updates

training/mochi-1/dataset_mochi.py

@@ -3,10 +3,11 @@ from typing import Any, Dict, Tuple
 
 import numpy as np
 import torch
-import torchvision.transforms as TT
+from torchvision import transforms
 from accelerate.logging import get_logger
 from torchvision.transforms import InterpolationMode
 from torchvision.transforms.functional import resize
+import torch.nn as nn
 
 
 # Must import after torch because this can sometimes lead to a nasty segmentation fault, or stack smashing error
@@ -25,7 +26,7 @@ logger = get_logger(__name__)
 # TODO (sayakpaul): probably not all buckets are needed for Mochi-1? 
 HEIGHT_BUCKETS = [256, 320, 384, 480, 512, 576, 720, 768, 960, 1024, 1280, 1536]
 WIDTH_BUCKETS = [256, 320, 384, 480, 512, 576, 720, 768, 848, 960, 1024, 1280, 1536]
-FRAME_BUCKETS = [16, 24, 32, 48, 64, 80, 84]
+FRAME_BUCKETS = [16, 24, 32, 48, 64, 80, 85]
 
 VAE_SPATIAL_SCALE_FACTOR = 8
 VAE_TEMPORAL_SCALE_FACTOR = 6
@@ -34,6 +35,19 @@ class VideoDataset(VDS):
     def __init__(self, *args, **kwargs) -> None:
         super().__init__(*args, **kwargs)
 
+        random_flip = kwargs.get("random_flip", None)
+        self.video_transforms = transforms.Compose(
+            [
+                transforms.RandomHorizontalFlip([(random_flip)])
+                if random_flip
+                else transforms.Lambda(lambda x: x),
+                transforms.Lambda(self.scale_transform),
+            ]
+        )
+
+    def scale_transform(self, x):
+        return x / 127.5 - 1.0
+
     # Overriding this because we calculate `num_frames` differently.
     def __getitem__(self, index: int) -> Dict[str, Any]:
         if isinstance(index, list):
@@ -55,9 +69,9 @@ class VideoDataset(VDS):
             # Output of the VAE encoding is 2 * output_channels and then it's
             # temporal compression factor is 6. Initially, the VAE encodings will have
             # 24 latent number of frames. So, if we were to train with a 
-            # max frame size of 84 and frame bucket of [84], we need to have the following logic.
+            # max frame size of 85 and frame bucket of [85], we need to have the following logic.
             latent_num_frames = video_latents.size(0)
-            num_frames = (latent_num_frames // 2) * (VAE_TEMPORAL_SCALE_FACTOR + 1)
+            num_frames = ((latent_num_frames // 2) * (VAE_TEMPORAL_SCALE_FACTOR + 1) + 1)
 
             height = video_latents.size(2) * VAE_SPATIAL_SCALE_FACTOR
             width = video_latents.size(3) * VAE_SPATIAL_SCALE_FACTOR
@@ -135,13 +149,13 @@ class VideoDataset(VDS):
         return images, latents, embeds, attention_masks
 
 
-# We need the `VideoDatasetWithResizing` and `VideoDatasetWithResizeAndRectangleCrop` classes to subclass from
-# the new `VideoDataset` class defined in this file. And also because of the changes in 
-# `_preprocess_video()` (how we handle `nearest_frame_bucket`). 
 
-class VideoDatasetWithResizing(VideoDataset):
+class VideoDatasetWithFlexibleResize(VideoDataset):
-    def __init__(self, *args, **kwargs) -> None:
+    def __init__(self, video_reshape_mode: str = None, *args, **kwargs) -> None:
         super().__init__(*args, **kwargs)
+        if video_reshape_mode:
+            assert video_reshape_mode in ["center", "random"]
+        self.video_reshape_mode = video_reshape_mode
 
     def _preprocess_video(self, path: Path) -> torch.Tensor:
         if self.load_tensors:
@@ -151,36 +165,56 @@ class VideoDatasetWithResizing(VideoDataset):
             video_num_frames = len(video_reader)
 
             nearest_frame_bucket = min(
-                [bucket for bucket in self.frame_buckets if bucket <= video_num_frames],
+                self.frame_buckets, key=lambda x: abs(x - min(video_num_frames, self.max_num_frames))
-                key=lambda x: abs(x - min(video_num_frames, self.max_num_frames)),
+            )
-                default=1,
+            frame_indices = list(
+                range(
+                    0, 
+                    video_num_frames, 
+                    1 if video_num_frames < nearest_frame_bucket else video_num_frames // nearest_frame_bucket
+                )
             )
-            
-            frame_indices = list(range(0, video_num_frames, video_num_frames // nearest_frame_bucket))
             frames = video_reader.get_batch(frame_indices)
-            frames = frames[:nearest_frame_bucket].float()
+            
+            # Pad or truncate frames to match the bucket size
+            if video_num_frames < nearest_frame_bucket:
+                pad_size = nearest_frame_bucket - video_num_frames
+                frames = nn.functional.pad(frames, (0, 0, 0, 0, 0, 0, 0, pad_size))
+                frames = frames.float()
+            else:
+                frames = frames[:nearest_frame_bucket].float()
             frames = frames.permute(0, 3, 1, 2).contiguous()
 
+            # Find nearest resolution and apply resizing
             nearest_res = self._find_nearest_resolution(frames.shape[2], frames.shape[3])
-            frames_resized = torch.stack([resize(frame, nearest_res) for frame in frames], dim=0)
+            if self.video_reshape_mode in {"center", "random"}:
-            frames = torch.stack([self.video_transforms(frame) for frame in frames_resized], dim=0)
+                frames = self._resize_for_rectangle_crop(frames, nearest_res)
+            else:
+                frames = torch.stack([resize(frame, nearest_res) for frame in frames], dim=0)
 
+            # Apply transformations
+            frames = torch.stack([self.video_transforms(frame) for frame in frames], dim=0)
+
+            # Optionally extract the first frame as an image
             image = frames[:1].clone() if self.image_to_video else None
 
             return image, frames, None
 
-    def _find_nearest_resolution(self, height, width):
+    def _find_nearest_resolution(self, height: int, width: int) -> Tuple[int, int]:
+        """
+        Find the nearest resolution from the predefined list of resolutions.
+        """
         nearest_res = min(self.resolutions, key=lambda x: abs(x[1] - height) + abs(x[2] - width))
         return nearest_res[1], nearest_res[2]
 
-
+    def _resize_for_rectangle_crop(self, arr: torch.Tensor, image_size: Tuple[int, int]) -> torch.Tensor:
-class VideoDatasetWithResizeAndRectangleCrop(VideoDataset):
+        """
-    def __init__(self, video_reshape_mode: str = "center", *args, **kwargs) -> None:
+        Resize frames for rectangular cropping.
-        super().__init__(*args, **kwargs)
+        
-        self.video_reshape_mode = video_reshape_mode
+        Args:
-
+            arr (torch.Tensor): The video frames tensor [N, C, H, W].
-    def _resize_for_rectangle_crop(self, arr, image_size):
+            image_size (Tuple[int, int]): The target resolution (height, width).
-        reshape_mode = self.video_reshape_mode
+        """
         if arr.shape[3] / arr.shape[2] > image_size[1] / image_size[0]:
             arr = resize(
                 arr,
@@ -194,48 +228,15 @@ class VideoDatasetWithResizeAndRectangleCrop(VideoDataset):
                 interpolation=InterpolationMode.BICUBIC,
             )
 
+        # Perform cropping
         h, w = arr.shape[2], arr.shape[3]
-        arr = arr.squeeze(0)
+        delta_h, delta_w = h - image_size[0], w - image_size[1]
 
-        delta_h = h - image_size[0]
+        if self.video_reshape_mode == "random":
-        delta_w = w - image_size[1]
+            top, left = np.random.randint(0, delta_h + 1), np.random.randint(0, delta_w + 1)
-
+        elif self.video_reshape_mode == "center":
-        if reshape_mode == "random" or reshape_mode == "none":
-            top = np.random.randint(0, delta_h + 1)
-            left = np.random.randint(0, delta_w + 1)
-        elif reshape_mode == "center":
             top, left = delta_h // 2, delta_w // 2
         else:
-            raise NotImplementedError
+            raise NotImplementedError(f"Unsupported reshape mode: {self.video_reshape_mode}")
-        arr = TT.functional.crop(arr, top=top, left=left, height=image_size[0], width=image_size[1])
-        return arr
 
-    def _preprocess_video(self, path: Path) -> torch.Tensor:
+        return transforms.functional.crop(arr, top=top, left=left, height=image_size[0], width=image_size[1])
-        if self.load_tensors:
-            return self._load_preprocessed_latents_and_embeds(path)
-        else:
-            video_reader = decord.VideoReader(uri=path.as_posix())
-            video_num_frames = len(video_reader)
-            nearest_frame_bucket = min(
-                [bucket for bucket in self.frame_buckets if bucket <= video_num_frames],
-                key=lambda x: abs(x - min(video_num_frames, self.max_num_frames)),
-                default=1,
-            )
-
-            frame_indices = list(range(0, video_num_frames, video_num_frames // nearest_frame_bucket))
-
-            frames = video_reader.get_batch(frame_indices)
-            frames = frames[:nearest_frame_bucket].float()
-            frames = frames.permute(0, 3, 1, 2).contiguous()
-
-            nearest_res = self._find_nearest_resolution(frames.shape[2], frames.shape[3])
-            frames_resized = self._resize_for_rectangle_crop(frames, nearest_res)
-            frames = torch.stack([self.video_transforms(frame) for frame in frames_resized], dim=0)
-
-            image = frames[:1].clone() if self.image_to_video else None
-
-            return image, frames, None
-
-    def _find_nearest_resolution(self, height, width):
-        nearest_res = min(self.resolutions, key=lambda x: abs(x[1] - height) + abs(x[2] - width))
-        return nearest_res[1], nearest_res[2]

training/mochi-1/prepare_dataset.py

@@ -21,7 +21,7 @@ from torch.utils.data import DataLoader
 from torchvision import transforms
 from tqdm import tqdm
 from transformers import T5EncoderModel, T5Tokenizer
-from dataset_mochi import VideoDatasetWithResizing, VideoDatasetWithResizeAndRectangleCrop
+from dataset_mochi import VideoDatasetWithFlexibleResize
 
 
 import decord  # isort:skip
@@ -326,6 +326,8 @@ def serialize_artifacts(
     metadata = []
     for i in range(videos.size(0)):
         video = videos[i:i+1]
+        if video.size(1) == 1:
+            print(f"{video_latents[i:i+1].shape=}")
         metadata_dict = {"num_frames": video.size(1), "height": video.size(3), "width": video.size(4)}
         metadata.append(metadata_dict)
 
@@ -421,6 +423,7 @@ def main():
     dataset_init_kwargs = {
         "data_root": args.data_root,
         "dataset_file": args.dataset_file,
+        "video_reshape_mode": args.video_reshape_mode,
         "caption_column": args.caption_column,
         "video_column": args.video_column,
         "max_num_frames": args.max_num_frames,
@@ -432,13 +435,7 @@ def main():
         "random_flip": args.random_flip,
         "image_to_video": args.save_image_latents,
     }
-    if args.video_reshape_mode is None:
+    dataset = VideoDatasetWithFlexibleResize(**dataset_init_kwargs)
-        dataset = VideoDatasetWithResizing(**dataset_init_kwargs)
-    else:
-        dataset = VideoDatasetWithResizeAndRectangleCrop(
-            video_reshape_mode=args.video_reshape_mode, **dataset_init_kwargs
-        )
-
     original_dataset_size = len(dataset)
 
     # Split data among GPUs

training/mochi-1/prepare_dataset.sh

@@ -11,8 +11,8 @@ VIDEO_COLUMN="videos.txt"
 OUTPUT_DIR="/home/sayak/cogvideox-factory/video-dataset-disney/mochi-1/preprocessed-dataset"
 HEIGHT_BUCKETS="480"
 WIDTH_BUCKETS="848"
-FRAME_BUCKETS="1 84"
+FRAME_BUCKETS="85"
-MAX_NUM_FRAMES="84"
+MAX_NUM_FRAMES="85"
 MAX_SEQUENCE_LENGTH=256
 TARGET_FPS=30
 BATCH_SIZE=4

training/mochi-1/text_to_video_lora.py

@@ -54,7 +54,7 @@ from tqdm.auto import tqdm
 from transformers import AutoTokenizer, T5EncoderModel
 
 from args import get_args  # isort:skip
-from dataset_mochi import VideoDatasetWithResizing, VideoDatasetWithResizeAndRectangleCrop  # isort:skip
+from dataset_mochi import VideoDatasetWithFlexibleResize  # isort:skip
 
 import sys
 sys.path.append("..")
@@ -309,7 +309,6 @@ def main(args):
         variant=args.variant,
     )
     scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
-    # noise_scheduler_copy = FlowMatchEulerDiscreteScheduler.from_config(scheduler.config, invert_sigmas=False)
     noise_scheduler_copy = copy.deepcopy(scheduler)
 
     vae_config = AutoencoderKLMochi.load_config(args.pretrained_model_name_or_path, subfolder="vae")
@@ -347,7 +346,8 @@ def main(args):
         )
     
     transformer.requires_grad_(False)
-    transformer.to(accelerator.device, dtype=weight_dtype)
+    # transformer.to(accelerator.device, dtype=weight_dtype)
+    transformer.to(accelerator.device)
     if args.gradient_checkpointing:
         transformer.enable_gradient_checkpointing()
 
@@ -423,9 +423,8 @@ def main(args):
         # Make sure the trainable params are in float32. This is again needed since the base models
         # are in `weight_dtype`. More details:
         # https://github.com/huggingface/diffusers/pull/6514#discussion_r1449796804
-        if args.mixed_precision == "fp16":
+        # only upcast trainable parameters (LoRA) into fp32
-            # only upcast trainable parameters (LoRA) into fp32
+        cast_training_params([transformer_])
-            cast_training_params([transformer_])
 
     accelerator.register_save_state_pre_hook(save_model_hook)
     accelerator.register_load_state_pre_hook(load_model_hook)
@@ -439,11 +438,8 @@ def main(args):
         args.learning_rate = (
             args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
         )
-
+    # only upcast trainable parameters (LoRA) into fp32
-    # Make sure the trainable params are in float32.
+    cast_training_params([transformer], dtype=torch.float32)
-    if args.mixed_precision == "fp16":
-        # only upcast trainable parameters (LoRA) into fp32
-        cast_training_params([transformer], dtype=torch.float32)
 
     transformer_lora_parameters = list(filter(lambda p: p.requires_grad, transformer.parameters()))
 
@@ -488,6 +484,7 @@ def main(args):
     # Dataset and DataLoader
     dataset_init_kwargs = {
         "data_root": args.data_root,
+        "video_reshape_mode": args.video_reshape_mode,
         "dataset_file": args.dataset_file,
         "caption_column": args.caption_column,
         "video_column": args.video_column,
@@ -499,15 +496,8 @@ def main(args):
         "load_tensors": args.load_tensors,
         "random_flip": args.random_flip,
     }
-    if args.video_reshape_mode is None:
+    train_dataset = VideoDatasetWithFlexibleResize(**dataset_init_kwargs)
-        train_dataset = VideoDatasetWithResizing(**dataset_init_kwargs)
-    else:
-        train_dataset = VideoDatasetWithResizeAndRectangleCrop(
-            video_reshape_mode=args.video_reshape_mode, **dataset_init_kwargs
-        )
-
     collate_fn = CollateFunction(weight_dtype, args.load_tensors)
-
     train_dataloader = DataLoader(
         train_dataset,
         batch_size=1,
@@ -635,7 +625,6 @@ def main(args):
         sigmas = noise_scheduler_copy.sigmas.to(device=accelerator.device, dtype=dtype)
         schedule_timesteps = noise_scheduler_copy.timesteps.to(accelerator.device)
         timesteps = timesteps.to(accelerator.device)
-        # notice the reverse.
         step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
 
         sigma = sigmas[step_indices].flatten()
@@ -944,6 +933,7 @@ def main(args):
                 commit_message="End of training",
                 ignore_patterns=["step_*", "epoch_*", "*.bin", "*.pt"],
             )
+            accelerator.print(f"Params pushed to {repo_id}.")
 
     accelerator.end_training()
 

training/mochi-1/train.sh

@@ -16,21 +16,17 @@ cmd="accelerate launch --config_file deepspeed.yaml --gpu_ids $GPU_IDS text_to_v
   --id_token BW_STYLE \
   --height_buckets 480 \
   --width_buckets 848 \
-  --frame_buckets 84 \
+  --frame_buckets 85 \
   --load_tensors \
-  --validation_prompt \"BW_STYLE A black and white animated scene unfolds with an anthropomorphic goat surrounded by musical notes and symbols, suggesting a playful environment. Mickey Mouse appears, leaning forward in curiosity as the goat remains still. The goat then engages with Mickey, who bends down to converse or react. The dynamics shift as Mickey grabs the goat, potentially in surprise or playfulness, amidst a minimalistic background. The scene captures the evolving relationship between the two characters in a whimsical, animated setting, emphasizing their interactions and emotions\" \
-  --validation_prompt_separator ::: \
-  --num_validation_videos 1 \
-  --validation_epochs 1 \
   --seed 42 \
   --rank 64 \
   --lora_alpha 64 \
   --mixed_precision bf16 \
   --output_dir /raid/.cache/huggingface/sayak/mochi-lora/ \
-  --max_num_frames 84 \
+  --max_num_frames 85 \
   --train_batch_size 1 \
   --dataloader_num_workers 4 \
-  --max_train_steps 500 \
+  --max_train_steps 10 \
   --checkpointing_steps 50 \
   --gradient_accumulation_steps 4 \
   --gradient_checkpointing \