storytold/FineTrainers-Conditioning
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fixed the training pipeline to address new changes from upstream

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This commit is contained in:
CrossProduct
2025-01-17 01:46:35 +00:00
parent 315431be0a
commit cd66e6e58e
3 changed files with 20 additions and 26 deletions
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finetrainers/dataset.py
+8 -5
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@@ -76,7 +76,7 @@ class ImageOrVideoDataset(Dataset):
(
self.prompts,
self.video_paths,
self.pose_pathes,
self.pose_paths,
) = self._load_dataset_from_local_path()
elif dataset_file.endswith(".csv"):
(
@@ -146,12 +146,15 @@ class ImageOrVideoDataset(Dataset):
else:
video = self._preprocess_video(video_path)
if self.pose_condition_column != None:
pose_video = self._preprocess_video(self.pose_paths[index])
if self.pose_column != None:
pose = self._preprocess_video(self.pose_paths[index])
img_ref = self._preprocess_video_image_reference_video(video_path)
return {
"prompt": prompt,
"video": video,
"pose_video": pose_video,
"pose": pose,
"img_ref": img_ref,
"video_metadata": {
"num_frames": video.shape[0],
"height": video.shape[2],
@@ -268,7 +271,7 @@ class ImageOrVideoDataset(Dataset):
frames = torch.stack([self.video_transforms(frame) for frame in frames], dim=0)
return frames
def _preprocess_video_single_frame(self, path: Path) -> torch.Tensor:
def _preprocess_video_image_reference_video(self, path: Path) -> torch.Tensor:
video_reader = decord.VideoReader(uri=path.as_posix())
video_num_frames = len(video_reader)