aMUSEd: An Open MUSE Reproduction
Suraj Patil William Berman Robin Rombach Patrick von Platen
We present aMUSEd, an open-source, lightweight masked image model (MIM) for
text-to-image generation based on MUSE. With 10 percent of MUSE's parameters,
aMUSEd is focused on fast image generation. We believe MIM is under-explored
compared to latent diffusion, the prevailing approach for text-to-image
generation. Compared to latent diffusion, MIM requires fewer inference steps
and is more interpretable. Additionally, MIM can be fine-tuned to learn
additional styles with only a single image. We hope to encourage further
exploration of MIM by demonstrating its effectiveness on large-scale
text-to-image generation and releasing reproducible training code. We also
release checkpoints for two models which directly produce images at 256x256 and
512x512 resolutions.