stable-diffusion-x4-upscaler
Maintainer: stabilityai
619
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| 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-x4-upscaler model is a text-guided latent upscaling diffusion model developed by StabilityAI. It is trained on a 10M subset of the LAION dataset containing images larger than 2048x2048 pixels. The model takes a low-resolution input image and a text prompt as inputs, and generates a higher-resolution version of the image (4x upscaling) based on the provided text. This model can be used to enhance the resolution of images generated by other Stable Diffusion models, such as stable-diffusion-2 or stable-diffusion.
Model inputs and outputs
Inputs
- Low-resolution input image: The model takes a low-resolution input image, which it will then upscale to a higher resolution.
- Text prompt: The model uses a text prompt to guide the upscaling process, allowing the model to generate an image that matches the provided description.
- Noise level: The model also takes a "noise level" input parameter, which can be used to add noise to the low-resolution input according to a predefined diffusion schedule.
Outputs
- High-resolution output image: The model generates a high-resolution (4x upscaled) version of the input image based on the provided text prompt.
Capabilities
The stable-diffusion-x4-upscaler model can be used to enhance the resolution of images generated by other Stable Diffusion models, while maintaining the semantic content and visual quality of the original image. This can be particularly useful for creating high-quality images for applications such as digital art, graphic design, or visualization.
What can I use it for?
The stable-diffusion-x4-upscaler model can be used for a variety of applications that require high-resolution images, such as:
- Digital art and illustration: Use the model to upscale and enhance the resolution of digital artwork and illustrations.
- Graphic design: Incorporate the model into your graphic design workflow to create high-quality assets and visuals.
- Visual content creation: Leverage the model to generate high-resolution images for presentations, social media, or other visual content.
- Research and development: Explore the capabilities of the model and its potential applications in various research domains, such as computer vision and image processing.
Things to try
One interesting aspect of the stable-diffusion-x4-upscaler model is its ability to use the provided text prompt to guide the upscaling process. This allows you to experiment with different prompts and see how the model's output changes. For example, you could try upscaling the same low-resolution image with different prompts, such as "a detailed landscape painting" or "a vibrant cityscape at night", and observe how the model's interpretation of the image differs.
Another thing to explore is the effect of the "noise level" input parameter. By adjusting the noise level, you can control the amount of noise added to the low-resolution input, which can impact the final output quality and visual characteristics.
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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