

SUPIR
Overview :
SUPIR is a groundbreaking image repair method utilizing the power of generative priors and model expansion. Through multi-modal techniques and advanced generative priors, SUPIR has made significant strides in intelligent and realistic image repair. Model expansion, a key catalyst within SUPIR, has significantly enhanced its capabilities, revealing new potential in image repair. We trained our model on a dataset of 20 million high-resolution, high-quality images, each accompanied by descriptive textual annotations. SUPIR can repair images based on text prompts, broadening its application range and potential. Furthermore, we introduced negative quality prompts to further improve perceived quality. We also developed a repair-guided sampling method to mitigate the fidelity issues encountered in generative repair. Experiments have demonstrated SUPIR's outstanding repair effects and its novel ability to manipulate repairs through text prompts.
Target Users :
Suitable for scenarios requiring intelligent and realistic image repair, such as photography post-processing and image restoration.
Use Cases
1. Photographers use SUPIR to repair old photographs, restoring their original quality.
2. Advertising companies utilize SUPIR to repair product images, enhancing visual appeal.
3. Museums use SUPIR to repair images of antique art, preserving cultural heritage.
Features
Image repair using generative priors
Support text prompt-guided repair
Introduce negative quality prompts to enhance perceived quality
Develop repair-guided sampling method to suppress fidelity issues in generative repair
Featured AI Tools

SUPIR
SUPIR is a groundbreaking image repair method utilizing the power of generative priors and model expansion. Through multi-modal techniques and advanced generative priors, SUPIR has made significant strides in intelligent and realistic image repair. Model expansion, a key catalyst within SUPIR, has significantly enhanced its capabilities, revealing new potential in image repair. We trained our model on a dataset of 20 million high-resolution, high-quality images, each accompanied by descriptive textual annotations. SUPIR can repair images based on text prompts, broadening its application range and potential. Furthermore, we introduced negative quality prompts to further improve perceived quality. We also developed a repair-guided sampling method to mitigate the fidelity issues encountered in generative repair. Experiments have demonstrated SUPIR's outstanding repair effects and its novel ability to manipulate repairs through text prompts.
AI Image Restoration
490.7K

Remove Background Webgpu
remove-background-webgpu is a browser-based mini-program that utilizes WebGPU technology to achieve fast image background removal. It allows users to quickly obtain images without backgrounds without downloading any additional software.
AI Image Editing
227.1K