stable-diffusion-inpainting
Maintainer: runwayml
1.5K
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| Property | Value |
|---|---|
| Model Link | View 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
stable-diffusion-inpainting is a latent text-to-image diffusion model developed by runwayml that is capable of generating photo-realistic images based on text inputs, with the added capability of inpainting - filling in masked parts of images. Similar models include the stable-diffusion-2-inpainting model from Stability AI, which was resumed from the stable-diffusion-2-base model and trained for inpainting, and the stable-diffusion-xl-1.0-inpainting-0.1 model from the Diffusers team, which was trained for high-resolution inpainting.
Model inputs and outputs
stable-diffusion-inpainting takes in a text prompt, an image, and a mask image as inputs. The mask image indicates which parts of the original image should be inpainted. The model then generates a new image that combines the original image with the inpainted content based on the text prompt.
Inputs
- Prompt: A text description of the desired image
- Image: The original image to be inpainted
- Mask Image: A binary mask indicating which parts of the original image should be inpainted (white for inpainting, black for keeping)
Outputs
- Generated Image: The new image with the inpainted content
Capabilities
stable-diffusion-inpainting can be used to fill in missing or corrupted parts of images while maintaining the overall composition and style. For example, you could use it to add a new object to a scene, replace a person in a photo, or fix damaged areas of an image. The model is able to generate highly realistic and cohesive results, leveraging the power of the Stable Diffusion text-to-image generation capabilities.
What can I use it for?
stable-diffusion-inpainting could be useful for a variety of creative and practical applications, such as:
- Restoring old or damaged photos
- Removing unwanted elements from images
- Compositing different visual elements together
- Experimenting with different variations of a scene or composition
- Generating concept art or illustrations for games, films, or other media
The model's ability to maintain the overall aesthetic and coherence of an image while manipulating specific elements makes it a powerful tool for visual creativity and production.
Things to try
One interesting aspect of stable-diffusion-inpainting is its ability to preserve the non-masked parts of the original image while seamlessly blending in the new content. This can be used to create surreal or fantastical compositions, such as adding a tiger to a park bench or a spaceship to a landscape. By carefully selecting the mask regions and prompt, you can explore the boundaries of what the model can achieve in terms of image manipulation and generation.
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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