Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
41 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing (2306.14435v6)

Published 26 Jun 2023 in cs.CV and cs.LG

Abstract: Accurate and controllable image editing is a challenging task that has attracted significant attention recently. Notably, DragGAN is an interactive point-based image editing framework that achieves impressive editing results with pixel-level precision. However, due to its reliance on generative adversarial networks (GANs), its generality is limited by the capacity of pretrained GAN models. In this work, we extend this editing framework to diffusion models and propose a novel approach DragDiffusion. By harnessing large-scale pretrained diffusion models, we greatly enhance the applicability of interactive point-based editing on both real and diffusion-generated images. Our approach involves optimizing the diffusion latents to achieve precise spatial control. The supervision signal of this optimization process is from the diffusion model's UNet features, which are known to contain rich semantic and geometric information. Moreover, we introduce two additional techniques, namely LoRA fine-tuning and latent-MasaCtrl, to further preserve the identity of the original image. Lastly, we present a challenging benchmark dataset called DragBench -- the first benchmark to evaluate the performance of interactive point-based image editing methods. Experiments across a wide range of challenging cases (e.g., images with multiple objects, diverse object categories, various styles, etc.) demonstrate the versatility and generality of DragDiffusion. Code: https://github.com/Yujun-Shi/DragDiffusion.

PDF HTML Abstract

An Expert Analysis of "DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing"

The paper, "DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing," introduces a novel approach to image editing by extending the DragGAN framework to large-scale pretrained diffusion models. The authors propose a method called DragDiffusion, which achieves accurate and controllable image edits through interactive point-based mechanisms, significantly enhancing the versatility and generality over previous GAN-based methods like DragGAN.

Summary of Contributions

The key contributions of this paper are threefold:

  1. Introduction of DragDiffusion: The method leverages pretrained diffusion models to improve interactive point-based editing. It achieves this through efficient spatial control by optimizing the latent of a single diffusion step, rather than multiple steps, as commonly done in prior diffusion-based methods.
  2. Identity-preserving Fine-tuning and Reference-latent-control: To maintain the identity and quality of the original image during the editing process, the authors introduce novel techniques such as identity-preserving fine-tuning and reference-latent-control.
  3. Development of DragBench: The paper presents a new benchmark dataset for evaluating interactive point-based editing methods, facilitating standardized assessment and comparison of different techniques.

Detailed Methodology

The methodology section of the paper provides a rigorous breakdown of the DragDiffusion approach, emphasizing the following stages:

  1. Preliminaries on Diffusion Models: The authors give an overview of denoising diffusion probabilistic models (DDPM) and latent diffusion models (LDM). They explain that these models learn to generate images by progressively denoising a starting random noise vector through a series of steps defined by Markov chains.
  2. Identity-preserving Fine-tuning: Implemented using Low Rank Adaptation (LoRA), this fine-tuning step helps the diffusion model encode original image features more accurately, which is crucial for maintaining the identity of the original image during the editing process.
  3. Diffusion Latent Optimization: This step involves optimizing the diffusion latent according to user-provided dragging instructions. The process includes motion supervision and point tracking to iteratively adjust the handle points towards their target locations, efficiently adapting the diffusion latent.
  4. Reference-latent-control: To mitigate potential identity shifts and quality degradation during the denoising process, the authors propose using reference-latent-control. This technique uses self-attention modules during denoising to ensure the edited image remains coherent with the original.

Experimental Results

The authors validate DragDiffusion through extensive qualitative and quantitative experiments. They compare their approach with DragGAN, demonstrating superior performance in different domains, including real images and images generated by various versions of Stable Diffusion models. Notably, they present the following findings:

  • DragDiffusion outperforms DragGAN in terms of Mean Distance (MD) and Image Fidelity (IF), showing lower MD values and higher IF scores across diverse categories.
  • The provided DragBench dataset helps evaluate different aspects of interactive point-based editing methods, supporting the comprehensive assessment of DragDiffusion.

Implications and Future Directions

The implications of this research are multifaceted. Practically, it paves the way for more versatile and accurate image editing tools, enabling users to make precise modifications with minimal artifacts. Theoretically, it demonstrates the potential of diffusion models in interactive editing tasks, highlighting their flexibility and generalization capabilities.

Future research directions could explore improvements in the robustness and reliability of drag-based editing with diffusion models. Additionally, further works could extend DragDiffusion to other types of generative models beyond diffusion and GANs, exploring new horizons in interactive image editing.

Conclusion

This paper presents a solid advancement in the field of interactive image editing by harnessing diffusion models. Through the introduction of DragDiffusion and the DragBench dataset, the authors offer a robust framework for precise and controllable edits while maintaining image integrity. The combination of novel techniques such as identity-preserving fine-tuning and reference-latent-control underscores the innovation and thoughtfulness behind this approach. Moving forward, the community can build upon these findings to develop even more advanced and user-friendly image editing solutions.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (53)
  1. Image2stylegan: How to embed images into the stylegan latent space? In Proceedings of the IEEE/CVF international conference on computer vision, pages 4432–4441, 2019.
  2. Styleflow: Attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows. ACM Transactions on Graphics (ToG), 40(3):1–21, 2021.
  3. Universal guidance for diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 843–852, 2023.
  4. Text2live: Text-driven layered image and video editing. In European Conference on Computer Vision, pages 707–723. Springer, 2022.
  5. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18392–18402, 2023.
  6. Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and editing. arXiv preprint arXiv:2304.08465, 2023.
  7. Inverting the generator of a generative adversarial network. IEEE transactions on neural networks and learning systems, 30(7):1967–1974, 2018.
  8. Yuki Endo. User-controllable latent transformer for stylegan image layout editing. arXiv preprint arXiv:2208.12408, 2022.
  9. Diffusion self-guidance for controllable image generation. arXiv preprint arXiv:2306.00986, 2023.
  10. Tokenflow: Consistent diffusion features for consistent video editing. arXiv preprint arxiv:2307.10373, 2023.
  11. Generative adversarial nets. In Advances in Neural Information Processing Systems. Curran Associates, Inc., 2014.
  12. Mix-of-show: Decentralized low-rank adaptation for multi-concept customization of diffusion models. arXiv preprint arXiv:2305.18292, 2023.
  13. Ganspace: Discovering interpretable gan controls. Advances in neural information processing systems, 33:9841–9850, 2020.
  14. Prompt-to-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626, 2022.
  15. Classifier-free diffusion guidance. In NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications, 2021.
  16. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840–6851, 2020.
  17. LoRA: Low-rank adaptation of large language models. In International Conference on Learning Representations (ICLR), 2022.
  18. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401–4410, 2019.
  19. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110–8119, 2020.
  20. Imagic: Text-based real image editing with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6007–6017, 2023.
  21. Adam: A method for stochastic optimization. In Proceedings of the International Conference on Learning Representations (ICLR), 2015.
  22. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
  23. Freestylegan: Free-view editable portrait rendering with the camera manifold. arXiv preprint arXiv:2109.09378, 2021.
  24. Magicmix: Semantic mixing with diffusion models. arXiv preprint arXiv:2210.16056, 2022.
  25. Freedrag: Point tracking is not you need for interactive point-based image editing. arXiv preprint arXiv:2307.04684, 2023.
  26. Precise recovery of latent vectors from generative adversarial networks. arXiv preprint arXiv:1702.04782, 2017.
  27. Guided image synthesis via initial image editing in diffusion model. arXiv preprint arXiv:2305.03382, 2023.
  28. Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073, 2021.
  29. Null-text inversion for editing real images using guided diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6038–6047, 2023.
  30. Dragondiffusion: Enabling drag-style manipulation on diffusion models. arXiv preprint arXiv:2307.02421, 2023.
  31. Drag your GAN: Interactive point-based manipulation on the generative image manifold. arXiv preprint arXiv:2305.10973, 2023.
  32. Zero-shot image-to-image translation. arXiv preprint arXiv:2302.03027, 2023.
  33. Styleclip: Text-driven manipulation of stylegan imagery. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2085–2094, 2021.
  34. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR, 2021.
  35. Pivotal tuning for latent-based editing of real images. ACM Transactions on Graphics (TOG), 42(1):1–13, 2022.
  36. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684–10695, 2022.
  37. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pages 234–241. Springer, 2015.
  38. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22500–22510, 2023.
  39. Simo Ryu. Low-rank adaptation for fast text-to-image diffusion fine-tuning. https://github.com/cloneofsimo/lora, 2022.
  40. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35:36479–36494, 2022.
  41. Closed-form factorization of latent semantics in gans. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1532–1540, 2021.
  42. Interpreting the latent space of gans for semantic face editing. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9243–9252, 2020.
  43. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256–2265. PMLR, 2015.
  44. Denoising diffusion implicit models. In Proceedings of the International Conference on Learning Representations (ICLR), 2021.
  45. Emergent correspondence from image diffusion. arXiv preprint arXiv:2306.03881, 2023.
  46. Stylerig: Rigging stylegan for 3d control over portrait images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6142–6151, 2020.
  47. Plug-and-play diffusion features for text-driven image-to-image translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1921–1930, 2023.
  48. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  49. Rewriting geometric rules of a gan. ACM Transactions on Graphics (TOG), 41(4):1–16, 2022.
  50. Freedom: Training-free energy-guided conditional diffusion model. arXiv preprint arXiv:2303.09833, 2023.
  51. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586–595, 2018.
  52. Linkgan: Linking gan latents to pixels for controllable image synthesis. arXiv preprint arXiv:2301.04604, 2023.
  53. Generative visual manipulation on the natural image manifold. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part V 14, pages 597–613. Springer, 2016.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (8)
  1. Yujun Shi (23 papers)
  2. Chuhui Xue (19 papers)
  3. Jun Hao Liew (29 papers)
  4. Jiachun Pan (16 papers)
  5. Hanshu Yan (28 papers)
  6. Wenqing Zhang (60 papers)
  7. Vincent Y. F. Tan (205 papers)
  8. Song Bai (87 papers)
Citations (131)
View on Semantic Scholar
Github Logo Streamline Icon: https://streamlinehq.com

GitHub

  1. GitHub - Yujun-Shi/DragDiffusion: [CVPR2024, Highlight] Official code for DragDiffusion (1,056 stars)
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

Youtube Logo Streamline Icon: https://streamlinehq.com

YouTube