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name: vision-filters description: "Route GIMP-ML model-backed restoration and vision filters with explicit input, checkpoint, device, memory, and output-folder checks." disable-model-invocation: true metadata: disco-role: operating license: MIT

Vision filters

Use this sub-skill for the model-backed GIMP-ML operations that consume one or two image layers and produce a restoration, map, mask, enlarged image, or interpolated frame sequence:

• restoration: deblur, dehaze, denoise, and enlighten;
• analysis maps: monocular depth and semantic segmentation;
• portrait parsing: face parsing;
• enlargement: super-resolution;
• temporal synthesis: frame interpolation.

This is an operating router, not a model zoo. Read only the linked reference needed for the requested operation:

• model overview for checkpoint paths, observed preprocessing, device loading, and output details;
• input contracts for layer and parameter checks;
• workflows for step-by-step routing;
• troubleshooting when prerequisites or runtime behavior fail.

Hard boundary

The documented plugin contracts and source-level loading behavior are recorded here, but checkpoint inference was not verified: the checkout has no weight files, and CUDA is visible on the inspection host but a tiny allocation was blocked by host CUDA OOM. Do not claim a restored image, depth map, segmentation, portrait mask, enlarged image, or interpolated frame was produced. Do not download weights, call external model services, or invent a model version. The model asset checker only checks files.

The original integration is a GIMP Python 2 plug-in. GIMP, gimpfu, and Python 2 are unavailable for the verified host. Therefore the GIMP menu, layer mutation, progress display, and actual model execution remain unverified. Python 3 inspection imports include Torch, TorchVision, Pillow, OpenCV, and the other packages recorded by the parent skill; those imports do not prove plug-in compatibility. No OpenAI call, hosted model, or substitute checkpoint is assumed.

Routing procedure

1. Identify the operation and whether it is restoration, an output map, a portrait mask, enlargement, or temporal synthesis.
2. Validate the layer contract before loading anything. Most operations use the currently selected layer; interpolation needs distinct start and end layers.
3. Ask for an explicit generic weights root. Run python scripts/check_model_assets.py WEIGHTS_ROOT; a missing asset is a blocker, not a reason to download or substitute a checkpoint.
4. Choose CPU when CUDA is unavailable, when the user sets Force CPU, or when memory is uncertain. Otherwise CUDA is only an attempted preference, not a success guarantee. Run python scripts/probe_torch_backend.py for a no-allocation report.
5. For super-resolution, set the requested scale and use the filter/tiled mode for images around or above 400 pixels in either dimension. For interpolation, confirm the output directory is intentional and writable before starting.
6. Report the intended output layer/file naming and the verification boundary. Preserve the input layer; these operations create a result layer or files rather than silently proving an in-place edit.

Route summary

Request	Required input	First action
Deblur, dehaze, denoise, enlighten	Current selected layer	Validate layer-to-image size and RGB conversion needs
Monocular depth	Current selected layer	Expect a normalized disparity/depth visualization
Semantic segmentation	Current selected layer with a supported class	Check the documented 21-class subset
Face parsing	Portrait-only current selected layer	Reject non-portrait use as unsupported
Super-resolution	Current selected layer, scale, filter flag	Check output size and tiled-memory mode
Frame interpolation	Start layer, end layer, output folder	Check matching dimensions and writable destination

Do not use this route for inpainting, coloring, matting, face generation, clustering, or text/image service operations; those belong to other sub-skills.

Verification record

Static review covered the documented input descriptions, the nine named entry points, selected model/helper loading code, and the weight-sync manifest. The safe scripts are the only executable artifacts in this subtree. They perform no network access, credential lookup, checkpoint loading, GIMP mutation, or destructive write.

Native candidates for later verification are one current selected RGB layer for each single-layer filter, a portrait layer for face parsing, a supported-class scene for segmentation, a medium/large layer for tiled super-resolution, and two equal-sized layers plus a temporary output directory for interpolation. See the references for synthetic difficult cases and known gaps.