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name: image-and-face-apps description: "Use PaddleGAN ppgan.apps image and face application predictors for single-image generation, restoration, denoising, super-resolution, style transfer, face parsing, enhancement, cartoonization, makeup transfer, and StyleGANv2 latent workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

image-and-face-apps

Use this sub-skill when the request stays in one image, one face, or one latent code at a time.

Route here

• photo-to-anime and portrait cartoonization
• face parsing, face enhancement, and makeup transfer
• single-image denoising, deblurring, inpainting, super-resolution, and depth
• StyleGANv2 sampling, fitting, mixing, and editing
• Pixel2Style2Pixel inversion and SinGAN-style single-image generation

Do not route here

• video restoration, motion driving, or lip-sync -> video-and-audio-apps
• exported or static inference model workflows -> deployment-export
• dataset download or preprocessing work -> data-preparation
• full runs without explicit weights, media, and backend approval

Start with

1. Run python scripts/check_image_app_deps.py for a safe import/dependency check.
2. Open references/image-workflows.md to pick the right model family.
3. Open references/predictor-api.md for constructor and run() shapes.
4. Open references/troubleshooting.md when weights, dlib, CLIP, or face detection are missing.

Output rule

Use a caller-owned output directory and keep file names predictable. Prefer explicit weight_path values when reproducibility or offline use matters.

Notes

• Use installed ppgan package imports and bundled helpers, not source-checkout scripts.
• If a predictor can auto-download weights, say so before relying on that behavior.
• CLIP-guided StyleGAN editing is optional and not the default route here.