demofusion-enhance
Maintainer: lucataco
9
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
The demofusion-enhance model is an image-to-image enhancer that uses the DemoFusion architecture. It can be used to upscale and improve the quality of input images. The model was created by lucataco, who has also developed similar models like [object Object], [object Object], [object Object], and [object Object].
Model inputs and outputs
The demofusion-enhance model takes an input image and various parameters, and outputs an enhanced version of the image. The inputs include the input image, a prompt, a negative prompt, guidance scale, and several other hyperparameters that control the enhancement process.
Inputs
- image: The input image to be enhanced
- prompt: The text prompt to guide the enhancement process
- negative_prompt: The negative prompt to exclude certain undesirable elements
- guidance_scale: The scale for classifier-free guidance
- num_inference_steps: The number of denoising steps to perform
- stride: The stride of moving local patches
- sigma: The standard deviation of the Gaussian filter
- cosine_scale_1, cosine_scale_2, cosine_scale_3: Controls the strength of various enhancement techniques
- multi_decoder: Whether to use multiple decoders
- view_batch_size: The batch size for multiple denoising paths
- seed: The random seed to use (leave blank to randomize)
Outputs
- Output: The enhanced version of the input image
Capabilities
The demofusion-enhance model can be used to improve the quality and resolution of input images. It can remove artifacts, sharpen details, and enhance the overall aesthetic of the image. The model is capable of handling a variety of input image types and can produce high-quality output images.
What can I use it for?
The demofusion-enhance model can be useful for a variety of applications, such as:
- Enhancing low-resolution or poor-quality images for use in design, photography, or other creative projects
- Improving the visual quality of images for use in web or mobile applications
- Upscaling and enhancing images for use in marketing or advertising materials
- Preparing images for printing or other high-quality output
Things to try
With the demofusion-enhance model, you can experiment with different input parameters to see how they affect the output. Try adjusting the guidance scale, the number of inference steps, or the various cosine scale parameters to see how they impact the level of enhancement. You can also try using different input images and prompts to see how the model handles different types of content.
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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