instructir
Maintainer: mv-lab
404
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| API Spec | View on Replicate |
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| Paper Link | View on Arxiv |
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Model overview
InstructIR is a high-quality image restoration model that can recover clean images from various types of degradation, such as noise, rain, blur, and haze. The model takes a degraded image and a human-written prompt as input, and generates a restored, high-quality output image. This approach is unique compared to similar models like gfpgan, which focuses on face restoration, or supir, which uses a large language model for image restoration. InstructIR is designed to be a versatile, all-in-one image restoration tool that can handle a wide range of degradation types.
Model inputs and outputs
InstructIR takes two inputs: an image and a human-written instruction prompt. The image can be any type of degraded or low-quality image, and the prompt should describe the desired restoration or enhancement. The model then generates a high-quality, restored output image that follows the instruction.
Inputs
- Image: The degraded or low-quality input image
- Prompt: A human-written instruction describing the desired restoration or enhancement
Outputs
- Image: The restored, high-quality output image
Capabilities
InstructIR can perform a variety of image restoration tasks, including denoising, deraining, deblurring, dehazing, and low-light enhancement. The model achieves state-of-the-art results on several benchmarks, outperforming previous all-in-one restoration methods by over 1 dB. This makes InstructIR a powerful tool for a wide range of applications, from computational photography to image editing and restoration.
What can I use it for?
InstructIR can be used for a variety of image-related tasks, such as:
- Restoring old or damaged photos
- Enhancing low-light or hazy images
- Removing unwanted elements like rain, snow, or lens flare
- Sharpening blurry images
- Improving the quality of AI-generated images
The model's ability to follow human instructions makes it a versatile tool for both professional and amateur users. For example, a photographer could use InstructIR to quickly remove unwanted elements from their images, while a designer could use it to enhance the visual quality of their work.
Things to try
One interesting aspect of InstructIR is its ability to handle different types of image degradation simultaneously. For example, you could try inputting an image with both noise and blur, and then providing a prompt like "Remove the noise and sharpen the image." The model should be able to restore the image, addressing both issues in a single step.
Another thing to try is experimenting with more creative or subjective prompts. For instance, you could try prompts like "Make this image look like a professional portrait" or "Apply a cinematic style to this landscape." The model's ability to understand and respond to these types of instructions is an exciting area of research and development.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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