name: video-stylization
description: "Plan neural-style-tf video and frame-sequence stylization with
frame extraction, optical flow, temporal consistency, and safe command
assembly."
disable-model-invocation: true
metadata:
disco-role: operating
license: GPL 3.0
video-stylization
Use this sub-skill when the user wants neural-style transfer over a video or numbered frame sequence, especially when temporal consistency, optical-flow files, frame initialization, or video reassembly are involved.
Do not use this sub-skill for:
Operating route
- Confirm the runtime is suitable: video is expensive, the repo wrapper refuses non-GPU runs, and full execution requires VGG-19 weights plus a TensorFlow 1.x-compatible runtime.
- Read references/video-workflow.md to choose either an end-to-end video plan or a pre-extracted frame-sequence plan.
- Read references/optical-flow-files.md before using
--init_frame_type prev_warped, because the source expects specific .flo and reliable_*.txt filenames.
- Use scripts/plan_video_pipeline.py to print a non-destructive command plan. It does not delete frames, download models, run optical flow, or render unless a future operator intentionally replaces the printed placeholders.
- If a video command fails, check references/troubleshooting.md for ffmpeg/ffprobe, GPU, flow-file, frame-format, and cleanup-related failures.
Minimum inputs for a full video run
- A video file or pre-extracted frame directory.
- One or more style images, normally handled by the still-image and advanced-control routes.
- VGG-19 MatConvNet weights reachable by
neural_style.py.
ffmpeg and ffprobe or equivalent frame extraction/assembly tools when starting from a video file.
- Optical-flow and consistency files if using
--init_frame_type prev_warped or source-compatible temporal consistency.
- A compatible GPU runtime for feasible full video rendering. CPU-only video plans are useful for command review but are not practical for long videos.
Full end-to-end video execution was not selected as a required verification target for this generated skill because it is GPU-, model-weight-, and time-dependent. The verified scope covers source/static behavior and safe command planning.