supir-v0q
Maintainer: cjwbw
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| Property | Value |
|---|---|
| Model Link | View on Replicate |
| API Spec | View on Replicate |
| Github Link | View on Github |
| Paper Link | View on Arxiv |
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Model overview
The supir-v0q model is a powerful AI-based image restoration system developed by researcher cjwbw. It is designed for practicing model scaling to achieve photo-realistic image restoration in the wild. The model is built upon several state-of-the-art techniques, including the SDXL CLIP Encoder, SDXL base 1.0_0.9vae, and the LLaVA CLIP and LLaVA v1.5 13B models. Compared to similar models like GFPGAN, Real-ESRGAN, Animagine-XL-3.1, and LLaVA-13B, the supir-v0q model showcases enhanced generalization and high-quality image restoration capabilities.
Model inputs and outputs
The supir-v0q model takes low-quality input images and generates high-quality, photo-realistic output images. The model supports upscaling of the input images by a specified ratio, and it offers various options for controlling the restoration process, such as adjusting the classifier-free guidance scale, noise parameters, and the strength of the two-stage restoration pipeline.
Inputs
- Image: The low-quality input image to be restored.
- Upscale: The upsampling ratio to apply to the input image.
- S Cfg: The classifier-free guidance scale for the prompts.
- S Churn: The original churn hyper-parameter of the Energetic Diffusion Model (EDM).
- S Noise: The original noise hyper-parameter of the EDM.
- A Prompt: The additive positive prompt for the input image.
- N Prompt: The fixed negative prompt for the input image.
- S Stage1: The control strength of the first stage of the restoration pipeline.
- S Stage2: The control strength of the second stage of the restoration pipeline.
- Edm Steps: The number of steps to use for the EDM sampling scheduler.
- Color Fix Type: The type of color correction to apply, such as "None", "AdaIn", or "Wavelet".
Outputs
- Output: The high-quality, photo-realistic image restored from the input.
Capabilities
The supir-v0q model demonstrates impressive capabilities in restoring low-quality images to high-quality, photo-realistic outputs. It can handle a wide range of degradations, including noise, blur, and compression artifacts, while preserving fine details and natural textures. The model's two-stage restoration pipeline, combined with its ability to control various hyperparameters, allows for fine-tuning and optimization to achieve the desired level of image quality and fidelity.
What can I use it for?
The supir-v0q model can be particularly useful for a variety of applications, such as:
- Photo Restoration: Restoring old, damaged, or low-quality photographs to high-quality, professional-looking images.
- Image Enhancement: Improving the quality of images captured with low-end cameras or devices, making them more visually appealing.
- Creative Workflows: Enhancing the quality of reference images or source materials used in various creative fields, such as digital art, animation, and visual effects.
- Content Creation: Generating high-quality images for use in websites, social media, marketing materials, and other content-driven applications.
Creators and businesses working in these areas may find the supir-v0q model a valuable tool for improving the visual quality and impact of their projects.
Things to try
With the supir-v0q model, you can experiment with various input parameters to fine-tune the restoration process. For example, you can try adjusting the upscaling ratio, the classifier-free guidance scale, or the strength of the two-stage restoration pipeline to achieve the desired level of image quality and fidelity. Additionally, you can explore the different color correction options to find the one that best suits your needs. By leveraging the model's flexibility and customization options, you can unlock new possibilities for your image restoration and enhancement tasks.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
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The supir-v0f model is part of the SUPIR (Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild) family of models developed by cjwbw. Unlike the SUPIR model, which uses the LLaVA-13b language model, the supir-v0f model does not incorporate LLaVA-13b. Instead, it focuses on practicing model scaling for photo-realistic image restoration in the wild. The supir-v0f model can be contrasted with the SUPIR-v0Q model, which has default training settings and aims for high generalization and high image quality in most cases, while the supir-v0f model is trained with light degradation settings, with its Stage1 encoder retaining more details when facing light degradations. Model inputs and outputs The supir-v0f model takes a low-quality input image and upscales it to a higher resolution, while restoring the image to be photo-realistic. The model is designed to handle a variety of input image degradations, such as low resolution, noise, and JPEG artifacts, and can produce high-quality, detailed, and color-corrected output images. Inputs Image**: A low-quality input image to be restored and upscaled. Upscale**: The ratio by which the input image should be upscaled. S Cfg**: The classifier-free guidance scale for the prompts used to guide the image restoration process. S Churn**: The original churn hyperparameter of the Entropic Diffusion Model (EDM) used for image generation. S Noise**: The original noise hyperparameter of the EDM used for image generation. A Prompt**: An additive positive prompt to guide the image restoration process. N Prompt**: A fixed negative prompt to guide the image restoration process. S Stage1**: A control strength for the first stage of the image restoration process. S Stage2**: A control strength for the second stage of the image restoration process. Edm Steps**: The number of steps to use for the EDM sampling schedule. Color Fix Type**: The type of color fixing to apply to the output image. Linear Cfg**: Whether to linearly increase the Cfg value during the image restoration process. Linear S Stage2**: Whether to linearly increase the S Stage2 value during the image restoration process. Spt Linear Cfg**: The starting point for the linear increase in Cfg. Spt Linear S Stage2**: The starting point for the linear increase in S Stage2. Outputs Output**: A high-quality, photo-realistic image restored and upscaled from the low-quality input. Capabilities The supir-v0f model is capable of producing high-quality, detailed, and color-corrected output images from low-quality inputs. It can handle a variety of degradations, such as low resolution, noise, and JPEG artifacts, and is particularly effective at retaining details when facing light degradations. What can I use it for? The supir-v0f model can be used for a variety of photo-realistic image restoration and upscaling tasks, such as restoring old photos, enhancing low-quality images from mobile devices, or improving the visual quality of AI-generated images. It can be particularly useful for projects that require high-fidelity, detailed, and color-corrected images, such as photography, video production, or visual design. Things to try One interesting aspect of the supir-v0f model is its ability to handle light degradations effectively, thanks to its Stage1 encoder. You could try experimenting with different input images with varying levels of degradation to see how the model performs and whether the supir-v0f version outperforms the SUPIR-v0Q model in those cases. Additionally, you could explore the effects of the different hyperparameters, such as the Cfg, churn, and noise values, to see how they impact the quality and fidelity of the output images.
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supir
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