Grounded Text-to-Image Synthesis with Attention Refocusing (2306.05427v2)

Abstract: Driven by the scalable diffusion models trained on large-scale datasets, text-to-image synthesis methods have shown compelling results. However, these models still fail to precisely follow the text prompt involving multiple objects, attributes, or spatial compositions. In this paper, we reveal the potential causes in the diffusion model's cross-attention and self-attention layers. We propose two novel losses to refocus attention maps according to a given spatial layout during sampling. Creating the layouts manually requires additional effort and can be tedious. Therefore, we explore using LLMs (LLM) to produce these layouts for our method. We conduct extensive experiments on the DrawBench, HRS, and TIFA benchmarks to evaluate our proposed method. We show that our proposed attention refocusing effectively improves the controllability of existing approaches.

Grounded Text-to-Image Synthesis with Attention Refocusing

The paper "Grounded Text-to-Image Synthesis with Attention Refocusing" by Quynh Phung, Songwei Ge, and Jia-Bin Huang presents a novel approach to enhance the accuracy and controllability in text-to-image synthesis when dealing with complex prompts. The paper addresses prevalent challenges in text-to-image diffusion models, specifically in synthesizing images from prompts that describe multiple objects with intricate spatial relationships and attributes.

Methodological Innovations

The authors introduce an innovative framework that leverages attention mechanisms to improve alignment between generated images and the input text description. The paper primarily identifies issues in both cross-attention and self-attention layers of diffusion models, which can lead to undesirable outcomes such as mixed attributes or incorrect spatial arrangements. The proposed solution comprises attention refocusing techniques, offering a model-agnostic enhancement applicable to various existing frameworks like GLIGEN and ControlNet.

Key Components of the Approach:

1. Attention Refocusing Losses:
   • Cross-Attention Refocusing (CAR): This loss targets the inaccuracies within cross-attention layers, aiming to enhance the attention given to the correct regions as dictated by the prompt's corresponding tokens. It introduces losses to optimize attention maps, focusing on foreground and minimizing misleading background attention.
   • Self-Attention Refocusing (SAR): To tackle similar issues in self-attention layers where pixel similarities could blur object boundaries, the SAR loss reduces attention to irrelevant regions, thus preserving object-specific features.
2. Layout Generation via LLMs:
   • Using state-of-the-art LLMs, such as GPT-4, to generate spatial layouts from text prompts. This step involves generating bounding boxes as intermediate representations, which serve as guidance for the image generation process, capitalizing on LLM's advanced understanding of spatial and quantitative relationships.

Experimental Evaluation

The research employs a comprehensive experimental setup to evaluate their method across several benchmarks, including DrawBench, HRS, and TIFA. These diverse datasets enable rigorous testing across different compositional challenges such as object counting, spatial relations, color, and size accuracy.

• Quantitative Outcomes: The attention refocusing approach shows consistent improvements over existing baselines. For example, it elevates F1 scores in object counting while significantly improving spatial arrangement accuracy. Such nuanced gains are crucial in settings where the generation model’s fidelity to complex prompts is essential.
• Baseline Comparison: The framework’s integration into various text-to-image models like GLIGEN yields notable enhancements, demonstrating its adaptability and effectiveness. Models enhanced with attention refocusing perform comparably or better than state-of-the-art methods without significant computational overhead.

Future Implications and Developments

The proposed design not only improves immediate outcomes in terms of text-to-image synthesis fidelity but also opens pathways for more sophisticated interaction models between LLMs and diffusion models. This dual-stage method underscores how LLMs can function beyond simple text encoders, becoming pivotal in producing more coherent visual outputs by structuring guidance at a conceptual level.

Speculative Insights for Future AI Research:

• Enhanced Human-AI Interaction: The procedural ability to iteratively refine layout and image based on user feedback can be developed into more interactive, real-time creative tools.
• Cross-Modal Synergies: The paper exemplifies how different AI models can collaboratively enhance each other’s capabilities, a concept that can be extended to other permutations of multi-modal tasks involving text, image, and potentially video generations.

This work pragmatically aligns advancements in language understanding with visual synthesis, offering a scalable solution for addressing complexities inherent in creative AI applications. The robust evaluation and demonstrable adaptability of attention refocusing mechanisms underscore its potential as a base methodology for future innovations in grounded text-to-image synthesis techniques.