MaskDiffusion: Boosting Text-to-Image Consistency with Conditional Mask

TL;DR

MaskDiffusion enhances text-to-image consistency in diffusion models by adaptively masking cross-attention, explicitly reducing semantic ambiguity and improving alignment without additional training.

Contribution

It introduces a training-free, plug-and-play adaptive masking mechanism in cross-attention to improve prompt-image alignment in diffusion models.

Findings

Significant improvement in text-image consistency.

Negligible additional computational cost.

Applicable to pre-trained latent diffusion models.

Abstract

Recent advancements in diffusion models have showcased their impressive capacity to generate visually striking images. Nevertheless, ensuring a close match between the generated image and the given prompt remains a persistent challenge. In this work, we identify that a crucial factor leading to the text-image mismatch issue is the inadequate cross-modality relation learning between the prompt and the output image. To better align the prompt and image content, we advance the cross-attention with an adaptive mask, which is conditioned on the attention maps and the prompt embeddings, to dynamically adjust the contribution of each text token to the image features. This mechanism explicitly diminishes the ambiguity in semantic information embedding from the text encoder, leading to a boost of text-to-image consistency in the synthesized images. Our method, termed MaskDiffusion, is training-free and hot-pluggable for popular pre-trained diffusion models. When applied to the latent diffusion models, our MaskDiffusion can significantly improve the text-to-image consistency with negligible computation overhead compared to the original diffusion models.