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DiT4Edit: Diffusion Transformer for Image Editing
Kunyu Feng, Yue Ma, Bingyuan Wang, Chenyang Qi, Haozhe Chen, Qifeng Chen, Zeyu Wang
TL;DR
Image editing research faces challenges in preserving source-target consistency, while existing approaches largely use UNet structures and DDIM inversion. DiT4Edit combines a diffusion transformer with DPM-Solver inversion, unified attention control, and patch merging; the paper reports stronger editing performance than UNet-based methods across multiple image sizes.
Problem
Image editing faces source-target consistency challenges, costly fine-tuning and many-step DDIM inversion, while DiT's use for editing remains under-explored.
Source: §1 · p. 1Method
Results
DiT4Edit reports superior editing results with fewer inference steps than traditional UNet-based methods, including object, style, and shape-aware editing across varied image sizes.
Source: §1 · pp. 1–2Takeaways & Limitations
Abstract
from arXiv · showhide
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.
1 Introduction
DiT4Edit introduces a tuning-free Diffusion Transformer framework for image editing, addressing consistency and efficiency challenges in existing approaches. It combines DPM-Solver inversion, unified attention control, and patch merging, with reported advantages over UNet-based methods across editing tasks and image sizes.
1 Introduction
- Existing editing methods face source-target consistency challenges; fine-tuning can be costly, and DDIM inversion relies on invertibility and often uses many steps.The introduction gives 50 steps as an example and notes that editing consistency depends heavily on DDIM invertibility.Source: §1 · p. 1
- UNet-based editing is bounded by UNet's generative capacity, while DiT's global patch attention captures broader, more detailed features than UNet convolution blocks.The passage also notes evidence that transformer-based diffusion models scale better and outperform UNet-based models in large-scale experiments.Source: §1 · p. 1
- DPM-Solver inversion reduces the number of inversion steps, unified attention control supports text-guided editing while preserving background details, and patch merging accelerates transformer computation.The paper presents these components as a way to reduce inference time while maintaining image quality.Source: §1 · p. 1
- DiT4Edit is the first tuning-free DiT image-editing framework, with reported superior performance in object, style, and shape-aware editing across multiple image sizes.The stated sizes include 512 × 512, 1024 × 1024, and 1024 × 2048.Source: §1 · p. 2
2 Related Work
Related work traces progress in text-to-image generation from GANs and diffusion models to controllable and higher-resolution systems. It also reviews diffusion-based image editing while noting that DiT's use for editing remains under-explored.
2.1 Text-to-Image Generation
- Text-to-image research moved from GAN synthesis to DDPMs, then developed methods for guidance, conditional generation, and higher-resolution image synthesis.Examples include classifier guidance (Dhariwal and Nichol 2021), Control-Net (Zhang, Rao, and Agrawala 2023), and ScaleCrafter (He et al. 2023).Source: §2.1 · p. 2
- Text-to-image models also contributed to the development of video generation and editing applications (Ma et al. 2022a, 2023, 2022b, 2024c; Chen et al. 2024).Source: §2.1 · p. 3
2.2 Interactive Image Editing
- Diffusion editing spans prompt-based attention control and user-guided methods, but existing approaches remain bounded by pretrained UNet generative power while DiT editing is under-explored.The passage cites prompt-based cross-attention editing (Hertz et al. 2022), InstructPix2Pix (Brooks, Holynski, and Efros 2023), and structure preservation with pix2pix-zero (Parmar et al. 2023).Source: §2.2 · p. 3
3 Methodology
DiT4Edit adapts a pretrained diffusion transformer for image editing across image sizes. Its methodology combines DPM-Solver++ inversion, attention control, and patch merging.
3.2 Diffusion Model Architecture
- DiT4Edit uses a pretrained transformer diffusion model for editing across image sizes, building on PixArt-α and the transformer’s global attention.The method is presented as a way to address editing tasks at large and arbitrary sizes.Source: §3 · p. 3, §3.2 · pp. 3–4
3.1 Preliminaries: Latent Diffusion Models
- Latent diffusion encodes images into low-resolution latents, which are noised and then denoised into image samples under text-prompt conditioning.The denoising model is trained to remove artificial noise from the latent sample.Source: §3.1 · p. 3
3.3 Diffusion Transformer-based Image Editing
- DPM-Solver++ inversion obtains editing latents without DDIM-derived y values; its 2M setting supports rapid inference, while inversion approximates earlier-step terms.The paper describes 2M as a second-order multistep solver.Source: §3.3 · pp. 4–5
- Cross-attention carries prompt semantics, while self-attention guides layout; DiT4Edit switches source and target queries at threshold S during editing.The paper observes that deeper-layer queries represent semantic layout and object details more clearly than shallow-layer queries.Source: §3.3 · p. 5
- Patch merging reduces attention inputs by merging the most similar patches, then unmerges them to preserve the original input size for the next layer.The method is intended to improve inference efficiency without changing each layer’s fundamental operations.Source: Figure 4 · p. 5, §3.3 · p. 5
4 Experiments
DiT4Edit is evaluated across image sizes and editing scenarios, with comparisons showing advantages over UNet-based methods and ablations examining patches merging and DPM-Solver inversion.
4.1 Implementation Details
- Experiments use PixArt-α-XL models for images from 512 × 512 through 1024 × 2048, with DPM-Solver inversion configured for 30 steps and patches merging ratio 0.8.The experiments include real and generated inputs and were run on an NVIDIA Tesla A100 GPU.Source: §4.1 · pp. 5–6
4.3 Quantitative Comparison
- DiT4Edit outperforms six prior methods in image generation quality, background preservation, and text alignment, with shorter inference time comparable to InfEdit.The evaluation covers 512 × 512, 1024 × 1024, and 1024 × 2048 images.Source: Table 1 · p. 6, §4.3 · p. 7
4.2 Qualitative Comparison
- On real, large-scale, and arbitrarily sized images, DiT4Edit preserves source content and supports style and object-shape edits where UNet-based methods often damage backgrounds or restrict output to 512 × 512.The qualitative comparison includes 512 × 512 and 1024 × 1024 images.Source: §4.2 · pp. 6–7
4.4 Ablation Study
- Patches merging reduces editing time for large images while maintaining quality comparable to editing without merging.The ablation caption reports acceleration especially for large-sized image editing without affecting final image quality.Source: §4.4 · p. 7, Table 2 · p. 7
- At the same 30 inference steps, DPM-Solver inversion produces better editing quality than DDIM, while requiring fewer steps for superior outcomes.The authors attribute the improvement to better latent maps from DPM-Solver inversion.Source: §4.4 · p. 7, Figure 7 · p. 7
5 Discussion and Conclusion
The paper concludes that DiT4Edit offers high-quality editing across image sizes, including arbitrary sizes, while identifying tokenizer and color inconsistencies as limitations.
Conclusion
- DiT4Edit is presented as the first diffusion-transformer image-editing framework, outperforming UNet structures at 512 × 512 and 1024 × 1024 and handling arbitrary sizes such as 1024 × 2048.The conclusion describes this as a baseline for further DiT-based image-editing research.Source: §5 · p. 8
Limitations
- The T5 tokenizer occasionally segments words in ways that cause editing failures, and outputs may have color inconsistencies relative to the original image.Source: §5 · p. 8
Potential social impact
- The authors identify image-authenticity assessment and unauthorized privacy breaches as social risks, calling for clear standards and regulations.They state that future model development will prioritize these concerns.Source: §5 · p. 8