stable-diffusion-high-resolution
Maintainer: cjwbw
72
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | No paper link provided |
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Model overview
stable-diffusion-high-resolution is a Cog implementation of a text-to-image model that generates detailed, high-resolution images. It builds upon the popular Stable Diffusion model by applying the GOBIG mode from progrockdiffusion and using Real-ESRGAN for upscaling. This results in images with more intricate details and higher resolutions compared to the original Stable Diffusion output.
Model inputs and outputs
stable-diffusion-high-resolution takes a text prompt as input and generates a high-resolution image as output. The model first creates a standard Stable Diffusion image, then upscales it and applies further refinement to produce the final detailed result.
Inputs
- Prompt: The text description used to generate the image.
- Seed: The seed value used for reproducible sampling.
- Scale: The unconditional guidance scale, which controls the balance between the text prompt and the model's own prior.
- Steps: The number of sampling steps used to generate the image.
- Width/Height: The dimensions of the original Stable Diffusion output image, which will be doubled in the final high-resolution result.
Outputs
- Image: A high-resolution image generated from the input prompt.
Capabilities
stable-diffusion-high-resolution can generate detailed, photorealistic images from text prompts, with a higher level of visual complexity and fidelity compared to the standard Stable Diffusion model. The upscaling and refinement steps allow for the creation of intricate, high-quality images that can be useful for various creative and design applications.
What can I use it for?
With its ability to produce detailed, high-resolution images, stable-diffusion-high-resolution can be a powerful tool for a variety of use cases, such as digital art, concept design, product visualization, and more. The model can be particularly useful for projects that require highly realistic and visually striking imagery, such as illustrations, advertising, or game asset creation.
Things to try
Experiment with different types of prompts, such as detailed character descriptions, complex scenes, or imaginative landscapes, to see the level of detail and realism the model can achieve. You can also try adjusting the input parameters, like scale and steps, to fine-tune the output to your preferences.
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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