

Realfill
Overview :
RealFill is a generative image inpainting model that can fill in missing areas of images using a small number of reference images from the same scene, generating visually consistent content with the original scene. RealFill creates personalized generative models by fine-tuning a pre-trained image inpainting diffusion model on both reference and target images. The model not only retains good image priors but also learns the content, illumination, and style of the input image. Subsequently, the fine-tuned model is used to fill in the missing regions of the target image through a standard diffusion sampling process. RealFill has been evaluated on a new image inpainting benchmark containing diverse complex scenes, demonstrating significantly superior performance compared to existing methods.
Target Users :
Used for image inpainting, particularly effective for cases where there is a large disparity between the missing region and the reference image.
Use Cases
Inpainting the missing area to match the content of the reference image
Repairing missing parts in an image
Generating high-quality visual content
Features
Fill in missing areas of images using a small number of reference images
Generate visually consistent content with the original scene
Personalized generative models
Support reference images with different viewpoints, lighting conditions, camera aperture, and image styles
Standard diffusion sampling process
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