supir
Maintainer: zust-ai
33
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
SUPIR is a image restoration model developed by zust-ai that aims to produce high-quality, photo-realistic images from low-quality inputs. It builds on similar models like zust-diffusion, SUPIR-v0F, and SUPIR-v0Q, which also focus on image restoration and enhancement.
Model inputs and outputs
SUPIR takes in a low-quality input image and various parameters to control the image generation process, such as guidance scale, churn, noise, and upscaling ratio. It then outputs a high-quality, photo-realistic image.
Inputs
- Image: Low quality input image
- Seed: Random seed (leave blank to randomize)
- S Cfg: Classifier-free guidance scale for prompts
- S Churn: Original churn parameter of EDM
- S Noise: Original noise parameter of EDM
- Upscale: Upsampling ratio of the input image
- A Prompt: Additive positive prompt for the inputs
- Min Size: Minimum resolution of output images
- N Prompt: Negative prompt for the inputs
- S Stage1: Control Strength of Stage1 (negative means invalid)
- S Stage2: Control Strength of Stage2
- Edm Steps: Number of steps for EDM Sampling Schedule
- Use Llava: Use LLaVA model to get captions
- Linear Cfg: Linearly (with sigma) increase CFG from 'spt_linear_CFG' to s_cfg
- Model Name: Choose a model (SUPIR-v0Q is the default)
- Color Fix Type: Color Fixing Type
- Spt Linear Cfg: Start point of linearly increasing CFG
- Linear S Stage2: Linearly (with sigma) increase s_stage2 from 'spt_linear_s_stage2' to s_stage2
- Spt Linear S Stage2: Start point of linearly increasing s_stage2
Outputs
- Output: A high-quality, photo-realistic image
Capabilities
SUPIR is capable of taking low-quality input images and restoring them to high-quality, photo-realistic outputs. It can handle a variety of image types and degradations, and the various input parameters allow for fine-tuning the generation process.
What can I use it for?
SUPIR can be used for a variety of image restoration and enhancement tasks, such as upscaling low-resolution images, removing noise and artifacts, and improving the overall quality of images. It could be particularly useful for professional photography, video production, and other creative industries that require high-quality visual assets.
Things to try
Some interesting things to try with SUPIR include experimenting with different combinations of input parameters to see how they affect the output quality, using it in conjunction with other image processing tools or workflows, and exploring its capabilities for specific use cases or creative applications.
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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