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sayakpaul
2024-11-28 12:33:25 +05:30
parent 58a06320ba
commit 2fde026d30
9 changed files with 463 additions and 1372 deletions
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training/mochi-1/args.py
+31 -119
View File
@@ -28,6 +28,11 @@ def _get_model_args(parser: argparse.ArgumentParser) -> None:
default=None,
help="The directory where the downloaded models and datasets will be stored.",
)
parser.add_argument(
"--cast_dit",
action="store_true",
help="If we should cast DiT params to a lower precision.",
)
def _get_dataset_args(parser: argparse.ArgumentParser) -> None:
@@ -38,58 +43,12 @@ def _get_dataset_args(parser: argparse.ArgumentParser) -> None:
help=("A folder containing the training data."),
)
parser.add_argument(
"--dataset_file",
type=str,
default=None,
help=("Path to a CSV file if loading prompts/video paths using this format."),
)
parser.add_argument(
"--video_column",
type=str,
default="video",
help="The column of the dataset containing videos. Or, the name of the file in `--data_root` folder containing the line-separated path to video data.",
)
parser.add_argument(
"--caption_column",
type=str,
default="text",
help="The column of the dataset containing the instance prompt for each video. Or, the name of the file in `--data_root` folder containing the line-separated instance prompts.",
)
parser.add_argument(
"--id_token",
type=str,
default=None,
help="Identifier token appended to the start of each prompt if provided.",
)
parser.add_argument(
"--height_buckets",
nargs="+",
type=int,
default=[256, 320, 384, 480, 512, 576, 720, 768, 960, 1024, 1280, 1536],
)
parser.add_argument(
"--width_buckets",
nargs="+",
type=int,
default=[256, 320, 384, 480, 512, 576, 720, 768, 848, 960, 1024, 1280, 1536],
)
parser.add_argument(
"--frame_buckets",
nargs="+",
type=int,
default=[84],
)
parser.add_argument(
"--load_tensors",
action="store_true",
help="Whether to use a pre-encoded tensor dataset of latents and prompt embeddings instead of videos and text prompts. The expected format is that saved by running the `prepare_dataset.py` script.",
)
parser.add_argument(
"--random_flip",
"--caption_dropout",
type=float,
default=None,
help="If random horizontal flip augmentation is to be used, this should be the flip probability.",
help=("Probability to drop out captions randomly."),
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
@@ -140,15 +99,31 @@ def _get_validation_args(parser: argparse.ArgumentParser) -> None:
default=False,
help="Whether or not to enable model-wise CPU offloading when performing validation/testing to save memory.",
)
parser.add_argument(
"--fps",
type=int,
default=30,
help="FPS to use when serializing the output videos.",
)
parser.add_argument(
"--height",
type=int,
default=480,
)
parser.add_argument(
"--width",
type=int,
default=848,
)
def _get_training_args(parser: argparse.ArgumentParser) -> None:
parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
parser.add_argument("--rank", type=int, default=64, help="The rank for LoRA matrices.")
parser.add_argument("--rank", type=int, default=16, help="The rank for LoRA matrices.")
parser.add_argument(
"--lora_alpha",
type=int,
default=64,
default=16,
help="The lora_alpha to compute scaling factor (lora_alpha / rank) for LoRA matrices.",
)
parser.add_argument(
@@ -156,7 +131,7 @@ def _get_training_args(parser: argparse.ArgumentParser) -> None:
nargs="+",
type=str,
default=["to_k", "to_q", "to_v", "to_out.0"],
help="Target modules to train LoRA for."
help="Target modules to train LoRA for.",
)
parser.add_argument(
"--mixed_precision",
@@ -175,43 +150,6 @@ def _get_training_args(parser: argparse.ArgumentParser) -> None:
default="mochi-lora",
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--height",
type=int,
default=480,
help="All input videos are resized to this height.",
)
parser.add_argument(
"--width",
type=int,
default=848,
help="All input videos are resized to this width.",
)
parser.add_argument(
"--video_reshape_mode",
type=str,
default=None,
help="All input videos are reshaped to this mode. Choose between ['center', 'random', 'none']",
)
parser.add_argument("--fps", type=int, default=30, help="All input videos will be used at this FPS.")
parser.add_argument(
"--max_num_frames",
type=int,
default=84,
help="All input videos will be truncated to these many frames.",
)
parser.add_argument(
"--skip_frames_start",
type=int,
default=0,
help="Number of frames to skip from the beginning of each input video. Useful if training data contains intro sequences.",
)
parser.add_argument(
"--skip_frames_end",
type=int,
default=0,
help="Number of frames to skip from the end of each input video. Useful if training data contains outro sequences.",
)
parser.add_argument(
"--train_batch_size",
type=int,
@@ -256,25 +194,6 @@ def _get_training_args(parser: argparse.ArgumentParser) -> None:
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
parser.add_argument(
"--weighting_scheme",
type=str,
default="none",
choices=["sigma_sqrt", "logit_normal", "mode", "cosmap", "none"],
help=('We default to the "none" weighting scheme for uniform sampling and uniform loss'),
)
parser.add_argument(
"--logit_mean", type=float, default=0.0, help="mean to use when using the `'logit_normal'` weighting scheme."
)
parser.add_argument(
"--logit_std", type=float, default=1.0, help="std to use when using the `'logit_normal'` weighting scheme."
)
parser.add_argument(
"--mode_scale",
type=float,
default=1.29,
help="Scale of mode weighting scheme. Only effective when using the `'mode'` as the `weighting_scheme`.",
)
parser.add_argument(
"--gradient_checkpointing",
action="store_true",
@@ -283,19 +202,18 @@ def _get_training_args(parser: argparse.ArgumentParser) -> None:
parser.add_argument(
"--learning_rate",
type=float,
default=1e-4,
default=2e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
parser.add_argument(
"--scale_lr",
action="store_true",
default=False,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
parser.add_argument(
"--lr_scheduler",
type=str,
default="constant",
default="cosine",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
@@ -304,7 +222,7 @@ def _get_training_args(parser: argparse.ArgumentParser) -> None:
parser.add_argument(
"--lr_warmup_steps",
type=int,
default=500,
default=200,
help="Number of steps for the warmup in the lr scheduler.",
)
parser.add_argument(
@@ -331,12 +249,6 @@ def _get_training_args(parser: argparse.ArgumentParser) -> None:
default=False,
help="Whether or not to use VAE tiling for saving memory.",
)
parser.add_argument(
"--noised_image_dropout",
type=float,
default=0.05,
help="Image condition dropout probability when finetuning image-to-video.",
)
def _get_optimizer_args(parser: argparse.ArgumentParser) -> None:
@@ -386,7 +298,7 @@ def _get_optimizer_args(parser: argparse.ArgumentParser) -> None:
parser.add_argument(
"--weight_decay",
type=float,
default=1e-04,
default=0.01,
help="Weight decay to use for optimizer.",
)
parser.add_argument(
training/mochi-1/dataset_mochi.py
-245
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@@ -1,245 +0,0 @@
from pathlib import Path
from typing import Any, Dict, Tuple
import numpy as np
import torch
import torch.nn as nn
from accelerate.logging import get_logger
from torchvision import transforms
from torchvision.transforms import InterpolationMode
from torchvision.transforms.functional import resize
# Must import after torch because this can sometimes lead to a nasty segmentation fault, or stack smashing error
# Very few bug reports but it happens. Look in decord Github issues for more relevant information.
import decord # isort:skip
decord.bridge.set_bridge("torch")
import sys
sys.path.append("..")
from dataset import VideoDataset as VDS
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, 85]
VAE_SPATIAL_SCALE_FACTOR = 8
VAE_TEMPORAL_SCALE_FACTOR = 6
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):
# Here, index is actually a list of data objects that we need to return.
# The BucketSampler should ideally return indices. But, in the sampler, we'd like
# to have information about num_frames, height and width. Since this is not stored
# as metadata, we need to read the video to get this information. You could read this
# information without loading the full video in memory, but we do it anyway. In order
# to not load the video twice (once to get the metadata, and once to return the loaded video
# based on sampled indices), we cache it in the BucketSampler. When the sampler is
# to yield, we yield the cache data instead of indices. So, this special check ensures
# that data is not loaded a second time. PRs are welcome for improvements.
return index
if self.load_tensors:
image_latents, video_latents, prompt_embeds, prompt_attention_mask = self._preprocess_video(self.video_paths[index])
# This is hardcoded for now.
# 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 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) + 1)
height = video_latents.size(2) * VAE_SPATIAL_SCALE_FACTOR
width = video_latents.size(3) * VAE_SPATIAL_SCALE_FACTOR
return {
"prompt": prompt_embeds,
"prompt_attention_mask": prompt_attention_mask,
"image": image_latents,
"video": video_latents,
"video_metadata": {
"num_frames": num_frames,
"height": height,
"width": width,
},
}
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,
"video": video,
"video_metadata": {
"num_frames": video.shape[0],
"height": video.shape[2],
"width": video.shape[3],
},
}
# Overriding this because we need `prompt_attention_mask`.
def _load_preprocessed_latents_and_embeds(self, path: Path) -> Tuple[torch.Tensor, torch.Tensor]:
filename_without_ext = path.name.split(".")[0]
pt_filename = f"{filename_without_ext}.pt"
# The current path is something like: /a/b/c/d/videos/00001.mp4
# We need to reach: /a/b/c/d/video_latents/00001.pt
image_latents_path = path.parent.parent.joinpath("image_latents")
video_latents_path = path.parent.parent.joinpath("video_latents")
embeds_path = path.parent.parent.joinpath("prompt_embeds")
attention_mask_path = path.parent.parent.joinpath("prompt_attention_mask")
if (
not video_latents_path.exists()
or not embeds_path.exists()
or not attention_mask_path.exists()
or (self.image_to_video and not image_latents_path.exists())
):
raise ValueError(
f"When setting the load_tensors parameter to `True`, it is expected that the `{self.data_root=}` contains three folders named `video_latents`, `prompt_embeds`, and `prompt_attention_mask`. However, these folders were not found. Please make sure to have prepared your data correctly using `prepare_data.py`. Additionally, if you're training image-to-video, it is expected that an `image_latents` folder is also present."
)
if self.image_to_video:
image_latent_filepath = image_latents_path.joinpath(pt_filename)
video_latent_filepath = video_latents_path.joinpath(pt_filename)
embeds_filepath = embeds_path.joinpath(pt_filename)
attention_mask_filepath = attention_mask_path.joinpath(pt_filename)
if not video_latent_filepath.is_file() or not embeds_filepath.is_file() or not attention_mask_filepath.is_file():
if self.image_to_video:
image_latent_filepath = image_latent_filepath.as_posix()
video_latent_filepath = video_latent_filepath.as_posix()
embeds_filepath = embeds_filepath.as_posix()
attention_mask_filepath = attention_mask_filepath.as_posix()
raise ValueError(
f"The file {video_latent_filepath=} or {embeds_filepath=} or {attention_mask_filepath=} could not be found. Please ensure that you've correctly executed `prepare_dataset.py`."
)
images = (
torch.load(image_latent_filepath, map_location="cpu", weights_only=True) if self.image_to_video else None
)
latents = torch.load(video_latent_filepath, map_location="cpu", weights_only=True)
embeds = torch.load(embeds_filepath, map_location="cpu", weights_only=True)
attention_masks = torch.load(attention_mask_filepath, map_location="cpu", weights_only=True)
return images, latents, embeds, attention_masks
class VideoDatasetWithFlexibleResize(VideoDataset):
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:
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(
self.frame_buckets, key=lambda x: abs(x - min(video_num_frames, self.max_num_frames))
)
frame_indices = list(
range(
0,
video_num_frames,
1 if video_num_frames < nearest_frame_bucket else video_num_frames // nearest_frame_bucket
)
)
frames = video_reader.get_batch(frame_indices)
# 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])
if self.video_reshape_mode in {"center", "random"}:
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: 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:
"""
Resize frames for rectangular cropping.
Args:
arr (torch.Tensor): The video frames tensor [N, C, H, W].
image_size (Tuple[int, int]): The target resolution (height, width).
"""
if arr.shape[3] / arr.shape[2] > image_size[1] / image_size[0]:
arr = resize(
arr,
size=[image_size[0], int(arr.shape[3] * image_size[0] / arr.shape[2])],
interpolation=InterpolationMode.BICUBIC,
)
else:
arr = resize(
arr,
size=[int(arr.shape[2] * image_size[1] / arr.shape[3]), image_size[1]],
interpolation=InterpolationMode.BICUBIC,
)
# Perform cropping
h, w = arr.shape[2], arr.shape[3]
delta_h, delta_w = h - image_size[0], w - image_size[1]
if self.video_reshape_mode == "random":
top, left = np.random.randint(0, delta_h + 1), np.random.randint(0, delta_w + 1)
elif self.video_reshape_mode == "center":
top, left = delta_h // 2, delta_w // 2
else:
raise NotImplementedError(f"Unsupported reshape mode: {self.video_reshape_mode}")
return transforms.functional.crop(arr, top=top, left=left, height=image_size[0], width=image_size[1])
training/mochi-1/dataset_simple.py
+50
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@@ -0,0 +1,50 @@
"""
Taken from
https://github.com/genmoai/mochi/blob/main/demos/fine_tuner/dataset.py
"""
from pathlib import Path
import click
import torch
from torch.utils.data import DataLoader, Dataset
def load_to_cpu(x):
return torch.load(x, map_location=torch.device("cpu"), weights_only=True)
class LatentEmbedDataset(Dataset):
def __init__(self, file_paths, repeat=1):
self.items = [
(Path(p).with_suffix(".latent.pt"), Path(p).with_suffix(".embed.pt"))
for p in file_paths
if Path(p).with_suffix(".latent.pt").is_file() and Path(p).with_suffix(".embed.pt").is_file()
]
self.items = self.items * repeat
print(f"Loaded {len(self.items)}/{len(file_paths)} valid file pairs.")
def __len__(self):
return len(self.items)
def __getitem__(self, idx):
latent_path, embed_path = self.items[idx]
return load_to_cpu(latent_path), load_to_cpu(embed_path)
@click.command()
@click.argument("directory", type=click.Path(exists=True, file_okay=False))
def process_videos(directory):
dir_path = Path(directory)
mp4_files = [str(f) for f in dir_path.glob("**/*.mp4") if not f.name.endswith(".recon.mp4")]
assert mp4_files, f"No mp4 files found"
dataset = LatentEmbedDataset(mp4_files)
dataloader = DataLoader(dataset, batch_size=4, shuffle=True)
for latents, embeds in dataloader:
print([(k, v.shape) for k, v in latents.items()])
if __name__ == "__main__":
process_videos()
training/mochi-1/embed.py
+111
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@@ -0,0 +1,111 @@
"""
Adapted from:
https://github.com/genmoai/mochi/blob/main/demos/fine_tuner/encode_videos.py
https://github.com/genmoai/mochi/blob/main/demos/fine_tuner/embed_captions.py
"""
import click
import torch
import torchvision
from pathlib import Path
from diffusers import AutoencoderKLMochi, MochiPipeline
from transformers import T5EncoderModel, T5Tokenizer
from tqdm.auto import tqdm
def encode_videos(model: torch.nn.Module, vid_path: Path, shape: str):
T, H, W = [int(s) for s in shape.split("x")]
assert (T - 1) % 6 == 0, "Expected T to be 1 mod 6"
video, _, metadata = torchvision.io.read_video(str(vid_path), output_format="THWC", pts_unit="secs")
fps = metadata["video_fps"]
video = video.permute(3, 0, 1, 2)
og_shape = video.shape
assert video.shape[2] == H, f"Expected {vid_path} to have height {H}, got {video.shape}"
assert video.shape[3] == W, f"Expected {vid_path} to have width {W}, got {video.shape}"
assert video.shape[1] >= T, f"Expected {vid_path} to have at least {T} frames, got {video.shape}"
if video.shape[1] > T:
video = video[:, :T]
print(f"Trimmed video from {og_shape[1]} to first {T} frames")
video = video.unsqueeze(0)
video = video.float() / 127.5 - 1.0
video = video.to(model.device)
assert video.ndim == 5
with torch.inference_mode():
with torch.autocast("cuda", dtype=torch.bfloat16):
ldist = model._encode(video)
torch.save(dict(ldist=ldist), vid_path.with_suffix(".latent.pt"))
@click.command()
@click.argument("output_dir", type=click.Path(exists=True, file_okay=False, dir_okay=True, path_type=Path))
@click.option(
"--model_id",
type=str,
help="Repo id. Should be genmo/mochi-1-preview",
default="genmo/mochi-1-preview",
)
@click.option("--shape", default="163x480x848", help="Shape of the video to encode")
@click.option("--overwrite", "-ow", is_flag=True, help="Overwrite existing latents and caption embeddings.")
def batch_process(output_dir: Path, model_id: Path, shape: str, overwrite: bool) -> None:
"""Process all videos and captions in a directory using a single GPU."""
# comment out when running on unsupported hardware
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
# Get all video paths
video_paths = list(output_dir.glob("**/*.mp4"))
if not video_paths:
print(f"No MP4 files found in {output_dir}")
return
text_paths = list(output_dir.glob("**/*.txt"))
if not text_paths:
print(f"No text files found in {output_dir}")
return
# load the models
vae = AutoencoderKLMochi.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32).to("cuda")
text_encoder = T5EncoderModel.from_pretrained(model_id, subfolder="text_encoder")
tokenizer = T5Tokenizer.from_pretrained(model_id, subfolder="tokenizer")
pipeline = MochiPipeline.from_pretrained(
model_id, text_encoder=text_encoder, tokenizer=tokenizer, transformer=None, vae=None
).to("cuda")
for idx, video_path in tqdm(enumerate(sorted(video_paths))):
print(f"Processing {video_path}")
try:
if video_path.with_suffix(".latent.pt").exists() and not overwrite:
print(f"Skipping {video_path}")
continue
# encode videos.
encode_videos(vae, vid_path=video_path, shape=shape)
# embed captions.
prompt_path = Path("/".join(str(video_path).split(".")[:-1]) + ".txt")
embed_path = prompt_path.with_suffix(".embed.pt")
if embed_path.exists() and not overwrite:
print(f"Skipping {prompt_path} - embeddings already exist")
continue
with open(prompt_path) as f:
text = f.read().strip()
with torch.inference_mode():
conditioning = pipeline.encode_prompt(prompt=[text])
conditioning = {"prompt_embeds": conditioning[0], "prompt_attention_mask": conditioning[1]}
torch.save(conditioning, embed_path)
except Exception as e:
import traceback
traceback.print_exc()
print(f"Error processing {video_path}: {str(e)}")
if __name__ == "__main__":
batch_process()
training/mochi-1/prepare_dataset.py
-682
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@@ -1,682 +0,0 @@
#!/usr/bin/env python3
import argparse
import functools
import json
import os
import pathlib
import queue
import traceback
import uuid
from concurrent.futures import ThreadPoolExecutor
from contextlib import nullcontext
from typing import Any, Dict, List, Optional, Union
import torch
import torch.distributed as dist
from dataset_mochi import VideoDatasetWithFlexibleResize
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
from torchvision import transforms
from tqdm import tqdm
from transformers import T5EncoderModel, T5Tokenizer
import decord # isort:skip
decord.bridge.set_bridge("torch")
import sys
sys.path.append("..")
from dataset import BucketSampler
logger = get_logger(__name__)
DTYPE_MAPPING = {
"fp32": torch.float32,
"fp16": torch.float16,
"bf16": torch.bfloat16,
}
def check_height(x: Any) -> int:
x = int(x)
if x % 16 != 0:
raise argparse.ArgumentTypeError(
f"`--height_buckets` must be divisible by 16, but got {x} which does not fit criteria."
)
return x
def check_width(x: Any) -> int:
x = int(x)
if x % 16 != 0:
raise argparse.ArgumentTypeError(
f"`--width_buckets` must be divisible by 16, but got {x} which does not fit criteria."
)
return x
def check_frames(x: Any) -> int:
x = int(x)
if x % 4 != 0 and x % 4 != 1:
raise argparse.ArgumentTypeError(
f"`--frames_buckets` must be of form `4 * k` or `4 * k + 1`, but got {x} which does not fit criteria."
)
return x
def get_args() -> Dict[str, Any]:
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_id",
type=str,
default="genmo/mochi-1-preview",
help="Hugging Face model ID to use for tokenizer, text encoder and VAE.",
)
parser.add_argument("--data_root", type=str, required=True, help="Path to where training data is located.")
parser.add_argument(
"--dataset_file", type=str, default=None, help="Path to CSV file containing metadata about training data."
)
parser.add_argument(
"--caption_column",
type=str,
default="caption",
help="If using a CSV file via the `--dataset_file` argument, this should be the name of the column containing the captions. If using the folder structure format for data loading, this should be the name of the file containing line-separated captions (the file should be located in `--data_root`).",
)
parser.add_argument(
"--video_column",
type=str,
default="video",
help="If using a CSV file via the `--dataset_file` argument, this should be the name of the column containing the video paths. If using the folder structure format for data loading, this should be the name of the file containing line-separated video paths (the file should be located in `--data_root`).",
)
parser.add_argument(
"--id_token",
type=str,
default=None,
help="Identifier token appended to the start of each prompt if provided.",
)
parser.add_argument(
"--height_buckets",
nargs="+",
type=check_height,
default=[256, 320, 384, 480, 512, 576, 720, 768, 960, 1024, 1280, 1536],
)
parser.add_argument(
"--width_buckets",
nargs="+",
type=check_width,
default=[256, 320, 384, 480, 512, 576, 720, 768, 848, 960, 1024, 1280, 1536],
)
parser.add_argument(
"--frame_buckets",
nargs="+",
type=check_frames,
default=[84],
)
parser.add_argument(
"--random_flip",
type=float,
default=None,
help="If random horizontal flip augmentation is to be used, this should be the flip probability.",
)
parser.add_argument(
"--dataloader_num_workers",
type=int,
default=0,
help="Number of subprocesses to use for data loading. 0 means that the data will be loaded in the main process.",
)
parser.add_argument(
"--pin_memory",
action="store_true",
help="Whether or not to use the pinned memory setting in pytorch dataloader.",
)
parser.add_argument(
"--video_reshape_mode",
type=str,
default=None,
help="All input videos are reshaped to this mode. Choose between ['center', 'random', 'none']",
)
parser.add_argument(
"--save_image_latents",
action="store_true",
help="Whether or not to encode and store image latents, which are required for image-to-video finetuning. The image latents are the first frame of input videos encoded with the VAE.",
)
parser.add_argument(
"--output_dir",
type=str,
required=True,
help="Path to output directory where preprocessed videos/latents/embeddings will be saved.",
)
parser.add_argument("--max_num_frames", type=int, default=84, help="Maximum number of frames in output video.")
parser.add_argument(
"--max_sequence_length", type=int, default=256, help="Max sequence length of prompt embeddings."
)
parser.add_argument("--target_fps", type=int, default=30, help="Frame rate of output videos.")
parser.add_argument(
"--save_latents_and_embeddings",
action="store_true",
help="Whether to encode videos/captions to latents/embeddings and save them in pytorch serializable format.",
)
parser.add_argument(
"--use_slicing",
action="store_true",
help="Whether to enable sliced encoding/decoding in the VAE. Only used if `--save_latents_and_embeddings` is also used.",
)
parser.add_argument(
"--use_tiling",
action="store_true",
help="Whether to enable tiled encoding/decoding in the VAE. Only used if `--save_latents_and_embeddings` is also used.",
)
parser.add_argument("--batch_size", type=int, default=1, help="Number of videos to process at once in the VAE.")
parser.add_argument(
"--num_decode_threads",
type=int,
default=0,
help="Number of decoding threads for `decord` to use. The default `0` means to automatically determine required number of threads.",
)
parser.add_argument(
"--dtype",
type=str,
choices=["fp32", "fp16", "bf16"],
default="fp32",
help="Data type to use when generating latents and prompt embeddings.",
)
parser.add_argument("--seed", type=int, default=42, help="Seed for reproducibility.")
parser.add_argument(
"--num_artifact_workers", type=int, default=4, help="Number of worker threads for serializing artifacts."
)
return parser.parse_args()
def _get_t5_prompt_embeds(
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
prompt: Union[str, List[str]],
num_videos_per_prompt: int = 1,
max_sequence_length: int = 226,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
text_input_ids=None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
batch_size = len(prompt)
if tokenizer is not None:
text_inputs = tokenizer(
prompt,
padding="max_length",
max_length=max_sequence_length,
truncation=True,
add_special_tokens=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_attention_mask = text_inputs.attention_mask
prompt_attention_mask = prompt_attention_mask.bool()
else:
if text_input_ids is None:
raise ValueError("`text_input_ids` must be provided when the tokenizer is not specified.")
prompt_embeds = text_encoder(text_input_ids.to(device))[0]
prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
_, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
prompt_attention_mask = prompt_attention_mask.view(batch_size, -1)
prompt_attention_mask = prompt_attention_mask.repeat(num_videos_per_prompt, 1)
return prompt_embeds, prompt_attention_mask
def encode_prompt(
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
prompt: Union[str, List[str]],
num_videos_per_prompt: int = 1,
max_sequence_length: int = 256,
device: Optional[torch.device] = None,
dtype: Optional[torch.dtype] = None,
text_input_ids=None,
):
prompt = [prompt] if isinstance(prompt, str) else prompt
prompt_embeds, prompt_attention_mask = _get_t5_prompt_embeds(
tokenizer,
text_encoder,
prompt=prompt,
num_videos_per_prompt=num_videos_per_prompt,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
text_input_ids=text_input_ids,
)
return prompt_embeds, prompt_attention_mask
def compute_prompt_embeddings(
tokenizer: T5Tokenizer,
text_encoder: T5EncoderModel,
prompts: List[str],
max_sequence_length: int,
device: torch.device,
dtype: torch.dtype,
requires_grad: bool = False,
):
ctx = nullcontext() if requires_grad else torch.no_grad()
with ctx:
prompt_embeds, prompt_attention_mask = encode_prompt(
tokenizer,
text_encoder,
prompts,
num_videos_per_prompt=1,
max_sequence_length=max_sequence_length,
device=device,
dtype=dtype,
)
return prompt_embeds, prompt_attention_mask
to_pil_image = transforms.ToPILImage(mode="RGB")
def save_image(image: torch.Tensor, path: pathlib.Path) -> None:
image = to_pil_image(image)
image.save(path)
def save_video(video: torch.Tensor, path: pathlib.Path, fps: int = 8) -> None:
video = [to_pil_image(frame) for frame in video]
export_to_video(video, path, fps=fps)
def save_prompt(prompt: str, path: pathlib.Path) -> None:
with open(path, "w", encoding="utf-8") as file:
file.write(prompt)
def save_metadata(metadata: Dict[str, Any], path: pathlib.Path) -> None:
with open(path, "w", encoding="utf-8") as file:
file.write(json.dumps(metadata))
@torch.no_grad()
def serialize_artifacts(
batch_size: int,
fps: int,
images_dir: Optional[pathlib.Path] = None,
image_latents_dir: Optional[pathlib.Path] = None,
videos_dir: Optional[pathlib.Path] = None,
video_latents_dir: Optional[pathlib.Path] = None,
prompts_dir: Optional[pathlib.Path] = None,
prompt_embeds_dir: Optional[pathlib.Path] = None,
prompt_attention_mask_dir: Optional[pathlib.Path] = None,
images: Optional[torch.Tensor] = None,
image_latents: Optional[torch.Tensor] = None,
videos: Optional[torch.Tensor] = None,
video_latents: Optional[torch.Tensor] = None,
prompts: Optional[List[str]] = None,
prompt_embeds: Optional[torch.Tensor] = None,
prompt_attention_mask: Optional[torch.Tensor] = None
) -> None:
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)
data_folder_mapper_list = [
(images, images_dir, lambda img, path: save_image(img[0], path), "png"),
(image_latents, image_latents_dir, torch.save, "pt"),
(videos, videos_dir, functools.partial(save_video, fps=fps), "mp4"),
(video_latents, video_latents_dir, torch.save, "pt"),
(prompts, prompts_dir, save_prompt, "txt"),
(prompt_embeds, prompt_embeds_dir, torch.save, "pt"),
(prompt_attention_mask, prompt_attention_mask_dir, torch.save, "pt"),
(metadata, videos_dir, save_metadata, "txt"),
]
filenames = [uuid.uuid4() for _ in range(batch_size)]
for data, folder, save_fn, extension in data_folder_mapper_list:
if data is None:
continue
for slice, filename in zip(data, filenames):
if isinstance(slice, torch.Tensor):
slice = slice.clone().to("cpu")
path = folder.joinpath(f"{filename}.{extension}")
save_fn(slice, path)
def save_intermediates(output_queue: queue.Queue) -> None:
while True:
try:
item = output_queue.get(timeout=30)
if item is None:
break
serialize_artifacts(**item)
except queue.Empty:
continue
@torch.no_grad()
def main():
args = get_args()
set_seed(args.seed)
output_dir = pathlib.Path(args.output_dir)
tmp_dir = output_dir.joinpath("tmp")
output_dir.mkdir(parents=True, exist_ok=True)
tmp_dir.mkdir(parents=True, exist_ok=True)
# Create task queue for non-blocking serializing of artifacts
output_queue = queue.Queue()
save_thread = ThreadPoolExecutor(max_workers=args.num_artifact_workers)
save_future = save_thread.submit(save_intermediates, output_queue)
# Initialize distributed processing
if "LOCAL_RANK" in os.environ:
local_rank = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
dist.init_process_group(backend="nccl")
world_size = dist.get_world_size()
rank = dist.get_rank()
else:
# Single GPU
local_rank = 0
world_size = 1
rank = 0
torch.cuda.set_device(rank)
# Create folders where intermediate tensors from each rank will be saved
images_dir = tmp_dir.joinpath(f"images/{rank}")
image_latents_dir = tmp_dir.joinpath(f"image_latents/{rank}")
videos_dir = tmp_dir.joinpath(f"videos/{rank}")
video_latents_dir = tmp_dir.joinpath(f"video_latents/{rank}")
prompts_dir = tmp_dir.joinpath(f"prompts/{rank}")
prompt_embeds_dir = tmp_dir.joinpath(f"prompt_embeds/{rank}")
prompt_attention_mask_dir = tmp_dir.joinpath(f"prompt_attention_mask/{rank}")
images_dir.mkdir(parents=True, exist_ok=True)
image_latents_dir.mkdir(parents=True, exist_ok=True)
videos_dir.mkdir(parents=True, exist_ok=True)
video_latents_dir.mkdir(parents=True, exist_ok=True)
prompts_dir.mkdir(parents=True, exist_ok=True)
prompt_embeds_dir.mkdir(parents=True, exist_ok=True)
prompt_attention_mask_dir.mkdir(parents=True, exist_ok=True)
weight_dtype = DTYPE_MAPPING[args.dtype]
target_fps = args.target_fps
if weight_dtype is not None:
weight_dtype = torch.float32
print("To get the best results, we set `weight_dtype` to `torch.float32`.")
# 1. Dataset
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,
"id_token": args.id_token,
"height_buckets": args.height_buckets,
"width_buckets": args.width_buckets,
"frame_buckets": args.frame_buckets,
"load_tensors": False,
"random_flip": args.random_flip,
"image_to_video": args.save_image_latents,
}
dataset = VideoDatasetWithFlexibleResize(**dataset_init_kwargs)
original_dataset_size = len(dataset)
# Split data among GPUs
if world_size > 1:
samples_per_gpu = original_dataset_size // world_size
start_index = rank * samples_per_gpu
end_index = start_index + samples_per_gpu
if rank == world_size - 1:
end_index = original_dataset_size # Make sure the last GPU gets the remaining data
# Slice the data
dataset.prompts = dataset.prompts[start_index:end_index]
dataset.video_paths = dataset.video_paths[start_index:end_index]
else:
pass
rank_dataset_size = len(dataset)
# 2. Dataloader
def collate_fn(data):
prompts = [x["prompt"] for x in data[0]]
images = None
if args.save_image_latents:
images = [x["image"] for x in data[0]]
images = torch.stack(images).to(dtype=weight_dtype, non_blocking=True)
videos = [x["video"] for x in data[0]]
videos = torch.stack(videos).to(dtype=weight_dtype, non_blocking=True)
return {
"images": images,
"videos": videos,
"prompts": prompts,
}
dataloader = DataLoader(
dataset,
batch_size=1,
sampler=BucketSampler(dataset, batch_size=args.batch_size, shuffle=True, drop_last=False),
collate_fn=collate_fn,
num_workers=args.dataloader_num_workers,
pin_memory=args.pin_memory,
)
# 3. Prepare models
device = f"cuda:{rank}"
if args.save_latents_and_embeddings:
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)
vae = AutoencoderKLMochi.from_pretrained(
args.model_id, subfolder="vae", torch_dtype=weight_dtype
).to(device)
if args.use_slicing:
vae.enable_slicing()
if args.use_tiling:
vae.enable_tiling()
# 4. Compute latents and embeddings and save
if rank == 0:
iterator = tqdm(
dataloader, desc="Encoding", total=(rank_dataset_size + args.batch_size - 1) // args.batch_size
)
else:
iterator = dataloader
for step, batch in enumerate(iterator):
try:
images = None
image_latents = None
video_latents = None
prompt_embeds = None
if args.save_image_latents:
images = batch["images"].to(device, non_blocking=True)
images = images.permute(0, 2, 1, 3, 4) # [B, C, F, H, W]
videos = batch["videos"].to(device, non_blocking=True)
videos = videos.permute(0, 2, 1, 3, 4) # [B, C, F, H, W]
prompts = batch["prompts"]
# Encode videos & images
# we run under autocast following the official recommendations of Mochi
with torch.autocast(device, torch.bfloat16, cache_enabled=False):
if args.save_latents_and_embeddings:
if args.use_slicing:
if args.save_image_latents:
encoded_slices = [vae._encode(image_slice) for image_slice in images.split(1)]
image_latents = torch.cat(encoded_slices)
image_latents = image_latents.to(memory_format=torch.contiguous_format, dtype=weight_dtype)
encoded_slices = [vae._encode(video_slice) for video_slice in videos.split(1)]
video_latents = torch.cat(encoded_slices)
else:
if args.save_image_latents:
image_latents = vae._encode(images)
image_latents = image_latents.to(memory_format=torch.contiguous_format, dtype=weight_dtype)
video_latents = vae._encode(videos)
video_latents = video_latents.to(memory_format=torch.contiguous_format, dtype=weight_dtype)
# Encode prompts
prompt_embeds, prompt_attention_mask = compute_prompt_embeddings(
tokenizer,
text_encoder,
prompts,
args.max_sequence_length,
device,
weight_dtype,
requires_grad=False,
)
if images is not None:
images = (images.permute(0, 2, 1, 3, 4) + 1) / 2
videos = (videos.permute(0, 2, 1, 3, 4) + 1) / 2
output_queue.put(
{
"batch_size": len(prompts),
"fps": target_fps,
"images_dir": images_dir,
"image_latents_dir": image_latents_dir,
"videos_dir": videos_dir,
"video_latents_dir": video_latents_dir,
"prompts_dir": prompts_dir,
"prompt_embeds_dir": prompt_embeds_dir,
"prompt_attention_mask_dir": prompt_attention_mask_dir,
"images": images,
"image_latents": image_latents,
"videos": videos,
"video_latents": video_latents,
"prompts": prompts,
"prompt_embeds": prompt_embeds,
"prompt_attention_mask": prompt_attention_mask,
}
)
except Exception:
print("-------------------------")
print(f"An exception occurred while processing data: {rank=}, {world_size=}, {step=}")
traceback.print_exc()
print("-------------------------")
# 5. Complete distributed processing
if world_size > 1:
dist.barrier()
dist.destroy_process_group()
output_queue.put(None)
save_thread.shutdown(wait=True)
save_future.result()
# 6. Combine results from each rank
if rank == 0:
print(
f"Completed preprocessing latents and embeddings. Temporary files from all ranks saved to `{tmp_dir.as_posix()}`"
)
# Move files from each rank to common directory
for subfolder, extension in [
("images", "png"),
("image_latents", "pt"),
("videos", "mp4"),
("video_latents", "pt"),
("prompts", "txt"),
("prompt_embeds", "pt"),
("prompt_attention_mask", "pt"),
("videos", "txt"),
]:
tmp_subfolder = tmp_dir.joinpath(subfolder)
combined_subfolder = output_dir.joinpath(subfolder)
combined_subfolder.mkdir(parents=True, exist_ok=True)
pattern = f"*.{extension}"
for file in tmp_subfolder.rglob(pattern):
file.replace(combined_subfolder / file.name)
# Remove temporary directories
def rmdir_recursive(dir: pathlib.Path) -> None:
for child in dir.iterdir():
if child.is_file():
child.unlink()
else:
rmdir_recursive(child)
dir.rmdir()
rmdir_recursive(tmp_dir)
# Combine prompts and videos into individual text files and single jsonl
prompts_folder = output_dir.joinpath("prompts")
prompts = []
stems = []
for filename in prompts_folder.rglob("*.txt"):
with open(filename, "r") as file:
prompts.append(file.read().strip())
stems.append(filename.stem)
prompts_txt = output_dir.joinpath("prompts.txt")
videos_txt = output_dir.joinpath("videos.txt")
data_jsonl = output_dir.joinpath("data.jsonl")
with open(prompts_txt, "w") as file:
for prompt in prompts:
file.write(f"{prompt}\n")
with open(videos_txt, "w") as file:
for stem in stems:
file.write(f"videos/{stem}.mp4\n")
with open(data_jsonl, "w") as file:
for prompt, stem in zip(prompts, stems):
video_metadata_txt = output_dir.joinpath(f"videos/{stem}.txt")
with open(video_metadata_txt, "r", encoding="utf-8") as metadata_file:
metadata = json.loads(metadata_file.read())
data = {
"prompt": prompt,
"prompt_embed": f"prompt_embeds/{stem}.pt",
"prompt_attention_mask": f"prompt_attention_mask/{stem}.pt",
"image": f"images/{stem}.png",
"image_latent": f"image_latents/{stem}.pt",
"video": f"videos/{stem}.mp4",
"video_latent": f"video_latents/{stem}.pt",
"metadata": metadata,
}
file.write(json.dumps(data) + "\n")
print(f"Completed preprocessing. All files saved to `{output_dir.as_posix()}`")
if __name__ == "__main__":
main()
training/mochi-1/prepare_dataset.sh
+5 -45
View File
@@ -1,49 +1,9 @@
#!/bin/bash
MODEL_ID="genmo/mochi-1-preview"
GPU_ID=0
VIDEO_DIR=/home/sayak/cogvideox-factory/video-dataset-disney-organized
OUTPUT_DIR=videos_prepared
NUM_GPUS=1
python trim_and_crop_videos.py $VIDEO_DIR $OUTPUT_DIR --num_frames=37 --resolution=480x848 --force_upsample
# For more details on the expected data format, please refer to the README.
DATA_ROOT="/home/sayak/cogvideox-factory/video-dataset-disney" # This needs to be the path to the base directory where your videos are located.
CAPTION_COLUMN="prompt.txt"
VIDEO_COLUMN="videos.txt"
OUTPUT_DIR="/home/sayak/cogvideox-factory/video-dataset-disney/mochi-1/preprocessed-dataset"
HEIGHT_BUCKETS="480"
WIDTH_BUCKETS="848"
FRAME_BUCKETS="85"
MAX_NUM_FRAMES="85"
MAX_SEQUENCE_LENGTH=256
TARGET_FPS=30
BATCH_SIZE=4
DTYPE=fp32
# To create a folder-style dataset structure without pre-encoding videos and captions
# For Image-to-Video finetuning, make sure to pass `--save_image_latents`
CMD_WITHOUT_PRE_ENCODING="\
torchrun --nproc_per_node=$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 \
--use_slicing \
--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 $CMD
echo -ne "===== Finished running script =====\n"
CUDA_VISIBLE_DEVICES=$GPU_ID python embed.py $OUTPUT_DIR --shape=37x480x848
training/mochi-1/text_to_video_lora.py
+123 -252
View File
@@ -13,16 +13,17 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import gc
import json
import random
from glob import glob
import logging
import math
import os
import shutil
import torch.nn.functional as F
from datetime import timedelta
from pathlib import Path
from typing import Any, Dict
from typing import Any, Dict, Tuple, List
import diffusers
import torch
@@ -44,11 +45,7 @@ from diffusers import (
)
from diffusers.models.autoencoders.vae import DiagonalGaussianDistribution
from diffusers.optimization import get_scheduler
from diffusers.training_utils import (
cast_training_params,
compute_density_for_timestep_sampling,
compute_loss_weighting_for_sd3,
)
from diffusers.training_utils import cast_training_params
from diffusers.utils import convert_unet_state_dict_to_peft, export_to_video
from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
from diffusers.utils.torch_utils import is_compiled_module
@@ -56,20 +53,17 @@ from huggingface_hub import create_repo, upload_folder
from peft import LoraConfig, get_peft_model_state_dict, set_peft_model_state_dict
from torch.utils.data import DataLoader
from tqdm.auto import tqdm
from transformers import AutoTokenizer, T5EncoderModel
from args import get_args # isort:skip
from dataset_mochi import VideoDatasetWithFlexibleResize # isort:skip
from dataset_simple import LatentEmbedDataset
import sys
sys.path.append("..")
from dataset import BucketSampler # isort:skip
from text_encoder import compute_prompt_embeddings # isort:skip
from utils import get_gradient_norm, get_optimizer, print_memory, reset_memory # isort:skip
from utils import get_optimizer, print_memory, reset_memory # isort:skip
logger = get_logger(__name__)
@@ -81,7 +75,7 @@ def save_model_card(
base_model: str = None,
validation_prompt=None,
repo_folder=None,
fps=8,
fps=30,
):
widget_dict = []
if videos is not None and len(videos) > 0:
@@ -196,29 +190,44 @@ def log_validation(
return videos
# Adapted from the original code:
# https://github.com/genmoai/mochi/blob/aba74c1b5e0755b1fa3343d9e4bd22e89de77ab1/src/genmo/mochi_preview/pipelines.py#L578
def cast_dit(model, dtype):
for name, module in model.named_modules():
if isinstance(module, torch.nn.Linear):
assert any(
n in name for n in ["time_embed", "proj_out", "blocks", "norm_out"]
), f"Unexpected linear layer: {name}"
module.to(dtype=dtype)
elif isinstance(module, torch.nn.Conv2d):
module.to(dtype=dtype)
return model
class CollateFunction:
def __init__(self, weight_dtype: torch.dtype, load_tensors: bool) -> None:
self.weight_dtype = weight_dtype
self.load_tensors = load_tensors
def __init__(self, caption_dropout: float = None) -> None:
self.caption_dropout = caption_dropout
def __call__(self, data: Dict[str, Any]) -> Dict[str, torch.Tensor]:
prompts = [x["prompt"] for x in data[0]]
prompt_attention_mask = None
def __call__(self, samples: List[Tuple[dict, torch.Tensor]]) -> Dict[str, torch.Tensor]:
ldists = torch.cat([data[0]["ldist"] for data in samples], dim=0)
z = DiagonalGaussianDistribution(ldists).sample()
assert torch.isfinite(z).all()
if self.load_tensors:
prompts = torch.stack(prompts).to(dtype=self.weight_dtype, non_blocking=True)
prompt_attention_mask = torch.stack([x["prompt_attention_mask"] for x in data[0]])
# Sample noise which we will add to the samples.
eps = torch.randn_like(z)
sigma = torch.rand(z.shape[:1], device="cpu", dtype=torch.float32)
videos = [x["video"] for x in data[0]]
videos = torch.stack(videos).to(dtype=self.weight_dtype, non_blocking=True)
prompt_embeds = torch.cat([data[1]["prompt_embeds"] for data in samples], dim=0)
prompt_attention_mask = torch.cat([data[1]["prompt_attention_mask"] for data in samples], dim=0)
if self.caption_dropout and random.random() < self.caption_dropout:
prompt_embeds.zero_()
prompt_attention_mask = prompt_attention_mask.long()
prompt_attention_mask.zero_()
prompt_attention_mask = prompt_attention_mask.bool()
out_dict = {
"videos": videos,
"prompts": prompts,
}
if prompt_attention_mask is not None:
out_dict.update({"prompt_attention_mask": prompt_attention_mask})
return out_dict
return dict(
z=z, eps=eps, sigma=sigma, prompt_embeds=prompt_embeds, prompt_attention_mask=prompt_attention_mask
)
def main(args):
@@ -281,73 +290,41 @@ def main(args):
).repo_id
# Prepare models and scheduler
if not args.load_tensors:
tokenizer = AutoTokenizer.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=args.revision,
)
text_encoder = T5EncoderModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="text_encoder",
revision=args.revision,
)
vae = AutoencoderKLMochi.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="vae",
revision=args.revision,
variant=args.variant,
)
if args.enable_slicing:
vae.enable_slicing()
if args.enable_tiling:
vae.enable_tiling()
# keep things in FP32.
text_encoder.requires_grad_(False)
text_encoder.to(accelerator.device, dtype=torch.float32)
vae.requires_grad_(False)
vae.to(accelerator.device, dtype=torch.float32)
load_dtype = torch.bfloat16 if "5b" in args.pretrained_model_name_or_path.lower() else torch.float16
transformer = MochiTransformer3DModel.from_pretrained(
args.pretrained_model_name_or_path,
subfolder="transformer",
torch_dtype=load_dtype,
revision=args.revision,
variant=args.variant,
)
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder="scheduler")
noise_scheduler_copy = copy.deepcopy(scheduler)
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
args.pretrained_model_name_or_path, subfolder="scheduler"
)
vae_config = AutoencoderKLMochi.load_config(args.pretrained_model_name_or_path, subfolder="vae")
vae_in_channels = vae_config["latent_channels"]
has_latents_mean = "latents_mean" in vae_config and vae_config["latents_mean"] is not None
has_latents_std = "latents_std" in vae_config and vae_config["latents_std"] is not None
if has_latents_mean and has_latents_std:
mean = torch.tensor(vae_config["latents_mean"])[:, None, None, None]
std = torch.tensor(vae_config["latents_mean"])[:, None, None, None]
VAE_SCALING_FACTOR = vae_config["scaling_factor"]
# For mixed precision training we cast all non-trainable weights (vae, text_encoder and transformer) to half-precision
# as these weights are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.state.deepspeed_plugin:
# DeepSpeed is handling precision, use what's in the DeepSpeed config
if (
"fp16" in accelerator.state.deepspeed_plugin.deepspeed_config
and accelerator.state.deepspeed_plugin.deepspeed_config["fp16"]["enabled"]
):
weight_dtype = torch.float16
if (
"bf16" in accelerator.state.deepspeed_plugin.deepspeed_config
and accelerator.state.deepspeed_plugin.deepspeed_config["bf16"]["enabled"]
):
weight_dtype = torch.bfloat16
else:
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# if accelerator.state.deepspeed_plugin:
# # DeepSpeed is handling precision, use what's in the DeepSpeed config
# if (
# "fp16" in accelerator.state.deepspeed_plugin.deepspeed_config
# and accelerator.state.deepspeed_plugin.deepspeed_config["fp16"]["enabled"]
# ):
# weight_dtype = torch.float16
# if (
# "bf16" in accelerator.state.deepspeed_plugin.deepspeed_config
# and accelerator.state.deepspeed_plugin.deepspeed_config["bf16"]["enabled"]
# ):
# weight_dtype = torch.bfloat16
# else:
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
if torch.backends.mps.is_available() and weight_dtype == torch.bfloat16:
# due to pytorch#99272, MPS does not yet support bfloat16.
@@ -355,11 +332,12 @@ def main(args):
"Mixed precision training with bfloat16 is not supported on MPS. Please use fp16 (recommended) or fp32 instead."
)
# keep the transformer in FP32.
transformer.requires_grad_(False)
transformer.to(accelerator.device, torch.float32)
transformer.to(accelerator.device)
if args.gradient_checkpointing:
transformer.enable_gradient_checkpointing()
if args.cast_dit:
transformer = cast_dit(transformer, weight_dtype)
# now we will add new LoRA weights to the attention layers
transformer_lora_config = LoraConfig(
@@ -492,43 +470,19 @@ def main(args):
accelerator.print(f"Using {optimizer.__class__.__name__} optimizer.")
# Dataset and DataLoader
if args.load_tensors and args.id_token:
with open(os.path.join(args.data_root, "data.jsonl")) as f:
contents = [json.loads(jline) for jline in f.read().splitlines()]
parsed_id_token = None
for content in contents:
if "id_token" in content:
parsed_id_token = content["id_token"]
if parsed_id_token is not None and parsed_id_token.strip() != args.id_token.strip():
raise ValueError(
f"Parsed `id_token` from serialized metadata is {parsed_id_token} and provided `id_token` is {args.id_token}. They should match."
)
train_vids = list(sorted(glob(f"{args.data_root}/*.mp4")))
train_vids = [v for v in train_vids if not v.endswith(".recon.mp4")]
accelerator.print(f"Found {len(train_vids)} training videos in {args.data_root}")
assert len(train_vids) > 0, f"No training data found in {args.data_root}"
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,
"max_num_frames": args.max_num_frames,
"id_token": args.id_token,
"height_buckets": args.height_buckets,
"width_buckets": args.width_buckets,
"frame_buckets": args.frame_buckets,
"load_tensors": args.load_tensors,
"random_flip": args.random_flip,
}
train_dataset = VideoDatasetWithFlexibleResize(**dataset_init_kwargs)
# keeping things in FP32 for now.
collate_fn = CollateFunction(weight_dtype=torch.float32, load_tensors=args.load_tensors)
collate_fn = CollateFunction(caption_dropout=args.caption_dropout)
train_dataset = LatentEmbedDataset(train_vids, repeat=1)
train_dataloader = DataLoader(
train_dataset,
batch_size=1,
sampler=BucketSampler(train_dataset, batch_size=args.train_batch_size, shuffle=True),
collate_fn=collate_fn,
batch_size=args.train_batch_size,
num_workers=args.dataloader_num_workers,
pin_memory=args.pin_memory,
prefetch_factor=4,
)
# Scheduler and math around the number of training steps.
@@ -636,27 +590,6 @@ def main(args):
disable=not accelerator.is_local_main_process,
)
# For DeepSpeed training
model_config = transformer.module.config if hasattr(transformer, "module") else transformer.config
if args.load_tensors:
gc.collect()
torch.cuda.empty_cache()
def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
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)
step_indices = [(schedule_timesteps == t).nonzero().item() for t in timesteps]
sigma = sigmas[step_indices].flatten()
if "invert_sigmas" in noise_scheduler_copy.config and noise_scheduler_copy.config.invert_sigmas:
# https://github.com/huggingface/diffusers/blob/99c0483b67427de467f11aa35d54678fd36a7ea2/src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py#L209
sigma = 1.0 - sigma
while len(sigma.shape) < n_dim:
sigma = sigma.unsqueeze(-1)
return sigma
for epoch in range(first_epoch, args.num_train_epochs):
transformer.train()
@@ -664,108 +597,45 @@ def main(args):
models_to_accumulate = [transformer]
with accelerator.accumulate(models_to_accumulate):
videos = batch["videos"].to(accelerator.device, non_blocking=True)
prompts = batch["prompts"]
if args.load_tensors:
prompt_attention_mask = batch["prompt_attention_mask"]
z = batch["z"]
# revisit
# if has_latents_mean and has_latents_std:
# z = (z - mean.to(z)) / std.to(z)
# Encode videos
if not args.load_tensors:
videos = videos.permute(0, 2, 1, 3, 4) # [B, C, F, H, W]
latent_dist = vae.encode(videos.to(vae.dtype)).latent_dist
else:
latent_dist = DiagonalGaussianDistribution(videos)
videos = latent_dist.sample()
if has_latents_mean and has_latents_std:
latents_mean = (
torch.tensor(vae_config["latents_mean"]).view(1, vae_in_channels, 1, 1, 1).to(videos.device, videos.dtype)
)
latents_std = (
torch.tensor(vae_config["latents_std"]).view(1, vae_in_channels, 1, 1, 1).to(videos.device, videos.dtype)
)
videos = (videos - latents_mean) * VAE_SCALING_FACTOR / latents_std
else:
videos = videos * VAE_SCALING_FACTOR
# keep in FP32 for now.
videos = videos.to(memory_format=torch.contiguous_format, dtype=torch.float32)
model_input = videos
# Encode prompts
if not args.load_tensors:
prompt_embeds, prompt_attention_mask = compute_prompt_embeddings(
tokenizer,
text_encoder,
prompts,
model_config.max_text_seq_length,
accelerator.device,
weight_dtype=weight_dtype,
requires_grad=False,
)
else:
prompt_embeds = prompts.to(weight_dtype)
prompt_attention_mask = prompt_attention_mask.to(accelerator.device)
# Sample noise that will be added to the latents
noise = torch.randn_like(model_input)
batch_size, num_channels, num_frames, height, width = model_input.shape
# Sample a random timestep for each image
# for weighting schemes where we sample timesteps non-uniformly
u = compute_density_for_timestep_sampling(
weighting_scheme=args.weighting_scheme,
batch_size=batch_size,
logit_mean=args.logit_mean,
logit_std=args.logit_std,
mode_scale=args.mode_scale,
)
# indices = (u * noise_scheduler_copy.config.num_train_timesteps).long()
# timesteps = noise_scheduler_copy.timesteps[indices].to(device=model_input.device)
# revisit.
timesteps = (u * noise_scheduler_copy.config.num_train_timesteps)
eps = batch["eps"]
sigma = batch["sigma"]
prompt_embeds = batch["prompt_embeds"]
prompt_attention_mask = batch["prompt_attention_mask"]
sigma_bcthw = sigma[:, None, None, None, None] # [B, 1, 1, 1, 1]
# Add noise according to flow matching.
# zt = (1 - texp) * x + texp * z1
sigmas = get_sigmas(
timesteps=noise_scheduler_copy.timesteps[timesteps.long()].to(device=model_input.device),
n_dim=model_input.ndim,
dtype=model_input.dtype
)
noisy_model_input = (1.0 - sigmas) * model_input + sigmas * noise # do we need to revisit this?
noisy_model_input = noisy_model_input.to(weight_dtype)
z_sigma = (1 - sigma_bcthw) * z + sigma_bcthw * eps
ut = z - eps
# Predict the noise residual
actual_num_train_timesteps = float(noise_scheduler_copy.config.num_train_timesteps)
timesteps = (1 - (timesteps / actual_num_train_timesteps)) * actual_num_train_timesteps # revisit
timesteps = timesteps.to(device=model_input.device)
model_pred = transformer(
hidden_states=noisy_model_input,
encoder_hidden_states=prompt_embeds,
encoder_attention_mask=prompt_attention_mask,
timestep=timesteps,
return_dict=False,
)[0]
# these weighting schemes use a uniform timestep sampling
# and instead post-weight the loss
weighting = compute_loss_weighting_for_sd3(weighting_scheme=args.weighting_scheme, sigmas=sigmas)
# flow matching loss
# target = noise - model_input
target = model_input - noise # as discussed with Ajay
loss = torch.mean(
(weighting * (model_pred.float() - target.float()) ** 2).reshape(batch_size, -1),
dim=1,
)
loss = loss.mean()
# (1 - sigma) because of
# https://github.com/genmoai/mochi/blob/aba74c1b5e0755b1fa3343d9e4bd22e89de77ab1/src/genmo/mochi_preview/dit/joint_model/asymm_models_joint.py#L656
# Also, we operate on the scaled version of the `timesteps` directly in the `diffusers` implementation.
timesteps = (1 - sigma) * scheduler.config.num_train_timesteps
with torch.autocast(accelerator.device.type, weight_dtype):
model_pred = transformer(
hidden_states=z_sigma,
encoder_hidden_states=prompt_embeds,
encoder_attention_mask=prompt_attention_mask,
timestep=timesteps,
return_dict=False,
)[0]
assert model_pred.shape == z.shape
loss = F.mse_loss(model_pred.float(), ut.float())
accelerator.backward(loss)
if accelerator.sync_gradients:
gradient_norm_before_clip = get_gradient_norm(transformer_lora_parameters)
accelerator.clip_grad_norm_(transformer_lora_parameters, args.max_grad_norm)
gradient_norm_after_clip = get_gradient_norm(transformer_lora_parameters)
# if accelerator.sync_gradients:
# no grad norm for now, following the original code
# https://github.com/genmoai/mochi/blob/aba74c1b5e0755b1fa3343d9e4bd22e89de77ab1/demos/fine_tuner/train.py#L380
# gradient_norm_before_clip = get_gradient_norm(transformer_lora_parameters)
# accelerator.clip_grad_norm_(transformer_lora_parameters, args.max_grad_norm)
# gradient_norm_after_clip = get_gradient_norm(transformer_lora_parameters)
if accelerator.state.deepspeed_plugin is None:
optimizer.step()
@@ -807,14 +677,14 @@ def main(args):
last_lr = lr_scheduler.get_last_lr()[0] if lr_scheduler is not None else args.learning_rate
logs = {"loss": loss.detach().item(), "lr": last_lr}
# gradnorm + deepspeed: https://github.com/microsoft/DeepSpeed/issues/4555
if accelerator.distributed_type != DistributedType.DEEPSPEED:
logs.update(
{
"gradient_norm_before_clip": gradient_norm_before_clip,
"gradient_norm_after_clip": gradient_norm_after_clip,
}
)
# # gradnorm + deepspeed: https://github.com/microsoft/DeepSpeed/issues/4555
# if accelerator.distributed_type != DistributedType.DEEPSPEED:
# logs.update(
# {
# "gradient_norm_before_clip": gradient_norm_before_clip,
# "gradient_norm_after_clip": gradient_norm_after_clip,
# }
# )
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
@@ -822,13 +692,14 @@ def main(args):
break
if global_step >= args.max_train_steps:
break
break
if accelerator.distributed_type == DistributedType.DEEPSPEED or accelerator.is_main_process:
if args.validation_prompt is not None and (epoch + 1) % args.validation_epochs == 0:
accelerator.print("===== Memory before validation =====")
print_memory(accelerator.device)
transformer.eval()
pipe = MochiPipeline.from_pretrained(
args.pretrained_model_name_or_path,
transformer=unwrap_model(transformer),
@@ -848,12 +719,12 @@ def main(args):
for validation_prompt in validation_prompts:
pipeline_args = {
"prompt": validation_prompt,
"guidance_scale": 4.5,
"guidance_scale": 6.0,
"num_inference_steps": 64,
"height": args.height,
"width": args.width,
"max_sequence_length": 256,
}
log_validation(
pipe=pipe,
args=args,
@@ -866,13 +737,14 @@ def main(args):
print_memory(accelerator.device)
reset_memory(accelerator.device)
del pipe.text_encoder
del pipe.vae
del pipe
gc.collect()
torch.cuda.empty_cache()
transformer.train()
accelerator.wait_for_everyone()
if accelerator.distributed_type == DistributedType.DEEPSPEED or accelerator.is_main_process:
@@ -884,10 +756,7 @@ def main(args):
)
# Cleanup trained models to save memory
if args.load_tensors:
del transformer
else:
del transformer, text_encoder, vae
del transformer
gc.collect()
torch.cuda.empty_cache()
@@ -922,9 +791,11 @@ def main(args):
for validation_prompt in validation_prompts:
pipeline_args = {
"prompt": validation_prompt,
"guidance_scale": 4.5,
"guidance_scale": 6.0,
"num_inference_steps": 64,
"height": args.height,
"width": args.width,
"max_sequence_length": 256,
}
video = log_validation(
training/mochi-1/train.sh
+17 -29
View File
@@ -1,50 +1,38 @@
#!/bin/bash
export NCCL_P2P_DISABLE=1
export TORCH_NCCL_ENABLE_MONITORING=0
GPU_IDS="2"
DATA_ROOT="/home/sayak/cogvideox-factory/video-dataset-disney/mochi-1/preprocessed-dataset"
CAPTION_COLUMN="prompts.txt"
VIDEO_COLUMN="videos.txt"
DATA_ROOT="/home/sayak/cogvideox-factory/training/mochi-1/videos_prepared"
MODEL="genmo/mochi-1-preview"
OUTPUT_PATH=/raid/.cache/huggingface/sayak/mochi-lora/
cmd="accelerate launch --config_file deepspeed.yaml --gpu_ids $GPU_IDS text_to_video_lora.py \
--pretrained_model_name_or_path genmo/mochi-1-preview \
--pretrained_model_name_or_path $MODEL \
--data_root $DATA_ROOT \
--caption_column $CAPTION_COLUMN \
--video_column $VIDEO_COLUMN \
--id_token BW_STYLE \
--height_buckets 480 \
--width_buckets 848 \
--frame_buckets 85 \
--load_tensors \
--seed 42 \
--rank 64 \
--lora_alpha 64 \
--mixed_precision bf16 \
--output_dir /raid/.cache/huggingface/sayak/mochi-lora/ \
--max_num_frames 85 \
--mixed_precision "bf16" \
--output_dir $OUTPUT_PATH \
--train_batch_size 1 \
--dataloader_num_workers 4 \
--max_train_steps 10 \
--checkpointing_steps 50 \
--pin_memory \
--caption_dropout 0.1 \
--max_train_steps 2000 \
--checkpointing_steps 200 \
--checkpoints_total_limit 1 \
--gradient_accumulation_steps 4 \
--gradient_checkpointing \
--learning_rate 1e-5 \
--lr_scheduler constant \
--lr_warmup_steps 0 \
--lr_num_cycles 1 \
--enable_slicing \
--enable_tiling \
--enable_model_cpu_offload \
--optimizer adamw --use_8bit \
--beta1 0.9 \
--beta2 0.95 \
--beta3 0.99 \
--weight_decay 0.001 \
--max_grad_norm 1.0 \
--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 \
--allow_tf32 \
--report_to wandb \
--push_to_hub \
--nccl_timeout 1800"
echo "Running command: $cmd"
training/mochi-1/trim_and_crop_videos.py
+126
View File
@@ -0,0 +1,126 @@
"""
Adapted from:
https://github.com/genmoai/mochi/blob/main/demos/fine_tuner/trim_and_crop_videos.py
"""
from pathlib import Path
import shutil
import click
from moviepy.editor import VideoFileClip
from tqdm import tqdm
@click.command()
@click.argument("folder", type=click.Path(exists=True, dir_okay=True))
@click.argument("output_folder", type=click.Path(dir_okay=True))
@click.option("--num_frames", "-f", type=float, default=30, help="Number of frames")
@click.option("--resolution", "-r", type=str, default="480x848", help="Video resolution")
@click.option("--force_upsample", is_flag=True, help="Force upsample.")
def truncate_videos(folder, output_folder, num_frames, resolution, force_upsample):
"""Truncate all MP4 and MOV files in FOLDER to specified number of frames and resolution"""
input_path = Path(folder)
output_path = Path(output_folder)
output_path.mkdir(parents=True, exist_ok=True)
# Parse target resolution
target_height, target_width = map(int, resolution.split("x"))
# Calculate duration
duration = (num_frames / 30) + 0.09
# Find all MP4 and MOV files
video_files = (
list(input_path.rglob("*.mp4"))
+ list(input_path.rglob("*.MOV"))
+ list(input_path.rglob("*.mov"))
+ list(input_path.rglob("*.MP4"))
)
for file_path in tqdm(video_files):
try:
relative_path = file_path.relative_to(input_path)
output_file = output_path / relative_path.with_suffix(".mp4")
output_file.parent.mkdir(parents=True, exist_ok=True)
click.echo(f"Processing: {file_path}")
video = VideoFileClip(str(file_path))
# Skip if video is too short
if video.duration < duration:
click.echo(f"Skipping {file_path} as it is too short")
continue
# Skip if target resolution is larger than input
if target_width > video.w or target_height > video.h:
if force_upsample:
click.echo(
f"{file_path} as target resolution {resolution} is larger than input {video.w}x{video.h}. So, upsampling the video."
)
video = video.resize(width=target_width, height=target_height)
else:
click.echo(
f"Skipping {file_path} as target resolution {resolution} is larger than input {video.w}x{video.h}"
)
continue
# First truncate duration
truncated = video.subclip(0, duration)
# Calculate crop dimensions to maintain aspect ratio
target_ratio = target_width / target_height
current_ratio = truncated.w / truncated.h
if current_ratio > target_ratio:
# Video is wider than target ratio - crop width
new_width = int(truncated.h * target_ratio)
x1 = (truncated.w - new_width) // 2
final = truncated.crop(x1=x1, width=new_width).resize((target_width, target_height))
else:
# Video is taller than target ratio - crop height
new_height = int(truncated.w / target_ratio)
y1 = (truncated.h - new_height) // 2
final = truncated.crop(y1=y1, height=new_height).resize((target_width, target_height))
# Set output parameters for consistent MP4 encoding
output_params = {
"codec": "libx264",
"audio": False, # Disable audio
"preset": "medium", # Balance between speed and quality
"bitrate": "5000k", # Adjust as needed
}
# Set FPS to 30
final = final.set_fps(30)
# Check for a corresponding .txt file
txt_file_path = file_path.with_suffix(".txt")
if txt_file_path.exists():
output_txt_file = output_path / relative_path.with_suffix(".txt")
output_txt_file.parent.mkdir(parents=True, exist_ok=True)
shutil.copy(txt_file_path, output_txt_file)
click.echo(f"Copied {txt_file_path} to {output_txt_file}")
else:
# Print warning in bold yellow with a warning emoji
click.echo(
f"\033[1;33m⚠️ Warning: No caption found for {file_path}, using an empty caption. This may hurt fine-tuning quality.\033[0m"
)
output_txt_file = output_path / relative_path.with_suffix(".txt")
output_txt_file.parent.mkdir(parents=True, exist_ok=True)
output_txt_file.touch()
# Write the output file
final.write_videofile(str(output_file), **output_params)
# Clean up
video.close()
truncated.close()
final.close()
except Exception as e:
click.echo(f"\033[1;31m Error processing {file_path}: {str(e)}\033[0m", err=True)
raise
if __name__ == "__main__":
truncate_videos()