title: aMUSEd: An Open MUSE Reproduction
summary: 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.
paper_authors._id: 65961d6012c3ee130fac1d2d
paper_authors.name: Suraj Patil
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-04T13:59:49.014Z
paper_authors._id: 5ec0135ded25d76864d553f1
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1600014820272-5ec0135ded25d76864d553f1.jpeg
paper_authors.fullname: Suraj Patil
paper_authors.type: user
paper_authors.user: valhalla
paper_authors._id: 65961d6012c3ee130fac1d2e
paper_authors.name: William Berman
paper_authors._id: 65961d6012c3ee130fac1d2f
paper_authors.name: Robin Rombach
paper_authors._id: 65961d6012c3ee130fac1d30
paper_authors.name: Patrick von Platen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-04T14:23:40.680Z
paper_authors._id: 5dfcb1aada6d0311fd3d5448
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1584435275418-5dfcb1aada6d0311fd3d5448.jpeg
paper_authors.fullname: Patrick von Platen
paper_authors.type: user
paper_authors.user: patrickvonplaten
paper_title: aMUSEd: An Open MUSE Reproduction
paper_summary: 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.
paper_ai_keywords: masked image model MIM MUSE text-to-image generation latent diffusion inference steps fine-tuning reproducible training code checkpoints