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RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models

Published 20 Feb 2024 in cs.CV, cs.AI, and cs.LG | (2402.12908v3)

Abstract: Diffusion models have achieved remarkable advancements in text-to-image generation. However, existing models still have many difficulties when faced with multiple-object compositional generation. In this paper, we propose RealCompo, a new training-free and transferred-friendly text-to-image generation framework, which aims to leverage the respective advantages of text-to-image models and spatial-aware image diffusion models (e.g., layout, keypoints and segmentation maps) to enhance both realism and compositionality of the generated images. An intuitive and novel balancer is proposed to dynamically balance the strengths of the two models in denoising process, allowing plug-and-play use of any model without extra training. Extensive experiments show that our RealCompo consistently outperforms state-of-the-art text-to-image models and spatial-aware image diffusion models in multiple-object compositional generation while keeping satisfactory realism and compositionality of the generated images. Notably, our RealCompo can be seamlessly extended with a wide range of spatial-aware image diffusion models and stylized diffusion models. Our code is available at: https://github.com/YangLing0818/RealCompo

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References (55)
  1. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023.
  2. ediffi: Text-to-image diffusion models with an ensemble of expert denoisers. arXiv preprint arXiv:2211.01324, 2022.
  3. Multidiffusion: Fusing diffusion paths for controlled image generation. arXiv preprint arXiv:2302.08113, 2023.
  4. Improving image generation with better captions. Computer Science. https://cdn. openai. com/papers/dall-e-3. pdf, 2:3, 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. Pixart-α𝛼\alphaitalic_α: Fast training of diffusion transformer for photorealistic text-to-image synthesis. arXiv preprint arXiv:2310.00426, 2023a.
  7. Training-free layout control with cross-attention guidance. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp.  5343–5353, 2024.
  8. Reason out your layout: Evoking the layout master from large language models for text-to-image synthesis. arXiv preprint arXiv:2311.17126, 2023b.
  9. Frido: Feature pyramid diffusion for complex scene image synthesis. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pp.  579–587, 2023.
  10. Training-free structured diffusion guidance for compositional text-to-image synthesis. In The Eleventh International Conference on Learning Representations, 2023.
  11. Matching pairs: Attributing fine-tuned models to their pre-trained large language models. arXiv preprint arXiv:2306.09308, 2023.
  12. Llm blueprint: Enabling text-to-image generation with complex and detailed prompts. arXiv preprint arXiv:2310.10640, 2023.
  13. Matryoshka diffusion models. arXiv preprint arXiv:2310.15111, 2023.
  14. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020.
  15. Instruct-imagen: Image generation with multi-modal instruction. arXiv preprint arXiv:2401.01952, 2024.
  16. T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation. arXiv preprint arXiv:2307.06350, 2023a.
  17. Collaborative diffusion for multi-modal face generation and editing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  6080–6090, 2023b.
  18. Lambada: Backward chaining for automated reasoning in natural language. arXiv preprint arXiv:2212.13894, 2022.
  19. Dense text-to-image generation with attention modulation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.  7701–7711, 2023.
  20. Unified demonstration retriever for in-context learning. arXiv preprint arXiv:2305.04320, 2023a.
  21. Gligen: Open-set grounded text-to-image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  22511–22521, 2023b.
  22. Llm-grounded diffusion: Enhancing prompt understanding of text-to-image diffusion models with large language models. arXiv preprint arXiv:2305.13655, 2023.
  23. Compositional visual generation with composable diffusion models. In European Conference on Computer Vision, pp.  423–439. Springer, 2022.
  24. Conform: Contrast is all you need for high-fidelity text-to-image diffusion models. arXiv preprint arXiv:2312.06059, 2023.
  25. Rethinking the role of demonstrations: What makes in-context learning work? arXiv preprint arXiv:2202.12837, 2022.
  26. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. In International Conference on Machine Learning, pp.  16784–16804. PMLR, 2022.
  27. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023.
  28. Layoutllm-t2i: Eliciting layout guidance from llm for text-to-image generation. In Proceedings of the 31st ACM International Conference on Multimedia, pp.  643–654, 2023.
  29. Learning transferable visual models from natural language supervision. In International conference on machine learning, pp.  8748–8763. PMLR, 2021.
  30. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 1(2):3, 2022.
  31. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.  10684–10695, 2022.
  32. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35:36479–36494, 2022.
  33. Measuring inductive biases of in-context learning with underspecified demonstrations. arXiv preprint arXiv:2305.13299, 2023.
  34. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning, pp.  2256–2265. PMLR, 2015.
  35. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020a.
  36. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020b.
  37. Dreamsync: Aligning text-to-image generation with image understanding feedback. arXiv preprint arXiv:2311.17946, 2023.
  38. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023.
  39. Compositional text-to-image synthesis with attention map control of diffusion models. arXiv preprint arXiv:2305.13921, 2023a.
  40. Tokencompose: Grounding diffusion with token-level supervision. arXiv preprint arXiv:2312.03626, 2023b.
  41. Speechgen: Unlocking the generative power of speech language models with prompts. arXiv preprint arXiv:2306.02207, 2023a.
  42. Autogen: Enabling next-gen llm applications via multi-agent conversation framework. arXiv preprint arXiv:2308.08155, 2023b.
  43. Harnessing the spatial-temporal attention of diffusion models for high-fidelity text-to-image synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.  7766–7776, 2023c.
  44. Self-correcting llm-controlled diffusion models. arXiv preprint arXiv:2311.16090, 2023d.
  45. Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.  7452–7461, 2023.
  46. Ufogen: You forward once large scale text-to-image generation via diffusion gans. arXiv preprint arXiv:2311.09257, 2023.
  47. Raphael: Text-to-image generation via large mixture of diffusion paths. arXiv preprint arXiv:2305.18295, 2023.
  48. Diffusion-based scene graph to image generation with masked contrastive pre-training. arXiv preprint arXiv:2211.11138, 2022.
  49. Diffusion models: A comprehensive survey of methods and applications. ACM Computing Surveys, 56(4):1–39, 2023a.
  50. Improving diffusion-based image synthesis with context prediction. Advances in Neural Information Processing Systems, 36, 2024a.
  51. Mastering text-to-image diffusion: Recaptioning, planning, and generating with multimodal llms. arXiv preprint arXiv:2401.11708, 2024b.
  52. Reco: Region-controlled text-to-image generation. In CVPR, 2023b.
  53. Progressive text-to-image diffusion with soft latent direction. arXiv preprint arXiv:2309.09466, 2023.
  54. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.  3836–3847, 2023.
  55. Layoutdiffusion: Controllable diffusion model for layout-to-image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  22490–22499, 2023.
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Summary

  • The paper introduces a training-free framework that dynamically balances contributions from text-to-image and layout-to-image models to optimize realism and compositionality.
  • It leverages large language models to extract layout information from text prompts, integrating this data at each denoising step for enhanced spatial accuracy.
  • Experimental results on T2I-CompBench show superior performance in attribute binding and object relationships compared to state-of-the-art methods.

An Analysis of "RealCompo: Dynamic Equilibrium between Realism and Compositionality Improves Text-to-Image Diffusion Models"

The paper under discussion, "RealCompo: Dynamic Equilibrium between Realism and Compositionality Improves Text-to-Image Diffusion Models," presents a novel framework designed to enhance text-to-image generation by achieving a balance between realism and compositionality in the depiction of complex scenes. The proposed framework, named RealCompo, addresses the limitations of existing models which often struggle with generating multiple-object images that adhere to specified textual descriptions.

Key Contributions

The authors propose a new, training-free, and transferable framework aimed at the text-to-image (T2I) generation challenge by balancing the strengths of T2I models with layout-to-image (L2I) models. RealCompo introduces a balancer that dynamically adjusts the influence of T2I and L2I models during the denoising process, optimizing for both realism and compositionality without requiring additional model training. The method leverages LLMs to infer layout information from text prompts, thus enhancing the correspondence between generated image content and textual input.

Methodology

The paper delineates the detailed process of how RealCompo operates. It involves first using the in-context learning capability of LLMs to extract layout information, which is then integrated into the generation process. The core of RealCompo lies in its innovative balancer that utilizes the cross-attention maps from both T2I and L2I models to dynamically update coefficients, resulting in a balanced combination of the models' outputs. This is performed at each denoising timestep, allowing the system to integrate strengths from both models effectively.

Experimental Evaluation

In rigorous experiments conducted on T2I-CompBench, a benchmark for compositional text-to-image generation, RealCompo consistently outperformed existing state-of-the-art models across diverse tasks including attribute binding, object relationship generation, and compositional complexity. Notably, it improved attribute binding tasks, where the enhanced use of layout ensured precise alignment of attributes with corresponding objects in the generated imagery. The framework demonstrated superior performance in representing spatial relationships, where other models typically fall short due to their limited understanding of spatial terms.

Implications and Future Work

RealCompo's ability to dynamically combine different generative models without requiring additional training opens a new avenue in the domain of controllable image generation. This approach not only enhances the quality of generated images but also extends the flexibility and utility of AI in generating complex visual scenes from textual input. Future work is anticipated to explore integrating more sophisticated model backbones into RealCompo, thereby advancing its performance and applicability to even more complex tasks. Additionally, the exploration of further applications in multi-modal generation tasks could be a significant step forward.

In conclusion, this paper contributes a significant advancement by showing that a dynamic balance between realism and compositionality in text-to-image generation is feasible and beneficial. The RealCompo framework evidences robust performance improvements over existing models, promising enhancements in AI-driven image synthesis that align closely with detailed textual instructions.

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