Towards Better Text-to-Image Generation Alignment via Attention Modulation

TL;DR

This paper introduces a training-free attention modulation mechanism for diffusion models to improve text-to-image alignment, especially with complex prompts, by guiding attention focus during generation.

Contribution

It proposes a novel attention modulation method that enhances image-text alignment without additional training or labeled data.

Findings

Improved alignment in complex prompts

Minimal additional computational cost

Effective attention focus guidance

Abstract

In text-to-image generation tasks, the advancements of diffusion models have facilitated the fidelity of generated results. However, these models encounter challenges when processing text prompts containing multiple entities and attributes. The uneven distribution of attention results in the issues of entity leakage and attribute misalignment. Training from scratch to address this issue requires numerous labeled data and is resource-consuming. Motivated by this, we propose an attribution-focusing mechanism, a training-free phase-wise mechanism by modulation of attention for diffusion model. One of our core ideas is to guide the model to concentrate on the corresponding syntactic components of the prompt at distinct timesteps. To achieve this, we incorporate a temperature control mechanism within the early phases of the self-attention modules to mitigate entity leakage issues. An object-focused masking scheme and a phase-wise dynamic weight control mechanism are integrated into the cross-attention modules, enabling the model to discern the affiliation of semantic information between entities more effectively. The experimental results in various alignment scenarios demonstrate that our model attain better image-text alignment with minimal additional computational cost.