DiffEdit: Diffusion-based semantic image editing with mask guidance

Understand

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.