Enhancing Semantic Fidelity in Text-to-Image Synthesis: Attention Regulation in Diffusion Models
This addresses the issue of semantic inaccuracies in generated images for users of text-to-image models, though it is incremental as it builds on existing diffusion models.
The paper tackles the problem of diffusion models in text-to-image synthesis often failing to accurately reflect text semantics due to disproportionate attention on certain tokens, and introduces attention regulation as an inference-time optimization that improves semantic fidelity with reduced computation overhead.
Recent advancements in diffusion models have notably improved the perceptual quality of generated images in text-to-image synthesis tasks. However, diffusion models often struggle to produce images that accurately reflect the intended semantics of the associated text prompts. We examine cross-attention layers in diffusion models and observe a propensity for these layers to disproportionately focus on certain tokens during the generation process, thereby undermining semantic fidelity. To address the issue of dominant attention, we introduce attention regulation, a computation-efficient on-the-fly optimization approach at inference time to align attention maps with the input text prompt. Notably, our method requires no additional training or fine-tuning and serves as a plug-in module on a model. Hence, the generation capacity of the original model is fully preserved. We compare our approach with alternative approaches across various datasets, evaluation metrics, and diffusion models. Experiment results show that our method consistently outperforms other baselines, yielding images that more faithfully reflect the desired concepts with reduced computation overhead. Code is available at https://github.com/YaNgZhAnG-V5/attention_regulation.