  Diffusion models have been widely used for conditional data cross-modal
generation tasks such as text-to-image and text-to-video. However,
state-of-the-art models still fail to align the generated visual concepts with
high-level semantics in a language such as object count, spatial relationship,
etc. We approach this problem from a multimodal data fusion perspective and
investigate how different fusion strategies can affect vision-language
alignment. We discover that compared to the widely used early fusion of
conditioning text in a pretrained image feature space, a specially designed
intermediate fusion can: (i) boost text-to-image alignment with improved
generation quality and (ii) improve training and inference efficiency by
reducing low-rank text-to-image attention calculations. We perform experiments
using a text-to-image generation task on the MS-COCO dataset. We compare our
intermediate fusion mechanism with the classic early fusion mechanism on two
common conditioning methods on a U-shaped ViT backbone. Our intermediate fusion
model achieves a higher CLIP Score and lower FID, with 20% reduced FLOPs, and
50% increased training speed compared to a strong U-ViT baseline with an early
fusion.
