InstantIR
I
Instantir
Overview :
InstantIR is a blind image restoration method based on diffusion models that can handle unknown degradation problems during testing, enhancing the model's generalization capabilities. This technology dynamically adjusts generation conditions by generating reference images during inference, thereby providing robust generation conditions. Key advantages of InstantIR include the ability to restore details in extremely degraded images, delivering realistic textures, and enabling creative image restoration through text descriptions. This technology has been jointly developed by researchers from Peking University, the InstantX team, and The Chinese University of Hong Kong, with sponsorship support from HuggingFace and fal.ai.
Target Users :
The target audience includes researchers and developers in the field of image processing, especially professionals dealing with image degradation issues. The technology of InstantIR can be applied across various domains such as image enhancement, restoration, and creative editing, aiding them in improving image quality, restoring image details, and achieving text-based image editing.
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Use Cases
Case 1: Using InstantIR to restore clarity and color of old photographs.
Case 2: Repairing images degraded by compression using InstantIR technology.
Case 3: Creating new image styles and textures based on text descriptions with InstantIR.
Features
- Dynamic generation condition adjustment: Dynamically generate reference images based on input during inference.
- Compact representation extraction: Use a pre-trained visual encoder to extract a compact representation of the input image.
- Generation prior: Decode the current diffusion latent space using the extracted representation to instantiate the generation prior.
- Adaptive sampling algorithm: Develop an adaptive sampling algorithm based on the variance of generated references that varies with degradation intensity.
- Realistic texture restoration: Capable of recovering rich and realistic texture details from real-world degraded images.
- Text-guided creative restoration: Achieve creative image restoration through text descriptions, even without explicit training on text-image paired data.
- Comparison with SOTA models: Provide comparisons in the recovery of low-quality input images against existing state-of-the-art technology models.
How to Use
1. Visit the official website of InstantIR.
2. Review the product introduction and feature descriptions on the homepage.
3. Click on the 'Code' link to access the GitHub page and obtain the project code.
4. Click on the 'Model' link to visit the HuggingFace page and download the pre-trained models.
5. Set up and run InstantIR according to the instructions in the project code documentation.
6. Input the image that needs restoration, and InstantIR will automatically process it and output the restored image.
7. For text-guided creative restoration, input the relevant text description and observe the results generated by InstantIR.
8. Evaluate the quality of the restored image and adjust parameters as needed for better results.
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