# install `huggingface_hub`
huggingface-cli download \
  --repo-type dataset Wild-Heart/Disney-VideoGeneration-Dataset \
  --local-dir video-dataset-disney

Then launch LoRA fine-tuning for text-to-video:

TODO

We can now use the trained model for inference:

TODO

We can also fine-tune the 5B variant with LoRA:

TODO

Below we provide additional sections detailing on more options we provide in this repository. They all attempt to make fine-tuning for video models as accessible as possible.

Dataset Preparation

Create two files where one file contains line-separated prompts and another file contains line-separated paths to video data (the path to video files must be relative to the path you pass when specifying --data_root). Let's take a look at an example to understand this better!

Assume you've specified --data_root as /dataset, and that this directory contains the files: prompts.txt and videos.txt.

The prompts.txt file should contain line-separated prompts:

A black and white animated sequence featuring a rabbit, named Rabbity Ribfried, and an anthropomorphic goat in a musical, playful environment, showcasing their evolving interaction.
A black and white animated sequence on a ship's deck features a bulldog character, named Bully Bulldoger, showcasing exaggerated facial expressions and body language. The character progresses from confident to focused, then to strained and distressed, displaying a range of emotions as it navigates challenges. The ship's interior remains static in the background, with minimalistic details such as a bell and open door. The character's dynamic movements and changing expressions drive the narrative, with no camera movement to distract from its evolving reactions and physical gestures.
...

The videos.txt file should contain line-separate paths to video files. Note that the path should be relative to the --data_root directory.

videos/00000.mp4
videos/00001.mp4
...

Overall, this is how your dataset would look like if you ran the tree command on the dataset root directory:

/dataset
├── prompts.txt
├── videos.txt
├── videos
    ├── videos/00000.mp4
    ├── videos/00001.mp4
    ├── ...

When using this format, the --caption_column must be prompts.txt and --video_column must be videos.txt. If you, instead, have your data stored in a CSV file, you can also specify --dataset_file as the path to CSV, the --caption_column and --video_column as the actual column names in the CSV file.

As an example, let's use this Disney dataset for finetuning. To download, one can use the 🤗 Hugging Face CLI.

huggingface-cli download --repo-type dataset Wild-Heart/Disney-VideoGeneration-Dataset --local-dir video-dataset-disney

TODO: Add a section on creating and using precomputed embeddings.

Training

We provide training script for both text-to-video and image-to-video generation which are compatible with the Cog family of models.

Take a look at *.sh

Note: Untested on MPS

Memory requirements

Slaying OOMs with PyTorch

Supported and verified memory optimizations for training include:

CPUOffloadOptimizer from torchao. You can read about its capabilities and limitations here. In short, it allows you to use the CPU for storing trainable parameters and gradients. This results in the optimizer step happening on the CPU, which requires a fast CPU optimizer, such as torch.optim.AdamW(fused=True) or applying torch.compile on the optimizer step. Additionally, it is recommended to not torch.compile your model for training. Gradient clipping and accumulation is not supported yet either.
Low-bit optimizers from bitsandbytes. TODO: to test and make torchao ones work
DeepSpeed Zero2: Since we rely on accelerate, follow this guide to configure your accelerate installation to enable training with DeepSpeed Zero2 optimizations.

Important

The memory requirements are reported after running the training/prepare_dataset.py, which converts the videos and captions to latents and embeddings. During training, we directly load the latents and embeddings, and do not require the VAE or the T5 text encoder. However, if you perform validation/testing, these must be loaded and increase the amount of required memory. Not performing validation/testing saves a significant amount of memory, which can be used to focus solely on training if you're on smaller VRAM GPUs.

If you choose to run validation/testing, you can save some memory on lower VRAM GPUs by specifying --enable_model_cpu_offload.

LoRA finetuning

Note

The memory requirements for image-to-video lora finetuning are similar to that of text-to-video on THUDM/CogVideoX-5b, so it hasn't been reported explicitly.

Additionally, to prepare test images for I2V finetuning, you could either generate them on-the-fly by modifying the script, or extract some frames from your training data using: ffmpeg -i input.mp4 -frames:v 1 frame.png, or provide a URL to a valid and accessible image.

AdamW

With train_batch_size = 1:

model	lora rank	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	16	False	12.945	43.764	46.918	24.234
THUDM/CogVideoX-2b	16	True	12.945	12.945	21.121	24.234
THUDM/CogVideoX-2b	64	False	13.035	44.314	47.469	24.469
THUDM/CogVideoX-2b	64	True	13.036	13.035	21.564	24.500
THUDM/CogVideoX-2b	256	False	13.095	45.826	48.990	25.543
THUDM/CogVideoX-2b	256	True	13.094	13.095	22.344	25.537
THUDM/CogVideoX-5b	16	True	19.742	19.742	28.746	38.123
THUDM/CogVideoX-5b	64	True	20.006	20.818	30.338	38.738
THUDM/CogVideoX-5b	256	True	20.771	22.119	31.939	41.537

With train_batch_size = 4:

model	lora rank	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	16	True	12.945	21.803	21.814	24.322
THUDM/CogVideoX-2b	64	True	13.035	22.254	22.254	24.572
THUDM/CogVideoX-2b	256	True	13.094	22.020	22.033	25.574
THUDM/CogVideoX-5b	16	True	19.742	46.492	46.492	38.197
THUDM/CogVideoX-5b	64	True	20.006	47.805	47.805	39.365
THUDM/CogVideoX-5b	256	True	20.771	47.268	47.332	41.008

Note

Trying to run CogVideoX-5b without gradient checkpointing OOMs even on an A100 (80 GB), so the memory measurements have not been specified.

AdamW (8-bit bitsandbytes)

With train_batch_size = 1:

model	lora rank	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	16	False	12.945	43.732	46.887	24.195
THUDM/CogVideoX-2b	16	True	12.945	12.945	21.430	24.195
THUDM/CogVideoX-2b	64	False	13.035	44.004	47.158	24.369
THUDM/CogVideoX-2b	64	True	13.035	13.035	21.297	24.357
THUDM/CogVideoX-2b	256	False	13.035	45.291	48.455	24.836
THUDM/CogVideoX-2b	256	True	13.035	13.035	21.625	24.869
THUDM/CogVideoX-5b	16	True	19.742	19.742	28.602	38.049
THUDM/CogVideoX-5b	64	True	20.006	20.818	29.359	38.520
THUDM/CogVideoX-5b	256	True	20.771	21.352	30.727	39.596

With train_batch_size = 4:

model	lora rank	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	16	True	12.945	21.734	21.775	24.281
THUDM/CogVideoX-2b	64	True	13.036	21.941	21.941	24.445
THUDM/CogVideoX-2b	256	True	13.094	22.020	22.266	24.943
THUDM/CogVideoX-5b	16	True	19.742	46.320	46.326	38.104
THUDM/CogVideoX-5b	64	True	20.006	46.820	46.820	38.588
THUDM/CogVideoX-5b	256	True	20.771	47.920	47.980	40.002

AdamW (8-bit torchao)

Currently, errors out with following stack-trace:

Traceback (most recent call last):               
  File "/raid/aryan/cogvideox-distillation/training/cogvideox_text_to_video_lora.py", line 915, in <module>                                                                                         
    main(args)                                   
  File "/raid/aryan/cogvideox-distillation/training/cogvideox_text_to_video_lora.py", line 719, in main                                                                                             
    optimizer.step()                                                                              
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/accelerate/optimizer.py", line 159, in step                                                                                           
    self.scaler.step(self.optimizer, closure)    
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/amp/grad_scaler.py", line 457, in step                                                                                          
    retval = self._maybe_opt_step(optimizer, optimizer_state, *args, **kwargs)                    
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/amp/grad_scaler.py", line 352, in _maybe_opt_step                                                                               
    retval = optimizer.step(*args, **kwargs)     
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/accelerate/optimizer.py", line 214, in patched_step                                                                                   
    return method(*args, **kwargs)               
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/optim/lr_scheduler.py", line 137, in wrapper                                                                                    
    return func.__get__(opt, opt.__class__)(*args, **kwargs)                                      
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/optim/optimizer.py", line 487, in wrapper                                                                                       
    out = func(*args, **kwargs)                  
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context                                                                            
    return func(*args, **kwargs)                 
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torchao/prototype/low_bit_optim/adam.py", line 87, in step                                                                            
    raise RuntimeError(                          
RuntimeError: lr was changed to a non-Tensor object. If you want to update lr, please use optim.param_groups[0]['lr'].fill_(new_lr)

AdamW (4-bit torchao)

Same error as AdamW (8-bit torchao)

AdamW + CPUOffloadOptimizer (with gradient offloading)

With train_batch_size = 1:

model	lora rank	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	16	False	12.945	43.705	46.859	24.180
THUDM/CogVideoX-2b	16	True	12.945	12.945	21.395	24.180
THUDM/CogVideoX-2b	64	False	13.035	43.916	47.070	24.234
THUDM/CogVideoX-2b	64	True	13.035	13.035	20.887	24.266
THUDM/CogVideoX-2b	256	False	13.095	44.947	48.111	24.607
THUDM/CogVideoX-2b	256	True	13.095	13.095	21.391	24.635
THUDM/CogVideoX-5b	16	True	19.742	19.742	28.533	38.002
THUDM/CogVideoX-5b	64	True	20.006	20.006	29.107	38.785
THUDM/CogVideoX-5b	256	True	20.771	20.771	30.078	39.559

With train_batch_size = 4:

model	lora rank	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	16	True	12.945	21.709	21.762	24.254
THUDM/CogVideoX-2b	64	True	13.035	21.844	21.855	24.338
THUDM/CogVideoX-2b	256	True	13.094	22.020	22.031	24.709
THUDM/CogVideoX-5b	16	True	19.742	46.262	46.297	38.400
THUDM/CogVideoX-5b	64	True	20.006	46.561	46.574	38.840
THUDM/CogVideoX-5b	256	True	20.771	47.268	47.332	39.623

Note

Trying to run CogVideoX-5b without gradient checkpointing OOMs even on an A100 (80 GB), so the memory measurements have not been specified.

AdamW (8-bit bitsandbytes) + CPUOffloadOptimizer (with gradient offloading)

Currently, errors out with the following stack-trace:

  File "/raid/aryan/cogvideox-distillation/training/cogvideox_text_to_video_lora.py", line 925, in <module>                                                                                         
    main(args)                                                                                                                                                                                      
  File "/raid/aryan/cogvideox-distillation/training/cogvideox_text_to_video_lora.py", line 727, in main                                                                                             
    optimizer.step()                                                                                                                                                                                
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context                                                                            
    return func(*args, **kwargs)                                                                                                                                                                    
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torchao/prototype/low_bit_optim/cpu_offload.py", line 87, in step                                                                     
    self.optim_dict[p_cuda].step()                                                                                                                                                                  
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/optim/optimizer.py", line 487, in wrapper                                                                                       
    out = func(*args, **kwargs)                                                                                                                                                                     
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context                                                                            
    return func(*args, **kwargs)                                                                                                                                                                    
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/bitsandbytes/optim/optimizer.py", line 287, in step                                                                                   
    self.update_step(group, p, gindex, pindex)                                                                                                                                                      
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context                                                                            
    return func(*args, **kwargs)                                                                                                                                                                    
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/bitsandbytes/optim/optimizer.py", line 546, in update_step                                                                            
    F.optimizer_update_8bit_blockwise(                                                                                                                                                              
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/bitsandbytes/functional.py", line 1774, in optimizer_update_8bit_blockwise                                                            
    prev_device = pre_call(g.device)                                                                                                                                                                
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/bitsandbytes/functional.py", line 463, in pre_call                                                                                    
    torch.cuda.set_device(device)                                                                                                                                                                   
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/cuda/__init__.py", line 476, in set_device                                                                                      
    device = _get_device_index(device)           
  File "/raid/aryan/nightly-venv/lib/python3.10/site-packages/torch/cuda/_utils.py", line 34, in _get_device_index                                                                                  
    raise ValueError(f"Expected a cuda device, but got: {device}")                                
ValueError: Expected a cuda device, but got: cpu

DeepSpeed (AdamW + CPU/Parameter offloading)

Note

Results are for lora_rank=256 with gradient_checkpointing enabled, 2x RTX 4090.

With train_batch_size = 1:

model	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	13.141	13.141	21.070	24.602
THUDM/CogVideoX-5b	20.170	20.170	28.662	38.957

With train_batch_size = 4:

model	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	13.141	19.854	20.836	24.709
THUDM/CogVideoX-5b	20.170	40.635	40.699	39.027

Full finetuning

Note

The memory requirements for image-to-video full finetuning are similar to that of text-to-video on THUDM/CogVideoX-5b, so it hasn't been reported explicitly.

Additionally, to prepare test images for I2V finetuning, you could either generate them on-the-fly by modifying the script, or extract some frames from your training data using: ffmpeg -i input.mp4 -frames:v 1 frame.png, or provide a URL to a valid and accessible image.

Note

Trying to run full finetuning without gradient checkpointing OOMs even on an A100 (80 GB), so the memory measurements have not been specified.

AdamW

With train_batch_size = 1:

model	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	True	16.396	33.934	43.848	37.520
THUDM/CogVideoX-5b	True	30.061	OOM	OOM	OOM

With train_batch_size = 4:

model	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	True	16.396	38.281	48.341	37.544
THUDM/CogVideoX-5b	True	30.061	OOM	OOM	OOM

AdamW (8-bit bitsandbytes)

With train_batch_size = 1:

model	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	True	16.396	16.447	27.555	27.156
THUDM/CogVideoX-5b	True	30.061	52.826	58.570	49.541

With train_batch_size = 4:

model	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	True	16.396	27.930	27.990	27.326
THUDM/CogVideoX-5b	True	16.396	66.648	66.705	48.828

AdamW + CPUOffloadOptimizer (with gradient offloading)

With train_batch_size = 1:

model	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	True	16.396	16.396	26.100	23.832
THUDM/CogVideoX-5b	True	30.061	39.359	48.307	37.947

With train_batch_size = 4:

model	gradient_checkpointing	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	True	16.396	27.916	27.975	23.936
THUDM/CogVideoX-5b	True	30.061	66.607	66.668	38.061

DeepSpeed (AdamW + CPU/Parameter offloading)

Note

Results with gradient_checkpointing enabled, 2x RTX 4090.

With train_batch_size = 1:

model	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	13.111	13.111	20.328	23.867
THUDM/CogVideoX-5b	19.762	19.998	27.697	38.018

With train_batch_size = 4:

model	memory_before_training	memory_before_validation	memory_after_validation	memory_after_testing
THUDM/CogVideoX-2b	13.111	21.188	21.254	23.869
THUDM/CogVideoX-5b	19.762	43.465	43.531	38.082

Note

memory_after_validation is indicative of the peak memory required for training. This is because apart from the activations, parameters and gradients stored for training, you also need to load the vae and text encoder in memory and spend some memory to perform inference. In order to reduce total memory required to perform training, one can choose to not perform validation/testing as part of the training script.

Make scripts compatible with DDP
Make scripts compatible with FSDP
Make scripts compatible with DeepSpeed
Test scripts with memory-efficient optimizer from bitsandbytes
Test scripts with CPUOffloadOptimizer, etc.
Test scripts with torchao quantization, and low bit memory optimizers, etc.
Make 5B lora finetuning work in under 24GB

Important

Since our goal is to make the scripts as memory-friendly as possible we don't guarantee multi-GPU training.

README.md Unescape Escape

CogVideoX Factory 🧪

Quickstart

Dataset Preparation

Training

Memory requirements

LoRA finetuning

Full finetuning

README.md