Despite recent advances in UNet-based image editing, methods for shape-aware object editing in high-resolution images are still lacking. Compared to UNet, Diffusion Transformers (DiT) demonstrate superior capabilities to effectively capture the long-range dependencies among patches, leading to higher-quality image generation. In this paper, we propose DiT4Edit, the first Diffusion Transformer-based image editing framework. Specifically, DiT4Edit uses the DPM-Solver inversion algorithm to obtain the inverted latents, reducing the number of steps compared to the DDIM inversion algorithm commonly used in UNet-based frameworks. Additionally, we design unified attention control and patches merging, tailored for transformer computation streams. This integration allows our framework to generate higher-quality edited images faster. Our design leverages the advantages of DiT, enabling it to surpass UNet structures in image editing, especially in high-resolution and arbitrary-size images. Extensive experiments demonstrate the strong performance of DiT4Edit across various editing scenarios, highlighting the potential of Diffusion Transformers in supporting image editing.

本研究解决了高分辨率图像中形状感知对象编辑方法的不足。提出的DiT4Edit是首个基于扩散变压器的图像编辑框架，通过使用DPM-Solver反演算法减少步骤，并设计了统一的注意力控制和补丁合并，显著提高了图像编辑的质量和速度。大量实验证明了DiT4Edit在各种编辑场景中的强大性能，展示了扩散变压器在图像编辑中的潜力。

DiT4Edit：用于图像编辑的扩散变压器