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RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion Models (2402.12908v3)

Published 20 Feb 2024 in cs.CV, cs.AI, and cs.LG

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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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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Authors (11)
  1. Xinchen Zhang (22 papers)
  2. Ling Yang (88 papers)
  3. Yaqi Cai (4 papers)
  4. Zhaochen Yu (7 papers)
  5. Jiake Xie (6 papers)
  6. Ye Tian (190 papers)
  7. Minkai Xu (40 papers)
  8. Yong Tang (86 papers)
  9. Yujiu Yang (155 papers)
  10. Bin Cui (165 papers)
  11. Kai-Ni Wang (5 papers)
Citations (1)
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