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Training-Free Layout Control with Cross-Attention Guidance (2304.03373v2)

Published 6 Apr 2023 in cs.CV

Abstract: Recent diffusion-based generators can produce high-quality images from textual prompts. However, they often disregard textual instructions that specify the spatial layout of the composition. We propose a simple approach that achieves robust layout control without the need for training or fine-tuning of the image generator. Our technique manipulates the cross-attention layers that the model uses to interface textual and visual information and steers the generation in the desired direction given, e.g., a user-specified layout. To determine how to best guide attention, we study the role of attention maps and explore two alternative strategies, forward and backward guidance. We thoroughly evaluate our approach on three benchmarks and provide several qualitative examples and a comparative analysis of the two strategies that demonstrate the superiority of backward guidance compared to forward guidance, as well as prior work. We further demonstrate the versatility of layout guidance by extending it to applications such as editing the layout and context of real images.

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References (61)
  1. Spatext: Spatio-textual representation for controllable image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18370–18380, 2023.
  2. ediffi: Text-to-image diffusion models with an ensemble of expert denoisers. arXiv preprint arXiv:2211.01324, 2022.
  3. Universal guidance for diffusion models. arXiv preprint arXiv:2302.07121, 2023.
  4. Multidiffusion: Fusing diffusion paths for controlled image generation. arXiv preprint arXiv:2302.08113, 2023.
  5. Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models. ACM Transactions on Graphics (TOG), 42(4):1–10, 2023.
  6. Zero-shot spatial layout conditioning for text-to-image diffusion models. arXiv preprint arXiv:2306.13754, 2023.
  7. Diffedit: Diffusion-based semantic image editing with mask guidance. arXiv preprint arXiv:2210.11427, 2022.
  8. Dall· e mini, 2021.
  9. Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems, 34:8780–8794, 2021.
  10. Cogview: Mastering text-to-image generation via transformers. Advances in Neural Information Processing Systems, 34:19822–19835, 2021.
  11. Cogview2: Faster and better text-to-image generation via hierarchical transformers. Advances in Neural Information Processing Systems, pages 16890–16902, 2022.
  12. Frido: Feature pyramid diffusion for complex scene image synthesis. arXiv preprint arXiv:2208.13753, 2022.
  13. Training-free structured diffusion guidance for compositional text-to-image synthesis. In The Eleventh International Conference on Learning Representations, 2023.
  14. Make-a-scene: Scene-based text-to-image generation with human priors. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XV, pages 89–106. Springer, 2022.
  15. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618, 2022.
  16. Benchmarking spatial relationships in text-to-image generation. arXiv preprint arXiv:2212.10015, 2022.
  17. Generative adversarial nets. In Proceedings of Advances in Neural Information Processing Systems (NeurIPS), 2014.
  18. Vector quantized diffusion model for text-to-image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10696–10706, 2022.
  19. Prompt-to-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626, 2022.
  20. Generating multiple objects at spatially distinct locations. arXiv preprint arXiv:1901.00686, 2019.
  21. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022.
  22. Inferring semantic layout for hierarchical text-to-image synthesis. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7986–7994, 2018.
  23. Composer: Creative and controllable image synthesis with composable conditions. arXiv preprint arXiv:2302.09778, 2023.
  24. Multimodal conditional image synthesis with product-of-experts gans. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XVI, pages 91–109. Springer, 2022.
  25. Image generation from scene graphs. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1219–1228, 2018.
  26. Imagic: Text-based real image editing with diffusion models. arXiv preprint arXiv:2210.09276, 2022.
  27. Gligen: Open-set grounded text-to-image generation. arXiv preprint arXiv:2301.07093, 2023.
  28. Microsoft COCO: common objects in context. In Proceedings of the European Conference on Computer Vision (ECCV), 2014.
  29. Microsoft coco: Common objects in context. In Proceedings of the European Conference on Computer Vision (ECCV), 2014.
  30. Compositional visual generation with composable diffusion models. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XVII, pages 423–439. Springer, 2022.
  31. Simple open-vocabulary object detection with vision transformers. arXiv preprint arXiv:2205.06230, 2022.
  32. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741, 2021.
  33. Semantic image synthesis with spatially-adaptive normalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2337–2346, 2019.
  34. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. International Journal of Computer Vision (IJCV), 2017.
  35. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748–8763. PMLR, 2021.
  36. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, page 3, 2022.
  37. Zero-shot text-to-image generation. In International Conference on Machine Learning, pages 8821–8831. PMLR, 2021.
  38. Generative adversarial text to image synthesis. In International conference on machine learning, pages 1060–1069. PMLR, 2016.
  39. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 10684–10695, 2022.
  40. 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.
  41. U-net: Convolutional networks for biomedical image segmentation. In MICCAI, 2015.
  42. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. arXiv preprint arXiv:2208.12242, 2022.
  43. Photorealistic text-to-image diffusion models with deep language understanding. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022.
  44. Laion-5b: An open large-scale dataset for training next generation image-text models. arXiv preprint arXiv:2210.08402, 2022.
  45. High-fidelity guided image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5997–6006, 2023.
  46. Image synthesis from reconfigurable layout and style. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10531–10540, 2019.
  47. Object-centric image generation from layouts. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 2647–2655, 2021.
  48. Df-gan: A simple and effective baseline for text-to-image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16515–16525, 2022.
  49. Unitune: Text-driven image editing by fine tuning an image generation model on a single image. arXiv preprint arXiv:2210.09477, 2022.
  50. Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion. arXiv preprint arXiv:2307.10816, 2023.
  51. Attngan: Fine-grained text to image generation with attentional generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1316–1324, 2018.
  52. Modeling image composition for complex scene generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7764–7773, 2022.
  53. Reco: Region-controlled text-to-image generation. arXiv preprint arXiv:2211.15518, 2022.
  54. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. TACL, 2014.
  55. Scaling autoregressive models for content-rich text-to-image generation. arXiv preprint arXiv:2206.10789, 2022.
  56. Cross-modal contrastive learning for text-to-image generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 833–842, 2021.
  57. Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 5907–5915, 2017.
  58. Stackgan++: Realistic image synthesis with stacked generative adversarial networks. IEEE transactions on pattern analysis and machine intelligence, 41(8):1947–1962, 2018.
  59. Adding conditional control to text-to-image diffusion models. arXiv preprint arXiv:2302.05543, 2023.
  60. Image generation from layout. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8584–8593, 2019.
  61. Detecting twenty-thousand classes using image-level supervision. In Proceedings of the European Conference on Computer Vision (ECCV), 2022.
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Authors (3)
  1. Minghao Chen (37 papers)
  2. Iro Laina (41 papers)
  3. Andrea Vedaldi (195 papers)
Citations (172)
View on Semantic Scholar

Summary

Insights into Training-Free Layout Control with Cross-Attention Guidance

The paper "Training-Free Layout Control with Cross-Attention Guidance" presents a novel approach to improving the spatial layout fidelity of images generated by diffusion models such as Stable Diffusion. The authors, Minghao Chen, Iro Laina, and Andrea Vedaldi from the Visual Geometry Group at the University of Oxford, propose a method that effectively guides the layout of generated images without requiring additional training or fine-tuning of existing image generators. The core innovation lies in manipulating the cross-attention mechanism within these models to achieve precise layout control.

Key Contributions

  1. Cross-Attention Manipulation: The paper introduces a framework that leverages cross-attention layers to manage the spatial relationships specified in user prompts. By modifying these layers' attention maps, this approach successfully aligns generated images with specified layouts.
  2. Forward and Backward Guidance Strategies: The authors explore two distinct strategies for manipulating cross-attention:
  • Forward Guidance: This directly biases the cross-attention layers, recalibrating their activations based on user-provided layouts. While computationally efficient, its effectiveness is often hindered by inherent model biases and dependencies between different language tokens.
  • Backward Guidance: Employs backpropagation to iteratively adjust image latents, ensuring that the generated layouts conform to user specifications through an energy-based minimization framework. This method proves superior, offering greater control and fidelity in the generated outputs by refining the images in alignment with desired layouts.
  1. Empirical Evaluation: Through comprehensive experiments on multiple benchmarks, including VISOR, COCO 2014, and Flickr30K, the paper demonstrates that backward guidance particularly excels in adhering to the specified spatial configurations, outperforming other methods in maintaining image quality and precision of layout.

Strong Numerical Results

  • On the VISOR benchmark, backward guidance achieves a 95.95% success rate with conditional spatial relationships, a substantial improvement over the baseline Stable Diffusion model.
  • Evaluation on COCO 2014 and Flickr30K datasets shows backward guidance achieving significant increases in mean Average Precision (mAP) for layout fidelity, highlighting the effectiveness of this method over existing state-of-the-art techniques.

Theoretical and Practical Implications

The implications of this research are multifaceted. From a theoretical standpoint, it underscores the potential of cross-attention as a pivot for enhancing generative model capabilities without necessitating training from scratch. Practically, this work can broaden the applications of diffusion models in fields like graphic design and virtual reality, which demand precise image compositions.

The backward guidance approach also introduces a more nuanced understanding of spatial information inherently captured by diffusion processes, offering pathways for future research to optimize initial noise selection, a factor shown to influence the quality and accuracy of generated images significantly.

Speculations on Future Developments in AI

The methodology proposed presents a pivotal shift in improving AI's ability to interpret and faithfully reproduce complex image layouts from text descriptions. It opens avenues for developing generative models that cater to highly specialized image generation tasks without additional costly training cycles. Future developments might involve integrating these layout control techniques into broader AI systems, streamlining workflows in creative industries and beyond.

In conclusion, this paper significantly advances our understanding of how to manipulate deep learning models' latent spaces to achieve specific task objectives, setting the stage for more intelligent and adaptable AI systems capable of understanding and fulfilling nuanced user demands in image generation.

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