title: Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion
    Distillation
summary: Diffusion models are the main driver of progress in image and video
  synthesis, but suffer from slow inference speed. Distillation methods, like the
  recently introduced adversarial diffusion distillation (ADD) aim to shift the
  model from many-shot to single-step inference, albeit at the cost of expensive
  and difficult optimization due to its reliance on a fixed pretrained DINOv2
  discriminator. We introduce Latent Adversarial Diffusion Distillation (LADD), a
  novel distillation approach overcoming the limitations of ADD. In contrast to
  pixel-based ADD, LADD utilizes generative features from pretrained latent
  diffusion models. This approach simplifies training and enhances performance,
  enabling high-resolution multi-aspect ratio image synthesis. We apply LADD to
  Stable Diffusion 3 (8B) to obtain SD3-Turbo, a fast model that matches the
  performance of state-of-the-art text-to-image generators using only four
  unguided sampling steps. Moreover, we systematically investigate its scaling
  behavior and demonstrate LADD's effectiveness in various applications such as
  image editing and inpainting.
paper_authors._id: 65f90374d09e592f404b84b2
paper_authors.name: Axel Sauer
paper_authors.status: admin_assigned
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paper_authors.fullname: Axel Sauer
paper_authors.type: user
paper_authors.user: xl-sr
paper_authors._id: 65f90374d09e592f404b84b3
paper_authors.name: Frederic Boesel
paper_authors.status: admin_assigned
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paper_authors.fullname: Frederic Boesel
paper_authors.type: user
paper_authors.user: fboeEl
paper_authors._id: 65f90374d09e592f404b84b4
paper_authors.name: Tim Dockhorn
paper_authors.status: admin_assigned
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paper_authors.fullname: Tim Dockhorn
paper_authors.type: user
paper_authors.user: timudk
paper_authors._id: 65f90374d09e592f404b84b5
paper_authors.name: Andreas Blattmann
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-19T09:05:23.010Z
paper_authors._id: 635fbc957c05eb9f5997b568
paper_authors.avatarUrl: /avatars/0bf85960fa7ad445b0abb2d70c9d5721.svg
paper_authors.fullname: Andreas Blattmann
paper_authors.type: user
paper_authors.user: ablattmann
paper_authors._id: 65f90374d09e592f404b84b6
paper_authors.name: Patrick Esser
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-19T09:05:29.105Z
paper_authors._id: 62f2576394fe062ca9adf3d5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1660049160797-noauth.png
paper_authors.fullname: Patrick Esser
paper_authors.type: user
paper_authors.user: pesser
paper_authors._id: 65f90374d09e592f404b84b7
paper_authors.name: Robin Rombach
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-19T09:05:35.009Z
paper_authors._id: 62cfefa74b3e8dc1e32e38bf
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1657794439283-noauth.png
paper_authors.fullname: Robin Rombach
paper_authors.type: user
paper_authors.user: rromb
paper_title: Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion
    Distillation
paper_summary: Diffusion models are the main driver of progress in image and video
  synthesis, but suffer from slow inference speed. Distillation methods, like the
  recently introduced adversarial diffusion distillation (ADD) aim to shift the
  model from many-shot to single-step inference, albeit at the cost of expensive
  and difficult optimization due to its reliance on a fixed pretrained DINOv2
  discriminator. We introduce Latent Adversarial Diffusion Distillation (LADD), a
  novel distillation approach overcoming the limitations of ADD. In contrast to
  pixel-based ADD, LADD utilizes generative features from pretrained latent
  diffusion models. This approach simplifies training and enhances performance,
  enabling high-resolution multi-aspect ratio image synthesis. We apply LADD to
  Stable Diffusion 3 (8B) to obtain SD3-Turbo, a fast model that matches the
  performance of state-of-the-art text-to-image generators using only four
  unguided sampling steps. Moreover, we systematically investigate its scaling
  behavior and demonstrate LADD's effectiveness in various applications such as
  image editing and inpainting.
paper_ai_summary: Latent Adversarial Diffusion Distillation (LADD) simplifies the training and improves the performance of diffusion models by using generative features from latent diffusion models, enabling fast and high-resolution image synthesis.
paper_ai_keywords: diffusion models adversarial diffusion distillation (ADD) pretrained DINOv2 discriminator Latent Adversarial Diffusion Distillation (LADD) pixel-based ADD latent diffusion models high-resolution multi-aspect ratio image synthesis Stable Diffusion 3 (8B) SD3-Turbo image editing image inpainting