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name: video-upscaling description: "Guides QualityScaler video frame extraction, resume, encoding, codec fallback, and cleanup." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

video-upscaling

Use this sub-skill when the task is about upscaling videos, extracting frames, resuming a partially completed job, choosing codecs, or troubleshooting frame/encode behavior.

Read this when

• The user wants to upscale a video rather than a still image.
• The task mentions frame extraction, resume, or keep-frames behavior.
• You need to explain how the output folder or output filename is formed for videos.
• Encoding fails and codec fallback matters.
• The job appears to stall, stop, or resume unexpectedly.

What this sub-skill owns

• VideoUpscaleTask and the video job lifecycle.
• Frame extraction, frame naming, and resume detection.
• Multiprocess frame upscaling and the save queue/thread.
• Encoding, codec selection, and libx264 fallback.
• Keep-frames cleanup and metadata copy after encode.

What belongs elsewhere

• Launch, provider, model, and asset readiness belong in setup-runtime.
• Still-image AI details belong in image-upscaling.
• Shared suffix rules and supported extensions live in the root references.

Core workflow

1. Read references/video-task-lifecycle.md for the end-to-end job sequence.
2. Read references/frame-resume-and-encode-matrix.md for codec mapping and resume rules.
3. Read references/video-failure-modes.md for frame-extraction, encoding, and stop/resume failures.
4. Use ../../scripts/derive_qualityscaler_paths.py to preview video and frame output names.

Important facts

• Video jobs build a task object before extraction begins.
• Frame extraction uses ffmpeg.exe and writes a frame_###.jpg sequence.
• Resume detection is filename-based: the app looks for already upscaled frames in the target folder.
• The image AI core from image-upscaling is reused for each frame.
• Encoding first tries the selected codec and then falls back to libx264.

Common user intents

• "Why did the video restart from scratch?" -> check the resume matrix.
• "Why did encoding fail?" -> check the codec fallback and the ffmpeg asset.
• "Why is there a frame folder left behind?" -> inspect the keep-frames setting and cleanup step.
• "What output will this video create?" -> run the path helper script.

Bundled references

• references/video-task-lifecycle.md
• references/frame-resume-and-encode-matrix.md
• references/video-failure-modes.md

Bundled scripts

• ../../scripts/derive_qualityscaler_paths.py for output-name previews.

Stop conditions

If the video job fails because the runtime, model file, or provider is missing, route back to setup-runtime before debugging the pipeline.