updates

training/dataset.py

@@ -22,8 +22,8 @@ decord.bridge.set_bridge("torch")
 logger = get_logger(__name__)
 
 HEIGHT_BUCKETS = [256, 320, 384, 480, 512, 576, 720, 768, 960, 1024, 1280, 1536]
-WIDTH_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]
+FRAME_BUCKETS = [16, 24, 32, 48, 64, 80, 84]
 
 
 class VideoDataset(Dataset):
@@ -144,7 +144,7 @@ class VideoDataset(Dataset):
             }
         else:
             image, video, _ = self._preprocess_video(self.video_paths[index])
-
+            if video is not None:
                 return {
                     "prompt": self.id_token + self.prompts[index],
                     "image": image,
@@ -276,12 +276,15 @@ class VideoDatasetWithResizing(VideoDataset):
         else:
             video_reader = decord.VideoReader(uri=path.as_posix())
             video_num_frames = len(video_reader)
+
             nearest_frame_bucket = min(
                 self.frame_buckets, key=lambda x: abs(x - min(video_num_frames, self.max_num_frames))
             )
+            if video_num_frames < nearest_frame_bucket:
+                # TODO: we could handle this by padding zero frames or duplicating the existing frames?
+                return None, None, None
             
             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()
@@ -344,6 +347,8 @@ class VideoDatasetWithResizeAndRectangleCrop(VideoDataset):
             nearest_frame_bucket = min(
                 self.frame_buckets, key=lambda x: abs(x - min(video_num_frames, self.max_num_frames))
             )
+            if video_num_frames < nearest_frame_bucket:
+                return None, None, None
 
             frame_indices = list(range(0, video_num_frames, video_num_frames // nearest_frame_bucket))
 
@@ -404,6 +409,7 @@ class BucketSampler(Sampler):
 
     def __iter__(self):
         for index, data in enumerate(self.data_source):
+            if data is not None:
                 video_metadata = data["video_metadata"]
                 f, h, w = video_metadata["num_frames"], video_metadata["height"], video_metadata["width"]
 

training/mochi-1/prepare_dataset.py

@@ -13,7 +13,7 @@ from typing import Any, Dict, List, Optional, Union
 
 import torch
 import torch.distributed as dist
-from diffusers import AutoencoderKL
+from diffusers import AutoencoderKLMochi
 from diffusers.training_utils import set_seed
 from diffusers.utils import export_to_video, get_logger
 from torch.utils.data import DataLoader
@@ -24,7 +24,9 @@ from transformers import T5EncoderModel, T5Tokenizer
 
 import decord  # isort:skip
 
-from ..dataset import BucketSampler, VideoDatasetWithResizing, VideoDatasetWithResizeAndRectangleCrop  # isort:skip
+import sys
+sys.path.append("..")
+from dataset import BucketSampler, VideoDatasetWithResizing, VideoDatasetWithResizeAndRectangleCrop  # isort:skip
 
 
 decord.bridge.set_bridge("torch")
@@ -485,11 +487,11 @@ def main():
         tokenizer = T5Tokenizer.from_pretrained(args.model_id, subfolder="tokenizer")
         text_encoder = T5EncoderModel.from_pretrained(
             args.model_id, subfolder="text_encoder", torch_dtype=weight_dtype
-        )
+        ).to(device)
-        text_encoder = text_encoder.to(device)
         
-        vae = AutoencoderKL.from_pretrained(args.model_id, subfolder="vae", torch_dtype=weight_dtype)
+        vae = AutoencoderKLMochi.from_pretrained(
-        vae = vae.to(device)
+            args.model_id, subfolder="vae", torch_dtype=weight_dtype
+        ).to(device)
         
         if args.use_slicing:
             vae.enable_slicing()

training/mochi-1/prepare_dataset.sh

@@ -0,0 +1,48 @@
+#!/bin/bash
+
+MODEL_ID="genmo/mochi-1-preview"
+
+NUM_GPUS=1
+
+# For more details on the expected data format, please refer to the README.
+DATA_ROOT="/home/sayak/cogvideox-factory/video-dataset-disney"  # This needs to be the path to the base directory where your videos are located.
+CAPTION_COLUMN="prompt.txt"
+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="84"
+MAX_NUM_FRAMES="84"
+MAX_SEQUENCE_LENGTH=256
+TARGET_FPS=30
+BATCH_SIZE=1
+DTYPE=fp32
+
+# To create a folder-style dataset structure without pre-encoding videos and captions
+# For Image-to-Video finetuning, make sure to pass `--save_image_latents`
+CMD_WITHOUT_PRE_ENCODING="\
+  torchrun --nproc_per_node=$NUM_GPUS \
+    prepare_dataset.py \
+      --model_id $MODEL_ID \
+      --data_root $DATA_ROOT \
+      --caption_column $CAPTION_COLUMN \
+      --video_column $VIDEO_COLUMN \
+      --output_dir $OUTPUT_DIR \
+      --height_buckets $HEIGHT_BUCKETS \
+      --width_buckets $WIDTH_BUCKETS \
+      --frame_buckets $FRAME_BUCKETS \
+      --max_num_frames $MAX_NUM_FRAMES \
+      --max_sequence_length $MAX_SEQUENCE_LENGTH \
+      --target_fps $TARGET_FPS \
+      --batch_size $BATCH_SIZE \
+      --dtype $DTYPE
+"
+
+CMD_WITH_PRE_ENCODING="$CMD_WITHOUT_PRE_ENCODING --save_latents_and_embeddings"
+
+# Select which you'd like to run
+CMD=$CMD_WITH_PRE_ENCODING
+
+echo "===== Running \`$CMD\` ====="
+eval $CMD
+echo -ne "===== Finished running script =====\n"