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Automated PR - 2026-06-17

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github-actions[bot]
2026-06-17 14:06:32 +00:00
parent d6053703e0
commit 0d3d3a3855
90 changed files with 8265 additions and 4511 deletions
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packages/ltx-trainer/scripts/process_captions.py
+16 -7
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@@ -154,6 +154,17 @@ class CaptionsDataset(Dataset):
else:
raise ValueError("Expected `dataset_file` to be a path to a CSV, JSON, or JSONL file.")
def _embedding_output_path(self, media_path: Path) -> str:
"""Output `.pt` path relative to the dataset dir; mirrors `process_videos._output_relative`
so caption keys match video/audio latent keys (and absolute paths don't escape output_dir)."""
data_root = self.dataset_file.parent
resolved = data_root / media_path # pathlib: an absolute media_path overrides data_root
try:
rel = resolved.relative_to(data_root)
except ValueError:
rel = Path(*resolved.parts[1:]) if resolved.is_absolute() else resolved
return str(rel.with_suffix(".pt"))
def _load_caption_data_from_csv(self) -> dict[str, str]:
"""Load captions from a CSV file and compute output embedding paths."""
df = pd.read_csv(self.dataset_file)
@@ -166,8 +177,7 @@ class CaptionsDataset(Dataset):
caption_data = {}
for _, row in df.iterrows():
media_path = Path(row[self.media_column].strip())
# Convert media path to embedding output path (same structure, .pt extension)
output_path = str(media_path.with_suffix(".pt"))
output_path = self._embedding_output_path(media_path)
caption_data[output_path] = row[self.caption_column]
return caption_data
@@ -188,8 +198,7 @@ class CaptionsDataset(Dataset):
raise ValueError(f"Key '{self.media_column}' not found in JSON entry: {entry}")
media_path = Path(entry[self.media_column].strip())
# Convert media path to embedding output path (same structure, .pt extension)
output_path = str(media_path.with_suffix(".pt"))
output_path = self._embedding_output_path(media_path)
caption_data[output_path] = entry[self.caption_column]
return caption_data
@@ -206,8 +215,7 @@ class CaptionsDataset(Dataset):
raise ValueError(f"Key '{self.media_column}' not found in JSONL entry: {entry}")
media_path = Path(entry[self.media_column].strip())
# Convert media path to embedding output path (same structure, .pt extension)
output_path = str(media_path.with_suffix(".pt"))
output_path = self._embedding_output_path(media_path)
caption_data[output_path] = entry[self.caption_column]
return caption_data
@@ -326,7 +334,8 @@ def compute_captions_embeddings( # noqa: PLR0913
# TODO(batch-tokenization): When tokenizer supports batching, encode all prompts at once.
# For now, process one at a time:
for i in range(len(batch["prompt"])):
hidden_states, prompt_attention_mask = text_encoder.encode(batch["prompt"][i], padding_side="left")
encoded = text_encoder.encode([batch["prompt"][i]], padding_side="left")
hidden_states, prompt_attention_mask = encoded[0]
video_prompt_embeds, audio_prompt_embeds = embeddings_processor.feature_extractor(
hidden_states, prompt_attention_mask, "left"
)