DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing (2402.02583v1)

Abstract: 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.

Introduction

The paper presents a novel model named DiffEditor, which addresses two primary challenges in diffusion-based image editing: enhancing editing accuracy in complex scenarios and improving the flexibility of edits without generating unexpected artifacts. The research targets various fine-grained image editing tasks, such as object moving, resizing, content dragging, and cross-image edits like appearance replacing and object pasting. The authors' approach introduces a mechanism of regional score-based gradient guidance, time travel strategy in diffusion sampling, and the use of image prompts, which provide more detail-oriented content descriptions for the edited images. This combination has demonstrated significant improvements in editing outcome quality.

Design of DiffEditor

DiffEditor integrates image prompts, which allow the model to understand fine-grained editing intentions, leading to a more controlled editing process. Additionally, the authors propose a hybrid sampling technique that merges both stochastic and ordinary differential equations to improve flexibility and maintain content consistency. The model also harnesses regional score-based gradient guidance and a time travel strategy during the diffusion sampling process, providing a mechanism to fine-tune the editing results and avoid incongruities, particularly in challenging generation tasks.

Experimental Results

Empirical evidence showcases the robustness of DiffEditor. The quantitative evaluation of the model demonstrated that it could outperform existing methods, notably in the keypoint-based face manipulation tasks where the accuracy was quantified by the mean squared error (MSE) between the edited result and the target landmarks. The model also showed improvements in image generation quality, evidenced by lower Fréchet Inception Distances (FID) scores compared to other diffusion-based methods. Importantly, in terms of time complexity, DiffEditor not only improved the flexibility of image editing but also reduced inference complexity relative to its diffusion-based counterparts.

Conclusion and Future Work

DiffEditor is positioned as a significant advancement in diffusion-based fine-grained image editing, tackling key issues that have hampered previous models. The paper effectively demonstrates the model's superior performance across various image editing tasks, substantiated by extensive experiments. However, the authors recognize that the model may encounter difficulties in highly imaginative scenarios due to the underlying base model's limitations. Future developmental directions include enhancing the model's capabilities to comprehend 3D object perception, which could further refine its editing potential.

In summary, DiffEditor is a substantial step forward in diffusion-based image editing, offering improvements in both accuracy and flexibility in image editing tasks while reducing time complexity. Its innovative use of image prompts, combined with the introduction of regional score-based gradient guidance and time travel strategy, sets a new standard for robust and reliable fine-grained image editing.