yangxy/PASD

Health v2 · maintenance only

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1022 stars · 65 forks observed · 2026-10-04

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PASD (Pixel-Aware Stable Diffusion) is a Python research codebase implementing an ECCV 2024 method for realistic image super-resolution and personalized stylization using diffusion models. It provides pretrained weights, SDXL support, colorization, tiled latent upscaling for high-resolution images, and Gradio/Colab demos.

Use cases

• upscale low-resolution photos to high resolution realistically
• enhance real-world degraded images with diffusion models
• stylize photos with a personalized stable diffusion model
• colorize grayscale images
• upscale ultra high-resolution images with tiled inference
• control detail level of super-resolution output

When to choose

• you need state-of-the-art diffusion-based realistic image super-resolution
• you want pretrained PASD or PASD-SDXL weights for restoration or stylization
• you need to upscale very large images via tiled latent inference
• you want a research reference implementation from an ECCV 2024 paper

When to avoid

• you need a lightweight non-GPU image upscaler for production
• you want a polished end-user application rather than research code
• you lack a GPU or cannot run large diffusion models
• you need fast real-time processing

Facets

library · maturity active

image-processing stable-diffusion machine-learning deep-learning image-processing computer-vision artificial-intelligence deep-learning python cross-platform super-resolution stylization diffusion-models eccv2024 image-restoration research-code gpu linux

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markdown · JSON · MCP: product_card(name="yangxy/PASD")