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import hashlib
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, Tuple
import torch
from accelerate.logging import get_logger
from safetensors.torch import load_file, save_file
from torch.utils.data import Dataset
from torchvision import transforms
from typing_extensions import override
from finetune.constants import LOG_LEVEL, LOG_NAME
from .utils import load_prompts, load_videos, preprocess_video_with_buckets, preprocess_video_with_resize
if TYPE_CHECKING:
from finetune.trainer import Trainer
# 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")
logger = get_logger(LOG_NAME, LOG_LEVEL)
class BaseT2VDataset(Dataset):
"""
Base dataset class for Text-to-Video (T2V) training.
This dataset loads prompts and videos for T2V training.
Args:
data_root (str): Root directory containing the dataset files
caption_column (str): Path to file containing text prompts/captions
video_column (str): Path to file containing video paths
device (torch.device): Device to load the data on
encode_video_fn (Callable[[torch.Tensor], torch.Tensor], optional): Function to encode videos
"""
def __init__(
self,
data_root: str,
caption_column: str,
video_column: str,
device: torch.device = None,
trainer: "Trainer" = None,
*args,
**kwargs,
) -> None:
super().__init__()
data_root = Path(data_root)
self.prompts = load_prompts(data_root / caption_column)
self.videos = load_videos(data_root / video_column)
self.device = device
self.encode_video = trainer.encode_video
self.encode_text = trainer.encode_text
self.trainer = trainer
# Check if all video files exist
if any(not path.is_file() for path in self.videos):
raise ValueError(
f"Some video files were not found. Please ensure that all video files exist in the dataset directory. Missing file: {next(path for path in self.videos if not path.is_file())}"
)
# Check if number of prompts matches number of videos
if len(self.videos) != len(self.prompts):
raise ValueError(
f"Expected length of prompts and videos to be the same but found {len(self.prompts)=} and {len(self.videos)=}. Please ensure that the number of caption prompts and videos match in your dataset."
)
def __len__(self) -> int:
return len(self.videos)
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
prompt = self.prompts[index]
video = self.videos[index]
train_resolution_str = "x".join(str(x) for x in self.trainer.args.train_resolution)
cache_dir = self.trainer.args.data_root / "cache"
video_latent_dir = cache_dir / "video_latent" / self.trainer.args.model_name / train_resolution_str
prompt_embeddings_dir = cache_dir / "prompt_embeddings"
video_latent_dir.mkdir(parents=True, exist_ok=True)
prompt_embeddings_dir.mkdir(parents=True, exist_ok=True)
prompt_hash = str(hashlib.sha256(prompt.encode()).hexdigest())
prompt_embedding_path = prompt_embeddings_dir / (prompt_hash + ".safetensors")
encoded_video_path = video_latent_dir / (video.stem + ".safetensors")
if prompt_embedding_path.exists():
prompt_embedding = load_file(prompt_embedding_path)["prompt_embedding"]
logger.debug(
f"process {self.trainer.accelerator.process_index}: Loaded prompt embedding from {prompt_embedding_path}",
main_process_only=False,
)
else:
prompt_embedding = self.encode_text(prompt)
prompt_embedding = prompt_embedding.to("cpu")
# [1, seq_len, hidden_size] -> [seq_len, hidden_size]
prompt_embedding = prompt_embedding[0]
save_file({"prompt_embedding": prompt_embedding}, prompt_embedding_path)
logger.info(f"Saved prompt embedding to {prompt_embedding_path}", main_process_only=False)
if encoded_video_path.exists():
# encoded_video = torch.load(encoded_video_path, weights_only=True)
encoded_video = load_file(encoded_video_path)["encoded_video"]
logger.debug(f"Loaded encoded video from {encoded_video_path}", main_process_only=False)
# shape of image: [C, H, W]
else:
frames = self.preprocess(video)
frames = frames.to(self.device)
# Current shape of frames: [F, C, H, W]
frames = self.video_transform(frames)
# Convert to [B, C, F, H, W]
frames = frames.unsqueeze(0)
frames = frames.permute(0, 2, 1, 3, 4).contiguous()
encoded_video = self.encode_video(frames)
# [1, C, F, H, W] -> [C, F, H, W]
encoded_video = encoded_video[0]
encoded_video = encoded_video.to("cpu")
save_file({"encoded_video": encoded_video}, encoded_video_path)
logger.info(f"Saved encoded video to {encoded_video_path}", main_process_only=False)
# shape of encoded_video: [C, F, H, W]
return {
"prompt_embedding": prompt_embedding,
"encoded_video": encoded_video,
"video_metadata": {
"num_frames": encoded_video.shape[1],
"height": encoded_video.shape[2],
"width": encoded_video.shape[3],
},
}
def preprocess(self, video_path: Path) -> torch.Tensor:
"""
Loads and preprocesses a video.
Args:
video_path: Path to the video file to load.
Returns:
torch.Tensor: Video tensor of shape [F, C, H, W] where:
- F is number of frames
- C is number of channels (3 for RGB)
- H is height
- W is width
"""
raise NotImplementedError("Subclass must implement this method")
def video_transform(self, frames: torch.Tensor) -> torch.Tensor:
"""
Applies transformations to a video.
Args:
frames (torch.Tensor): A 4D tensor representing a video
with shape [F, C, H, W] where:
- F is number of frames
- C is number of channels (3 for RGB)
- H is height
- W is width
Returns:
torch.Tensor: The transformed video tensor with the same shape as the input
"""
raise NotImplementedError("Subclass must implement this method")
class T2VDatasetWithResize(BaseT2VDataset):
"""
A dataset class for text-to-video generation that resizes inputs to fixed dimensions.
This class preprocesses videos by resizing them to specified dimensions:
- Videos are resized to max_num_frames x height x width
Args:
max_num_frames (int): Maximum number of frames to extract from videos
height (int): Target height for resizing videos
width (int): Target width for resizing videos
"""
def __init__(self, max_num_frames: int, height: int, width: int, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.max_num_frames = max_num_frames
self.height = height
self.width = width
self.__frame_transform = transforms.Compose([transforms.Lambda(lambda x: x / 255.0 * 2.0 - 1.0)])
@override
def preprocess(self, video_path: Path) -> torch.Tensor:
return preprocess_video_with_resize(
video_path,
self.max_num_frames,
self.height,
self.width,
)
@override
def video_transform(self, frames: torch.Tensor) -> torch.Tensor:
return torch.stack([self.__frame_transform(f) for f in frames], dim=0)
class T2VDatasetWithBuckets(BaseT2VDataset):
def __init__(
self,
video_resolution_buckets: List[Tuple[int, int, int]],
vae_temporal_compression_ratio: int,
vae_height_compression_ratio: int,
vae_width_compression_ratio: int,
*args,
**kwargs,
) -> None:
""" """
super().__init__(*args, **kwargs)
self.video_resolution_buckets = [
(
int(b[0] / vae_temporal_compression_ratio),
int(b[1] / vae_height_compression_ratio),
int(b[2] / vae_width_compression_ratio),
)
for b in video_resolution_buckets
]
self.__frame_transform = transforms.Compose([transforms.Lambda(lambda x: x / 255.0 * 2.0 - 1.0)])
@override
def preprocess(self, video_path: Path) -> torch.Tensor:
return preprocess_video_with_buckets(video_path, self.video_resolution_buckets)
@override
def video_transform(self, frames: torch.Tensor) -> torch.Tensor:
return torch.stack([self.__frame_transform(f) for f in frames], dim=0)
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