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---
title: ResShift Super-Resolution
emoji: 🖼️
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 4.0.0
app_file: app.py
pinned: false
license: mit
---
# ResShift Super-Resolution
Super-resolution using ResShift diffusion model. Upload a low-resolution image to get an enhanced, super-resolved version.
## Features
- 4x super-resolution using diffusion model
- Works in latent space for efficient processing
- Full diffusion sampling loop (15 steps)
- Real-time inference with Gradio interface
## Usage
1. Upload a low-resolution image
2. Click "Super-Resolve" or wait for automatic processing
3. Download the super-resolved output
## Model
The model is trained on DIV2K dataset and uses VQGAN for latent space encoding/decoding.
## Technical Details
- **Architecture**: U-Net with Swin Transformer blocks
- **Latent Space**: 64x64 (encoded from 256x256 pixel space)
- **Diffusion Steps**: 15 timesteps
- **Scale Factor**: 4x
## Citation
If you use this model, please cite the ResShift paper.
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