stable-diffusion-xl-refiner-0.9
Maintainer: stabilityai
326
🗣️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The stable-diffusion-xl-refiner-0.9 model is a diffusion-based text-to-image generative model developed by Stability AI. It is a Latent Diffusion Model that uses a pretrained text encoder, OpenCLIP-ViT/G. The model is not intended to be used as a pure text-to-image model, but rather as an image-to-image model to refine and denoise high-quality data. It is part of the SDXL model pipeline, which first uses a base model to generate latents and then applies a specialized high-resolution refiner model using SDEdit.
Model inputs and outputs
The stable-diffusion-xl-refiner-0.9 model takes an image as input and refines and denoises it based on the provided text prompt. It outputs the refined and denoised image.
Inputs
- Image: An input image to be refined and denoised
- Text Prompt: A text prompt describing the desired output image
Outputs
- Refined and Denoised Image: The output image with improved quality and reduced noise
Capabilities
The stable-diffusion-xl-refiner-0.9 model is capable of refining and denoising high-quality images based on text prompts. It can be used to enhance the visual fidelity of images generated by other models or to improve existing images.
What can I use it for?
The stable-diffusion-xl-refiner-0.9 model can be used for research purposes, such as:
- Generation of artworks and use in design and other artistic processes
- Applications in educational or creative tools
- Research on generative models
- Safe deployment of models which have the potential to generate harmful content
- Probing and understanding the limitations and biases of generative models
It should not be used for commercial purposes or to generate content that could be harmful or offensive.
Things to try
One interesting thing to try with the stable-diffusion-xl-refiner-0.9 model is using it in combination with the [object Object] model. The base model can be used to generate initial latents, which are then refined and denoised by the refiner model. This two-step pipeline can produce high-quality images while maintaining flexibility and control over the generation process.
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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