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ReNoise: Real Image Inversion Through Iterative Noising (2403.14602v1)

Published 21 Mar 2024 in cs.CV, cs.GR, cs.LG, and eess.IV

Abstract: Recent advancements in text-guided diffusion models have unlocked powerful image manipulation capabilities. However, applying these methods to real images necessitates the inversion of the images into the domain of the pretrained diffusion model. Achieving faithful inversion remains a challenge, particularly for more recent models trained to generate images with a small number of denoising steps. In this work, we introduce an inversion method with a high quality-to-operation ratio, enhancing reconstruction accuracy without increasing the number of operations. Building on reversing the diffusion sampling process, our method employs an iterative renoising mechanism at each inversion sampling step. This mechanism refines the approximation of a predicted point along the forward diffusion trajectory, by iteratively applying the pretrained diffusion model, and averaging these predictions. We evaluate the performance of our ReNoise technique using various sampling algorithms and models, including recent accelerated diffusion models. Through comprehensive evaluations and comparisons, we show its effectiveness in terms of both accuracy and speed. Furthermore, we confirm that our method preserves editability by demonstrating text-driven image editing on real images.

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References (55)
  1. John Wiley and Sons, Ltd, 2003.
  2. Image2stylegan: How to embed images into the stylegan latent space? In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019.
  3. Image2stylegan++: How to edit the embedded images? In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2020.
  4. Restyle: A residual-based stylegan encoder via iterative refinement. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2021a.
  5. Hyperstyle: Stylegan inversion with hypernetworks for real image editing, 2021b.
  6. Cross-image attention for zero-shot appearance transfer, 2023.
  7. Blended latent diffusion. arXiv preprint arXiv:2206.02779, 2022a.
  8. Blended diffusion for text-driven editing of natural images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18208–18218, 2022b.
  9. Instructpix2pix: Learning to follow image editing instructions, 2023.
  10. Numerical Analysis. Cengage Learning, 2015.
  11. Diffedit: Diffusion-based semantic image editing with mask guidance. ArXiv, abs/2210.11427, 2022.
  12. Diffusion models beat gans on image synthesis, 2021.
  13. Hyperinverter: Improving stylegan inversion via hypernetwork. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
  14. Diffusion self-guidance for controllable image generation. 2023.
  15. Expressive text-to-image generation with rich text. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023.
  16. Improving tuning-free real image editing with proximal guidance, 2023.
  17. Prompt-to-prompt image editing with cross attention control, 2022.
  18. Style aligned image generation via shared attention. 2023.
  19. Classifier-free diffusion guidance, 2022.
  20. Denoising diffusion probabilistic models, 2020.
  21. An edit friendly ddpm noise space: Inversion and manipulations, 2023.
  22. Elucidating the design space of diffusion-based generative models. Advances in Neural Information Processing Systems, 35:26565–26577, 2022.
  23. Imagic: Text-based real image editing with diffusion models. In Conference on Computer Vision and Pattern Recognition 2023, 2023.
  24. Understanding ddpm latent codes through optimal transport. In The Eleventh International Conference on Learning Representations, 2022.
  25. Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. arXiv preprint arXiv:2301.12597, 2023.
  26. Microsoft COCO: common objects in context. CoRR, abs/1405.0312, 2014.
  27. Latent consistency models: Synthesizing high-resolution images with few-step inference, 2023a.
  28. Lcm-lora: A universal stable-diffusion acceleration module. arXiv preprint arXiv:2311.05556, 2023b.
  29. Fixed-point inversion for text-to-image diffusion models, 2023.
  30. Sdedit: Guided image synthesis and editing with stochastic differential equations, 2022.
  31. Negative-prompt inversion: Fast image inversion for editing with text-guided diffusion models, 2023.
  32. Null-text inversion for editing real images using guided diffusion models, 2022.
  33. Effective real image editing with accelerated iterative diffusion inversion. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 15912–15921, 2023.
  34. Spatially-adaptive multilayer selection for gan inversion and editing. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022.
  35. Zero-shot image-to-image translation. In Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Proceedings. ACM, 2023.
  36. Localizing object-level shape variations with text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023.
  37. Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023.
  38. Hierarchical text-conditional image generation with clip latents, 2022.
  39. Encoding in style: a stylegan encoder for image-to-image translation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
  40. High-resolution image synthesis with latent diffusion models, 2022.
  41. Photorealistic text-to-image diffusion models with deep language understanding, 2022.
  42. Progressive distillation for fast sampling of diffusion models. In International Conference on Learning Representations, 2021.
  43. Adversarial diffusion distillation, 2023.
  44. Denoising diffusion implicit models, 2022.
  45. Score-based generative modeling through stochastic differential equations. In International Conference on Learning Representations, 2020.
  46. Consistency models, 2023.
  47. Designing an encoder for stylegan image manipulation. ACM Trans. Graph., 40(4), 2021.
  48. Plug-and-play diffusion features for text-driven image-to-image translation. pages 1921–1930, 2023.
  49. Anylens: A generative diffusion model with any rendering lens. 2023.
  50. Edict: Exact diffusion inversion via coupled transformations. arXiv preprint arXiv:2211.12446, 2022.
  51. Chen Henry Wu and Fernando De la Torre. Unifying diffusion models’ latent space, with applications to cyclediffusion and guidance. arXiv preprint arXiv:2210.05559, 2022.
  52. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018.
  53. In-domain gan inversion for real image editing. In Proceedings of European Conference on Computer Vision (ECCV), 2020a.
  54. Generative visual manipulation on the natural image manifold. In Proceedings of European Conference on Computer Vision (ECCV), 2016.
  55. Improved stylegan embedding: Where are the good latents?, 2020b.
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Authors (5)
  1. Daniel Garibi (6 papers)
  2. Or Patashnik (32 papers)
  3. Andrey Voynov (15 papers)
  4. Hadar Averbuch-Elor (43 papers)
  5. Daniel Cohen-Or (172 papers)
Citations (31)
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Summary

Comprehensive Analysis of ReNoise: Real Image Inversion Through Iterative Noising

Introduction

The field of image synthesis and manipulation has been significantly advanced by the development of text-guided diffusion models. A critical challenge in applying these models for real image modification lies in the process of inverting real images into the latent domain of pretrained models. Particularly, inversion becomes more problematic for cutting-edge diffusion models designed for high-quality image generation with a reduced number of denoising steps. Introducing ReNoise, this paper presents an innovative inversion method that strikes a superior balance between reconstruction accuracy and the amount of operational overhead, termed as the quality-to-operation ratio, against the backdrop of reversing diffusion sampling processes.

Methodology

ReNoise capitalizes on an iterative renoising mechanism that refines the approximation of forward diffusion trajectories. This iterative process, integrated at each inversion sampling step, leverages the pretrained model to enhance the direction from ztz_t to zt+1z_{t+1}, ensuring more accurate reconstruction while enabling longer strides along the inversion trajectory. The methodology encompasses:

  • Iterative Renoising: An initial estimation for zt+1z_{t+1} is progressively refined by applying the pretrained diffusion model several times, each iteration aiming to tighten the approximation of the predicted point along the forward diffusion trajectory.
  • Averaging Predictions: After a designated number of renoising iterations, an averaging procedure is employed to synthesize a more precise direction from ztz_t to zt+1z_{t+1}, effectively improving the overall reconstruction accuracy.

Experimental Results and Implications

The ReNoise technique underwent rigorous testing using various models (including recent accelerated diffusion models) and sampling algorithms to demonstrate its efficacy in image reconstruction accuracy and speed:

  • Superior Reconstruction Quality: ReNoise consistently outperforms traditional inversion methods in terms of reconstruction accuracy, as verified across multiple models and samplers.
  • Enhanced Speed vs. Quality Trade-off: The technique proposes a favorable trade-off between the amount of computational operations (UNet operations) required and the quality of image reconstruction, particularly beneficial for models trained with a small number of denoising steps.
  • Preservation of Editability: Through text-driven image editing experiments on real images, ReNoise confirms its capability to preserve the editability of inverted images, enabling a broader spectrum of image manipulation applications.

Theoretical Insights

The paper explores the mechanisms underlying the iterative renoising process, presenting a theoretical foundation based on the backward Euler method and fixed-point iterations. The convergence of the iterative renoising procedure is empirically substantiated, illuminating the stability and efficacy of ReNoise in navigating the inversion landscape.

Future Directions and Limitations

While ReNoise marks a significant advancement in image inversion for diffusion models, it also opens avenues for further exploration. The method's adaptability to few-step diffusion models hints at potential applications in real-time image editing and manipulation workflows. Additionally, model-specific tuning required for edit enhancement and noise correction components signals a direction for automating hyperparameter optimization. Future work may also extend ReNoise's application to the inversion of video diffusion models, broadening the scope of generative model applications.

Conclusion

The introduction of ReNoise addresses a critical gap in the utilization of diffusion models for real image editing. By amalgamating iterative renoising with an averaging mechanism, it sets a new benchmark for image inversion in terms of both accuracy and efficiency. The method's broad applicability across various models and its contribution to preserving editability underline its potential to catalyze innovations in generative models for image synthesis and manipulation.

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