A beginner's guide to the Supir-V0q model by Cjwbw on Replicate

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This is a simplified guide to an AI model called Supir-V0q maintained by Cjwbw. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.

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".