ri
Maintainer: simbrams
146
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The ri model, created by maintainer simbrams, is a Realistic Inpainting model with ControlNET (M-LSD + SEG). It allows for realistic image inpainting, with the ability to control the inpainting process using a segmentation map. This model can be compared to similar models like controlnet-inpaint-test, sks, controlnet-scribble, and controlnet-seg, which also leverage ControlNET for various image manipulation tasks.
Model inputs and outputs
The ri model takes in an input image, a mask image, and various parameters to control the inpainting process, such as the number of inference steps, the guidance scale, and the image size. The model then generates an output image with the specified inpainted regions.
Inputs
- Image: The input image to be inpainted.
- Mask: The mask image indicating the regions to be inpainted.
- Prompt: A text prompt describing the desired inpainting result.
- Negative prompt: A text prompt describing undesired content to be avoided in the inpainting.
- Strength: The strength or weight of the inpainting process.
- Image size: The desired size of the output image.
- Guidance scale: The scale of the text guidance during the inpainting process.
- Scheduler: The type of scheduler to use for the diffusion process.
- Seed: A seed value for the random number generator, allowing for reproducible results.
- Debug: A flag to enable debug mode for the model.
- Blur mask: A flag to blur the mask before inpainting.
- Blur radius: The radius of the blur applied to the mask.
- Preserve elements: A flag to preserve elements during the inpainting process.
Outputs
- Output images: The inpainted output images.
Capabilities
The ri model is capable of realistic inpainting, allowing users to remove or modify specific regions of an image while preserving the overall coherence and realism of the result. By leveraging ControlNET and segmentation, the model can be directed to focus on specific elements or areas of the image during the inpainting process.
What can I use it for?
The ri model can be useful for a variety of applications, such as photo editing, content creation, and digital art. Users can use it to remove unwanted objects, repair damaged images, or even create entirely new scenes by inpainting selected regions. The model's ability to preserve elements and control the inpainting process makes it a powerful tool for creative and professional use cases.
Things to try
With the ri model, users can experiment with different input prompts, mask shapes, and parameter settings to achieve a wide range of inpainting results. For example, you could try inpainting a person in a landscape, removing distracting elements from a photo, or even creating entirely new scenes by combining multiple inpainting steps. The model's flexibility allows for a high degree of creative exploration and customization.
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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