The Qwen3.5 processor needs video_metadata (fps, frame indices) to
compute temporal position embeddings. Use return_video_metadata=True
which embeds metadata inside the video tensors as (tensor, metadata)
tuples, and return_video_kwargs=True which returns {'do_sample_frames':
False} without the problematic fps list.
Made-with: Cursor
The Qwen3.5 processor expects fps as a scalar, not a list, so passing
video_kwargs with fps=[...] fails validation. Since process_vision_info
already handles frame sampling, we only need do_sample_frames=False to
tell the processor to use the pre-sampled frames as-is.
Made-with: Cursor
The Qwen processor needs fps metadata (via return_video_kwargs=True)
to compute correct temporal position embeddings. Without it, the
processor defaults to fps=24 regardless of the actual video fps,
causing shape mismatches between expected and actual video tokens.
Made-with: Cursor
Replace manual cv2 frame reading with FORCE_QWENVL_VIDEO_READER=torchvision
env var. The torchvision backend (PyAV) properly reads video metadata and
respects the fps parameter, avoiding the torchcodec fps=24 default issue.
Made-with: Cursor
When torchcodec is installed, qwen-vl-utils ignores the fps parameter
and defaults to 24fps if video metadata is missing, causing shape
mismatches. Fix by reading video frames directly as PIL images and
passing them to the processor, bypassing torchcodec entirely.
Made-with: Cursor
The single-episode `segment_skills` method was missing
`enable_thinking=False` in `apply_chat_template`, causing the model to
output reasoning traces instead of JSON when the batch path fails and
falls back to per-episode processing.
Made-with: Cursor
