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

Abstract

Recent advancements in diffusion models have showcased their impressive capacity to generate visually striking images. However, 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 erroneous generation of objects and their attributes is the inadequate cross-modality relation learning between the prompt and the generated images. 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 the semantic information embedding of 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 largely enhance their capability to correctly generate objects and their attributes, with negligible computation overhead compared to the original diffusion models. Our project page is https://github.com/HVision-NKU/MaskDiffusion.