DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing
Authors: Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, Jian Zhang

Large-scale Text-to-Image (T2I) diffusion models have revolutionized image generation over the last few years. Although owning diverse and high-quality generation capabilities translating these abilities to fine-grained image editing remains challenging. In this paper we propose DiffEditor to rectify two weaknesses in existing diffusion-based image editing: (1) in complex scenarios editing results often lack editing accuracy and exhibit unexpected artifacts; (2) lack of flexibility to harmonize editing operations e.g. imagine new content. In our solution we introduce image prompts in fine-grained image editing cooperating with the text prompt to better describe the editing content. To increase the flexibility while maintaining content consistency we locally combine stochastic differential equation (SDE) into the ordinary differential equation (ODE) sampling. In addition we incorporate regional score-based gradient guidance and a time travel strategy into the diffusion sampling further improving the editing quality. Extensive experiments demonstrate that our method can efficiently achieve state-of-the-art performance on various fine-grained image editing tasks including editing within a single image (e.g. object moving resizing and content dragging) and across images (e.g. appearance replacing and object pasting). Our source code is released at https://github.com/MC-E/DragonDiffusion.