title: EdgeFusion: On-Device Text-to-Image Generation
summary: The intensive computational burden of Stable Diffusion (SD) for text-to-image
  generation poses a significant hurdle for its practical application. To tackle
  this challenge, recent research focuses on methods to reduce sampling steps,
  such as Latent Consistency Model (LCM), and on employing architectural
  optimizations, including pruning and knowledge distillation. Diverging from
  existing approaches, we uniquely start with a compact SD variant, BK-SDM. We
  observe that directly applying LCM to BK-SDM with commonly used crawled
  datasets yields unsatisfactory results. It leads us to develop two strategies:
  (1) leveraging high-quality image-text pairs from leading generative models and
  (2) designing an advanced distillation process tailored for LCM. Through our
  thorough exploration of quantization, profiling, and on-device deployment, we
  achieve rapid generation of photo-realistic, text-aligned images in just two
  steps, with latency under one second on resource-limited edge devices.
paper_authors._id: 6621e429a11ce46061d124d5
paper_authors.name: Thibault Castells
paper_authors.status: admin_assigned
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paper_authors.fullname: Thibault Castells
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paper_authors.name: Hyoung-Kyu Song
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paper_authors.fullname: Hyoung-Kyu Song
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paper_authors.user: deepkyu
paper_authors._id: 6621e429a11ce46061d124d7
paper_authors.name: Tairen Piao
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paper_authors.fullname: Tairen Piao
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paper_authors.user: tairenpiao
paper_authors._id: 6621e429a11ce46061d124d8
paper_authors.name: Shinkook Choi
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paper_authors.fullname: shinkookchoi
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paper_authors.user: skchoi
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paper_authors.name: Bo-Kyeong Kim
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paper_authors._id: 64374f10cd93f4c9a34c1239
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paper_authors.fullname: Bo-Kyeong Kim
paper_authors.type: user
paper_authors.user: bokyeong1015
paper_authors._id: 6621e429a11ce46061d124da
paper_authors.name: Hanyoung Yim
paper_authors._id: 6621e429a11ce46061d124db
paper_authors.name: Changgwun Lee
paper_authors._id: 6621e429a11ce46061d124dc
paper_authors.name: Jae Gon Kim
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-25T07:37:22.295Z
paper_authors._id: 6629ff27e13614048b1cdcdc
paper_authors.avatarUrl: /avatars/17af10b6a710f99e96c6eabd0fb689e8.svg
paper_authors.fullname: Jae Gon Kim
paper_authors.type: user
paper_authors.user: jgon9031
paper_authors._id: 6621e429a11ce46061d124dd
paper_authors.name: Tae-Ho Kim
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-19T08:44:57.310Z
paper_authors._id: 64cccbe8c7f30fbf7bbf5a6d
paper_authors.avatarUrl: /avatars/1c1f7c2da22c2afb3b352d446e80e5ef.svg
paper_authors.fullname: Tae-Ho Kim
paper_authors.type: user
paper_authors.user: ktho22
paper_title: EdgeFusion: On-Device Text-to-Image Generation
paper_summary: The intensive computational burden of Stable Diffusion (SD) for text-to-image
  generation poses a significant hurdle for its practical application. To tackle
  this challenge, recent research focuses on methods to reduce sampling steps,
  such as Latent Consistency Model (LCM), and on employing architectural
  optimizations, including pruning and knowledge distillation. Diverging from
  existing approaches, we uniquely start with a compact SD variant, BK-SDM. We
  observe that directly applying LCM to BK-SDM with commonly used crawled
  datasets yields unsatisfactory results. It leads us to develop two strategies:
  (1) leveraging high-quality image-text pairs from leading generative models and
  (2) designing an advanced distillation process tailored for LCM. Through our
  thorough exploration of quantization, profiling, and on-device deployment, we
  achieve rapid generation of photo-realistic, text-aligned images in just two
  steps, with latency under one second on resource-limited edge devices.
paper_ai_keywords: Stable Diffusion (SD) Latent Consistency Model (LCM) BK-SDM high-quality image-text pairs leading generative models quantization profiling on-device deployment photo-realistic text-aligned images