What Is HYPIR
HYPIR (Harnessing Diffusion-Yielded Score Priors for Image Restoration) is an advanced large image restoration model launched by the team of Researcher Dong Chao at the Digital Institute of the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. It leverages score priors generated by diffusion models and combines them with adversarial generative networks to achieve efficient and high-quality image restoration. HYPIR supports personalized text-guided restoration; users can customize the restoration effect by entering text descriptions to better meet individual needs. The model performs excellently in multiple aspects, including ultra-fast restoration capability, ultra-high-resolution generation, text fidelity, and texture sharpness adjustment. It can quickly repair old photos, improve image resolution, and maintain the clarity of text and details.
Main Features of HYPIR
- Ultra-Fast Restoration: HYPIR supports fast, high-quality image restoration. It can complete high-resolution image restoration tasks in a short time; for example, on a single graphics card, it can complete restoration of a 1024x1024 resolution image in only 1.7 seconds, dozens of times faster than traditional methods.
- Personalized Restoration: HYPIR supports text-guided image restoration. Users can customize the restoration effect by entering text descriptions to better meet individual needs. For example, users can specify the style, details, and so on of the restored image.
- Old Photo Repair: HYPIR can effectively repair low-quality old photos and restore their original details and colors. It performs excellently when processing old photos, can remove noise, repair scratches and blur, and make old photos look completely renewed.
- Ultra-High-Resolution Generation: HYPIR supports generating images up to 8K resolution. It can maintain the details and clarity of images during the restoration process and generate high-quality ultra-high-resolution images.
- Text Fidelity: HYPIR can maintain the clarity and completeness of text during the restoration process. Whether it is a simple sign or a complex document, HYPIR can accurately restore its original form, making the text in the image clear and readable.
- Texture Sharpness Adjustment: HYPIR supports users in adjusting the texture details of images as needed. Users can enhance or weaken the texture sharpness of an image through simple parameter settings to achieve the ideal restoration effect.
- Generation-Fidelity Trade-Off: HYPIR supports users in flexibly adjusting between generation quality and fidelity. Users can choose, according to specific needs, whether to focus more on the generation effect or on fidelity, achieving the best restoration effect.
Technical Principles of HYPIR
- Initializing the Restoration Network with a Pretrained Diffusion Model: HYPIR uses a pretrained diffusion model to initialize the restoration network. The core advantage is that the diffusion model has already been trained to learn score fields under different noise levels (that is, the gradient of the log probability density of degraded images), making the internalized prior knowledge very close to the ideal restoration operation. In this way, the initial output distribution of the restoration network can approach the natural image distribution, ensuring that the gradients of adversarial training remain small and numerically stable. This good initialization covers almost all modes of the data, prevents mode collapse during training, and converges to high-fidelity results faster than training from scratch.
- Single-Step Adversarial Generative Model Training: HYPIR abandons the traditional iterative diffusion model training approach and instead uses a single-step adversarial generative model training. It does not rely on diffusion model distillation, ControlNet adapters, or multi-step inference processes, making it more than an order of magnitude faster than diffusion-based methods in both training and inference speed. Experimental data shows that on a single GPU, HYPIR requires only 1.7 seconds to complete the restoration of a 1024×1024 resolution image, achieving a speed improvement of dozens of times compared to existing image restoration methods.
HYPIR project address
- Project official website：https://hypir.xpixel.group/
- Github repository：https://github.com/XPixelGroup/HYPIR
- arXiv technical paper：https://arxiv.org/pdf/2507.20590
HYPIR application scenarios
- High-resolution image restoration: HYPIR performs excellently in high-resolution image restoration, capable of quickly generating images up to 8K resolution.
- Film and television restoration: HYPIR can be used to restore low-quality footage in film and television works, improving their resolution and clarity, providing efficient technical support for restoration work in the film and television industry.
- Cultural heritage protection: HYPIR provides new possibilities for cultural heritage protection, capable of restoring and recovering historical images and documents, assisting in the digital preservation of cultural heritage.
- Digital content creation: HYPIR's efficiency and high-quality restoration capabilities have broad application prospects in the field of digital content creation, capable of quickly generating high-quality image materials.