  Diffusion models have achieved remarkable results in generating high-quality,
diverse, and creative images. However, when it comes to text-based image
generation, they often fail to capture the intended meaning presented in the
text. For instance, a specified object may not be generated, an unnecessary
object may be generated, and an adjective may alter objects it was not intended
to modify. Moreover, we found that relationships indicating possession between
objects are often overlooked. While users' intentions in text are diverse,
existing methods tend to specialize in only some aspects of these. In this
paper, we propose Predicated Diffusion, a unified framework to express users'
intentions. We consider that the root of the above issues lies in the text
encoder, which often focuses only on individual words and neglects the logical
relationships between them. The proposed method does not solely rely on the
text encoder, but instead, represents the intended meaning in the text as
propositions using predicate logic and treats the pixels in the attention maps
as the fuzzy predicates. This enables us to obtain a differentiable loss
function that makes the image fulfill the proposition by minimizing it. When
compared to several existing methods, we demonstrated that Predicated Diffusion
can generate images that are more faithful to various text prompts, as verified
by human evaluators and pretrained image-text models.
