  Images produced by text-to-image diffusion models might not always faithfully
represent the semantic intent of the provided text prompt, where the model
might overlook or entirely fail to produce certain objects. Existing solutions
often require customly tailored functions for each of these problems, leading
to sub-optimal results, especially for complex prompts. Our work introduces a
novel perspective by tackling this challenge in a contrastive context. Our
approach intuitively promotes the segregation of objects in attention maps
while also maintaining that pairs of related attributes are kept close to each
other. We conduct extensive experiments across a wide variety of scenarios,
each involving unique combinations of objects, attributes, and scenes. These
experiments effectively showcase the versatility, efficiency, and flexibility
of our method in working with both latent and pixel-based diffusion models,
including Stable Diffusion and Imagen. Moreover, we publicly share our source
code to facilitate further research.
