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name: image-upscaling description: "Guides QualityScaler still-image upscaling, tiling, blending, metadata copy, and output naming." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

image-upscaling

Use this sub-skill when the task is about upscaling photos or other still images, reasoning about the AI image core, previewing image output names, or troubleshooting image-specific channel and tiling behavior.

Read this when

• The user wants to upscale a photo or still image.
• The task asks about model choice, alpha handling, grayscale handling, or tiling.
• You need to explain the image output filename pattern.
• Metadata copy or blending behavior is relevant.
• The runtime is ready, but the image pipeline still misbehaves.

What this sub-skill owns

• AI_upscale and the still-image path through AI_orchestration.
• Image preprocessing, normalization, postprocessing, and resizing.
• Tiling decisions and tile-combination behavior.
• Image output naming, extension selection, and blending.
• Metadata copy from source image to output image.

What belongs elsewhere

• Launch, install, provider, and asset readiness belong in setup-runtime.
• Video frame extraction, resume, and encoding belong in video-upscaling.
• Shared model names, output suffix rules, and supported formats live in the root references.

Core workflow

1. Read references/ai-core-and-image-flow.md for the verified image pipeline.
2. Read references/image-tiling-and-format-matrix.md for the model, format, and tile matrix.
3. Read references/image-failure-modes.md if output colors, alpha handling, or edge pixels look wrong.
4. Use ../../scripts/derive_qualityscaler_paths.py to preview image output names.

Important facts

• The image pipeline lazily creates the ONNX session and uses the selected model file.
• The input image may be RGB, RGBA, or grayscale.
• Large images are tiled once they exceed the tile-pixel threshold.
• Output names are built from the input stem plus model, resize, and blending suffixes.
• Metadata copy is best-effort; a metadata failure does not necessarily mean the image write failed.

Common user intents

• "What happens to alpha channels?" -> read the image flow reference.
• "Why are the corners missing?" -> inspect the tiling reference and the tile-edge limitation.
• "What file will this image produce?" -> run the path helper or read the output contract reference.
• "Why did metadata not copy?" -> read the failure modes page and check for exiftool.exe.

Bundled references

• references/ai-core-and-image-flow.md
• references/image-tiling-and-format-matrix.md
• references/image-failure-modes.md

Bundled scripts

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

Stop conditions

If the problem is actually missing runtime support, a missing model file, or a GUI launch issue, route back to setup-runtime before debugging image logic.