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Real-Time Stable Diffusion-Based Image Super-Resolution. AdcSR super-resolves a 128×128 image to 512×512 in just 0.03s 🚀 on an A100 GPU
With Cog (recommended)
cog predict -i image=@input.jpg
From Python
from predict import Predictor
predictor = Predictor()
predictor.setup()
result = predictor.predict(image="input.jpg")
⚙️ Main Parameters
image: Input image (required)scale_factor: Fixed at 4x (also supports 2x)- No text prompt required
📝 Usage Tips
- Best Results: Use with natural photographs and realistic images
- Input Quality: Higher quality inputs produce better outputs
- File Size: Consider input image size for optimal processing speed
- Batch Processing: Process multiple images for maximum efficiency
📝 Important Notes
- Model weights (
net_params_200.pkl) are downloaded automatically from HuggingFace - AdcSR is optimized for 4x, but also supports 2x
- If AdcSR fails, bicubic is used as fallback
📄 License
MIT License – see LICENSE
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🙏 Credits
Original Research: AdcSR - Adversarial Diffusion Compression for Real-World Image Super-Resolution
Model Authors: - Research team behind the AdcSR methodology - Original implementation and training - HuggingFace Model
Base Architecture: - Stable Diffusion (Stability AI) - Diffusion model components from the diffusers library
This model is intended for research and creative applications. Please ensure appropriate licensing for commercial use.