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name: portrait-inference description: "Run, adapt, and troubleshoot Photo2Cartoon portrait inference across PyTorch weights, ONNX weights, and Cog-like predictor surfaces." disable-model-invocation: true metadata: disco-role: operating license: MIT

Portrait Inference

Use this sub-skill when you need to work with the model-side half of Photo2Cartoon:

• validate or run the portrait checkpoints
• choose between PyTorch and ONNX inference
• understand the Cog-style prediction surface
• debug asset placement, device/provider selection, and output validation

Do not use this sub-skill for face detection, crop expansion, alignment, or segmentation math. Those details belong to the preprocessing sub-skill. Do not use it for training or checkpoint generation; that belongs to the data-and-training sub-skill. Do not use it for architecture internals; that belongs to the model-internals sub-skill.

Quick route

1. Check the asset set with scripts/check_photo2cartoon_assets.py.
2. Run the PyTorch recipe with scripts/pytorch_inference_recipe.py.
3. Run the ONNX recipe with scripts/onnx_inference_recipe.py.
4. Read references/cog-and-api-reference.md when adapting the Cog predictor surface.
5. Read references/troubleshooting.md when inference fails.

Contract

• Required external assets: photo2cartoon_weights.pt, photo2cartoon_weights.onnx, seg_model_384.pb.
• Input assumption: this sub-skill consumes the face crop produced upstream, not the raw face-crop algorithm itself.
• The model-side tensor is a resized 256 x 256 face patch with alpha mask compositing.
• PyTorch normalization: face = (face * mask + (1 - mask) * 255) / 127.5 - 1.
• Postprocess: rescale back to uint8, composite against white, then save the RGB image.
• ONNX names: input input, output output.
• Cog surface: Predictor.predict(photo: Path) returns an output path or None when no face is detected.

Validation

• Run the asset checker before attempting a real inference.
• Confirm the saved image exists, can be decoded, and has nonzero width and height.
• Treat no-face errors as upstream input issues, not model failures.
• Treat provider, device, and load failures as configuration issues before touching model internals.

References

• Inference workflows
• Cog and API reference
• Troubleshooting

Notes

• Scripts are safe by default: no downloads, no training, and no destructive writes.
• Pass explicit checkout paths and asset paths when validating a repository checkout.
• Keep preprocessing details in the preprocessing sub-skill and keep architecture internals in the model-internals sub-skill.