  While recent developments in text-to-image generative models have led to a
suite of high-performing methods capable of producing creative imagery from
free-form text, there are several limitations. By analyzing the cross-attention
representations of these models, we notice two key issues. First, for text
prompts that contain multiple concepts, there is a significant amount of
pixel-space overlap (i.e., same spatial regions) among pairs of different
concepts. This eventually leads to the model being unable to distinguish
between the two concepts and one of them being ignored in the final generation.
Next, while these models attempt to capture all such concepts during the
beginning of denoising (e.g., first few steps) as evidenced by cross-attention
maps, this knowledge is not retained by the end of denoising (e.g., last few
steps). Such loss of knowledge eventually leads to inaccurate generation
outputs. To address these issues, our key innovations include two test-time
attention-based loss functions that substantially improve the performance of
pretrained baseline text-to-image diffusion models. First, our attention
segregation loss reduces the cross-attention overlap between attention maps of
different concepts in the text prompt, thereby reducing the confusion/conflict
among various concepts and the eventual capture of all concepts in the
generated output. Next, our attention retention loss explicitly forces
text-to-image diffusion models to retain cross-attention information for all
concepts across all denoising time steps, thereby leading to reduced
information loss and the preservation of all concepts in the generated output.
