title: Wuerstchen: Efficient Pretraining of Text-to-Image Models
summary: We introduce Wuerstchen, a novel technique for text-to-image synthesis that
  unites competitive performance with unprecedented cost-effectiveness and ease
  of training on constrained hardware. Building on recent advancements in machine
  learning, our approach, which utilizes latent diffusion strategies at strong
  latent image compression rates, significantly reduces the computational burden,
  typically associated with state-of-the-art models, while preserving, if not
  enhancing, the quality of generated images. Wuerstchen achieves notable speed
  improvements at inference time, thereby rendering real-time applications more
  viable. One of the key advantages of our method lies in its modest training
  requirements of only 9,200 GPU hours, slashing the usual costs significantly
  without compromising the end performance. In a comparison against the
  state-of-the-art, we found the approach to yield strong competitiveness. This
  paper opens the door to a new line of research that prioritizes both
  performance and computational accessibility, hence democratizing the use of
  sophisticated AI technologies. Through Wuerstchen, we demonstrate a compelling
  stride forward in the realm of text-to-image synthesis, offering an innovative
  path to explore in future research.
paper_authors._id: 64797b91bb9a5693c48cff56
paper_authors.name: Pablo Pernias
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-06-02T10:09:25.495Z
paper_authors._id: 62cd414f3e5ba89c40f0f296
paper_authors.avatarUrl: /avatars/e89f8d2665592c145994d1052cc9dd95.svg
paper_authors.fullname: Pablo Pernías
paper_authors.type: user
paper_authors.user: babbleberns
paper_authors._id: 64797b91bb9a5693c48cff57
paper_authors.name: Dominic Rampas
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T08:00:18.333Z
paper_authors._id: 634cb5eefb80cc6bcaf63c3e
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paper_authors.fullname: Dominic Rampas
paper_authors.type: user
paper_authors.user: dome272
paper_authors._id: 64797b91bb9a5693c48cff58
paper_authors.name: Marc Aubreville
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-06-02T05:20:20.976Z
paper_authors._id: 6305bf83660f01f1509ce1c2
paper_authors.avatarUrl: /avatars/efecf251f6901cc066e79477f36a1222.svg
paper_authors.fullname: Marc Aubreville
paper_authors.type: user
paper_authors.user: maubreville
paper_title: Wuerstchen: Efficient Pretraining of Text-to-Image Models
paper_summary: We introduce Wuerstchen, a novel technique for text-to-image synthesis that
  unites competitive performance with unprecedented cost-effectiveness and ease
  of training on constrained hardware. Building on recent advancements in machine
  learning, our approach, which utilizes latent diffusion strategies at strong
  latent image compression rates, significantly reduces the computational burden,
  typically associated with state-of-the-art models, while preserving, if not
  enhancing, the quality of generated images. Wuerstchen achieves notable speed
  improvements at inference time, thereby rendering real-time applications more
  viable. One of the key advantages of our method lies in its modest training
  requirements of only 9,200 GPU hours, slashing the usual costs significantly
  without compromising the end performance. In a comparison against the
  state-of-the-art, we found the approach to yield strong competitiveness. This
  paper opens the door to a new line of research that prioritizes both
  performance and computational accessibility, hence democratizing the use of
  sophisticated AI technologies. Through Wuerstchen, we demonstrate a compelling
  stride forward in the realm of text-to-image synthesis, offering an innovative
  path to explore in future research.
paper_ai_keywords: latent diffusion strategies latent image compression rates computational burden generated images inference time real-time applications training requirements GPU hours state-of-the-art