DiffEdit DiffEdit: Diffusion-based semantic image editing with mask guidance is by Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord. The abstract from the paper is: Image generation has recently seen tremendous advances, with diffusion models allowing to synthesize convincing images for a large variety of text prompts. In this article, we propose DiffEdit, a method to take advantage of text-conditioned diffusion models for the task of semantic image editing, where the goal is to edit an image based on a text query. Semantic image editing is an extension of image generation, with the additional constraint that the generated image should be as similar as possible to a given input image. Current editing methods based on diffusion models usually require to provide a mask, making the task much easier by treating it as a conditional inpainting task. In contrast, our main contribution is able to automatically generate a mask highlighting regions of the input image that need to be edited, by contrasting predictions of a diffusion model conditioned on different text prompts. Moreover, we rely on latent inference to preserve content in those regions of interest and show excellent synergies with mask-based diffusion. DiffEdit achieves state-of-the-art editing performance on ImageNet. In addition, we evaluate semantic image editing in more challenging settings, using images from the COCO dataset as well as text-based generated images. The original codebase can be found at Xiang-cd/DiffEdit-stable-diffusion, and you can try it out in this demo. This pipeline was contributed by clarencechen. ❤️  Tips The pipeline can generate masks that can be fed into other inpainting pipelines. In order to generate an image using this pipeline, both an image mask (source and target prompts can be manually specified or generated, and passed to generate_mask())