Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis (2212.05032v3)

Abstract: Large-scale diffusion models have achieved state-of-the-art results on text-to-image synthesis (T2I) tasks. Despite their ability to generate high-quality yet creative images, we observe that attribution-binding and compositional capabilities are still considered major challenging issues, especially when involving multiple objects. In this work, we improve the compositional skills of T2I models, specifically more accurate attribute binding and better image compositions. To do this, we incorporate linguistic structures with the diffusion guidance process based on the controllable properties of manipulating cross-attention layers in diffusion-based T2I models. We observe that keys and values in cross-attention layers have strong semantic meanings associated with object layouts and content. Therefore, we can better preserve the compositional semantics in the generated image by manipulating the cross-attention representations based on linguistic insights. Built upon Stable Diffusion, a SOTA T2I model, our structured cross-attention design is efficient that requires no additional training samples. We achieve better compositional skills in qualitative and quantitative results, leading to a 5-8% advantage in head-to-head user comparison studies. Lastly, we conduct an in-depth analysis to reveal potential causes of incorrect image compositions and justify the properties of cross-attention layers in the generation process.

Training-Free Structured Diffusion Guidance for Improved Compositional Text-to-Image Synthesis

The paper "Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesis" addresses a significant challenge within the domain of text-to-image (T2I) synthesis: the compositional generation of images that faithfully correspond to the attributes and relationships specified in input text prompts. Previous T2I models, particularly those leveraging diffusion processes, have demonstrated impressive capabilities in generating high-quality images. However, challenges remain, especially concerning the accurate binding of attributes to specific objects and achieving coherent compositions involving multiple entities within a scene.

Key Contributions

1. Attribution and Compositional Fidelity: The paper identifies two critical issues in existing T2I models—attribute binding and compositional skills. Attribute binding involves associating the correct attributes with the respective objects (e.g., colors or sizes), while compositional skills require generating images that accurately combine and reflect multiple distinct concepts from a given text prompt.
2. Structured Diffusion Guidance (StructureDiffusion): A novel approach is introduced that incorporates linguistic structures into the diffusion guidance process, without requiring additional training. By modifying cross-attention layers within diffusion models using structured representations like constituency trees or scene graphs, the method preserves the semantic integrity of input prompts. This manipulation facilitates better attribute-object mapping and improved semantic coherence in generated images.
3. Efficiency and Practical Implementation: Significantly, the proposed method, StructureDiffusion, imposes no requirement for additional training samples. It operates in conjunction with existing models such as Stable Diffusion, leveraging pretrained components like CLIP text encoders to encode structured representations of text inputs.
4. Empirical Validation: The empirical results demonstrate the effectiveness of this approach, showing a measurable improvement in compositional image synthesis. Qualitatively and quantitatively, the proposed method achieves a 5-8% advantage in user comparison studies over baseline approaches like Stable Diffusion.

Implications and Theoretical Insights

• Model Adaptability: The introduction of a training-free, structured guidance mechanism significantly eases the burden of model retraining. It offers a flexible adaptation layer for existing powerful models without requiring extensive computational resources.
• Attention Manipulation: The paper highlights the pivotal role of cross-attention layers in mediating the interactions between text embeddings and image features. By strategically modifying attention mechanisms, this work offers insights into more interpretable and controllable T2I generation processes.
• Benchmarking: A newly proposed benchmark, Attribute Binding Contrast set (ABC-6K), has been developed to quantitatively and qualitatively measure the object-attribute binding capabilities of T2I models, offering a valuable tool for future research on compositional synthesis.

Future Directions

1. Extended Compositional Understanding: While current methods show advancements in simple to moderately complex compositions, there remains a need for models with deeper understanding to tackle more nuanced scenes and abstract prompts.
2. Integration with More Diverse Linguistic Structures: The potential for integrating other forms of linguistic analysis, such as dependency parsing or advanced scene graph representations, could further enhance the comprehensiveness of T2I models.
3. Exploration of Attention Layers: Deeper exploration into the dynamics of attention layers across different model architectures could yield further optimization strategies, particularly in improving the perceptual quality and cohesiveness of generated images.

In summary, the paper significantly contributes to the field of text-to-image synthesis by tackling the compositional challenges in modern diffusion models. Through a judicious use of structured linguistic information, it provides a scalable approach to improving attribute binding and overall scene representation. The findings pave the way for more nuanced and flexible model designs, encouraging the refinement of attention-based mechanisms in visual-linguistic tasks.