# SUPIR — Photo-Realistic Image Restoration and Upscaling

> A diffusion-based image restoration system that upscales and enhances low-quality photos to high-resolution results with realistic detail synthesis.

## Install

Save in your project root:

# SUPIR — Photo-Realistic Image Restoration and Upscaling

## Quick Use
```bash
git clone https://github.com/Fanghua-Yu/SUPIR.git
cd SUPIR
pip install -r requirements.txt
python test.py --img_dir inputs/ --save_dir outputs/ --upscale 4
```

## Introduction
SUPIR (Scaling Up to Photo-Realistic Image Restoration) combines large-scale diffusion models with a vision-language understanding module to restore degraded images with photo-realistic detail. It generates plausible textures and structures that traditional upscalers cannot recover.

## What SUPIR Does
- Restores and upscales low-resolution or degraded photographs
- Synthesizes realistic textures and fine details from low-quality input
- Uses language-guided restoration for semantic-aware enhancement
- Handles various degradation types including noise, blur, and compression
- Supports up to 4x upscaling with high perceptual quality

## Architecture Overview
SUPIR builds on SDXL as its generative backbone, adding a degradation-aware encoder that maps low-quality images into the diffusion latent space. A CLIP-based vision-language module provides semantic understanding of the image content, guiding the diffusion process to generate contextually appropriate details during the denoising steps.

## Self-Hosting & Configuration
- Requires Python 3.10+ with PyTorch 2.x and CUDA
- GPU with at least 24 GB VRAM due to SDXL backbone
- Download SDXL base model and SUPIR adapter weights separately
- Adjust denoising strength and upscale factor via command-line arguments
- Batch processing supported for multiple images

## Key Features
- Diffusion-based restoration producing photorealistic output
- Semantic guidance via vision-language model for context-aware detail
- Handles real-world degradations beyond synthetic benchmarks
- Significant quality improvement over traditional upscaling methods
- Compatible with SDXL ecosystem and community extensions

## Comparison with Similar Tools
- **Real-ESRGAN** — GAN-based upscaler; SUPIR produces more detailed and realistic textures
- **StableSR** — earlier diffusion approach; SUPIR adds language guidance for better semantics
- **Upscayl** — desktop app using Real-ESRGAN; SUPIR offers higher quality at more compute cost
- **SwinIR** — transformer-based; SUPIR leverages generative diffusion for richer detail synthesis

## FAQ
**Q: How much VRAM does it require?**
A: At least 24 GB due to the SDXL backbone; 40 GB+ recommended for large images.

**Q: Is it suitable for batch processing?**
A: Yes, though each image takes 30-60 seconds on a high-end GPU.

**Q: Does it work on faces?**
A: Yes, it handles face restoration well thanks to the semantic guidance module.

**Q: Can I reduce VRAM usage?**
A: Tiled processing and fp16 precision can lower requirements at some quality cost.

## Sources
- https://github.com/Fanghua-Yu/SUPIR
- https://supir.xpixel.group/

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Source: https://tokrepo.com/en/workflows/asset-e05af3e7
Author: AI Open Source