  The enormous success of diffusion models in text-to-image synthesis has made
them promising candidates for the next generation of end-user applications for
image generation and editing. Previous works have focused on improving the
usability of diffusion models by reducing the inference time or increasing user
interactivity by allowing new, fine-grained controls such as region-based text
prompts. However, we empirically find that integrating both branches of works
is nontrivial, limiting the potential of diffusion models. To solve this
incompatibility, we present StreamMultiDiffusion, the first real-time
region-based text-to-image generation framework. By stabilizing fast inference
techniques and restructuring the model into a newly proposed multi-prompt
stream batch architecture, we achieve $\times 10$ faster panorama generation
than existing solutions, and the generation speed of 1.57 FPS in region-based
text-to-image synthesis on a single RTX 2080 Ti GPU. Our solution opens up a
new paradigm for interactive image generation named semantic palette, where
high-quality images are generated in real-time from given multiple hand-drawn
regions, encoding prescribed semantic meanings (e.g., eagle, girl). Our code
and demo application are available at
https://github.com/ironjr/StreamMultiDiffusion.
