MaskDiffusion: Boosting Text-to-Image Consistency with Conditional Mask
This work addresses a persistent challenge in text-to-image generation for users needing more reliable outputs, though it is incremental as it builds on existing diffusion models.
The paper tackles the problem of text-to-image mismatch in diffusion models by introducing an adaptive mask to enhance cross-modality alignment, resulting in significantly improved consistency with negligible computational overhead.
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.