HYPIR - A large-scale image restoration model developed by a team from the Chinese Academy of Sciences.

What is HYPIR?

HYPIR (Harnessing Diffusion-Yielded Score Priors for Image Restoration) is an advanced image restoration model developed by the research team of Professor Dong Chao at the Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. It utilizes score priors generated by a diffusion model, combined with a generative adversarial network, to achieve efficient and high-quality image restoration. HYPIR supports personalized text-guided restoration, allowing users to customize the restoration effect by inputting text descriptions, better meeting individual needs. The model excels in several aspects, including extremely fast restoration capabilities, ultra-high resolution generation, text fidelity, and texture sharpness adjustment. It can quickly restore old photos, improve image resolution, and maintain the clarity of text and details.

HYPIR's main functions

• Rapid recoveryHYPIR supports fast, high-quality image restoration. It can complete high-resolution image restoration tasks in a short time; for example, it can restore a 1024×1024 resolution image in just 1.7 seconds on a single graphics card, which is dozens of times faster than traditional methods.
• Personalized restorationHYPIR supports text-guided image restoration. Users can customize the restoration effect by entering a text description, making it more tailored to their individual needs. For example, users can specify the style, details, etc., of the restored image.
• Restoration of old photosHYPIR effectively restores low-quality old photos, recovering their original details and colors. It excels at processing old photos, removing noise, repairing scratches and blur, making them look brand new.
• Ultra-high resolution generationHYPIR supports generating images up to 8K resolution. It preserves image detail and sharpness during restoration, producing high-quality, ultra-high-resolution images.
• Text authenticity guaranteedHYPIR maintains the clarity and integrity of text during the restoration process. Whether it's a simple logo or a complex document, HYPIR accurately restores its original form, making the text in the image clearly readable.
• Texture sharpness adjustmentHYPIR allows users to adjust the texture details of an image as needed. Users can enhance or reduce the texture sharpness of an image through simple parameter settings to achieve the desired restoration effect.
• Generative fidelity trade-offHYPIR allows users to flexibly adjust between output quality and fidelity. Users can choose to prioritize output quality or fidelity based on their specific needs to achieve the best restoration results.

HYPIR technical principles

• Pre-trained diffusion model initializes the restoration networkHYPIR uses a pre-trained diffusion model to initialize the restoration network. The core advantage lies in the fact that the diffusion model has been trained to learn the fractional field (i.e., the gradient of the log probability density of the degraded image) at different noise levels, making the internalized prior knowledge very close to the ideal restoration operation. In this way, the initial output distribution of the restoration network approximates the distribution of the natural image, ensuring that the gradient during adversarial training remains small and numerically stable. This good initialization covers almost all modes of the data, preventing mode collapse during training, and converges to high-fidelity results much faster than training from scratch.
• Single-step adversarial generative model trainingHYPIR abandons the traditional iterative diffusion model training method and instead uses a single-step adversarial generative model training. Without relying on diffusion model distillation, ControlNet adapters, or multi-step inference processes, it is more than an order of magnitude faster in training and inference than diffusion model-based methods. Experimental data shows that on a single GPU, HYPIR can restore a 1024×1024 resolution image in just 1.7 seconds, a speed improvement of tens of times compared to existing image restoration methods.

HYPIR project address

• Project official websitehttps://hypir.xpixel.group/
• Github repositoryhttps://github.com/XPixelGroup/HYPIR
• arXiv technical paperhttps://arxiv.org/pdf/2507.20590

Application scenarios of HYPIR

• High-resolution image restorationHYPIR excels in high-resolution image restoration, quickly generating images up to 8K resolution.
• Film and television restorationHYPIR can be used to restore low-quality images in film and television works, improving their resolution and clarity, and providing efficient technical support for restoration work in the film and television industry.
• Cultural heritage protectionHYPIR offers new possibilities for cultural heritage preservation, enabling the restoration and reconstruction of historical images and documents, and contributing to the digital preservation of cultural heritage.
• Digital content creationHYPIR's high efficiency and high-quality restoration capabilities have broad application prospects in the field of digital content creation, enabling the rapid generation of high-quality image materials.