.write(im) + # media_writer.release() + + + exception = None + break + except Exception as e: + exception = e + continue + if exception is not None: + print('save video to {} failed, error: {}'.format(local_path, exception), flush=True) +