title: SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices
    with Efficient Architectures and Training
summary: Existing text-to-image (T2I) diffusion models face several limitations,
  including large model sizes, slow runtime, and low-quality generation on mobile
  devices. This paper aims to address all of these challenges by developing an
  extremely small and fast T2I model that generates high-resolution and
  high-quality images on mobile platforms. We propose several techniques to
  achieve this goal. First, we systematically examine the design choices of the
  network architecture to reduce model parameters and latency, while ensuring
  high-quality generation. Second, to further improve generation quality, we
  employ cross-architecture knowledge distillation from a much larger model,
  using a multi-level approach to guide the training of our model from scratch.
  Third, we enable a few-step generation by integrating adversarial guidance with
  knowledge distillation. For the first time, our model SnapGen, demonstrates the
  generation of 1024x1024 px images on a mobile device around 1.4 seconds. On
  ImageNet-1K, our model, with only 372M parameters, achieves an FID of 2.06 for
  256x256 px generation. On T2I benchmarks (i.e., GenEval and DPG-Bench), our
  model with merely 379M parameters, surpasses large-scale models with billions
  of parameters at a significantly smaller size (e.g., 7x smaller than SDXL, 14x
  smaller than IF-XL).
paper_authors._id: 675bc9374c7255e45f9085f7
paper_authors.name: Dongting Hu
paper_authors.status: claimed_verified
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paper_authors.fullname: Dongting Hu
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paper_authors.user: timmy11hu
paper_authors._id: 675bc9374c7255e45f9085f8
paper_authors.name: Jierun Chen
paper_authors.status: claimed_verified
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paper_authors.fullname: Jierun Chen
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paper_authors.user: JierunChen
paper_authors._id: 675bc9374c7255e45f9085f9
paper_authors.name: Xijie Huang
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paper_authors.fullname: Xijie Huang
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paper_authors.user: ScarletAce
paper_authors._id: 675bc9374c7255e45f9085fa
paper_authors.name: Huseyin Coskun
paper_authors._id: 675bc9374c7255e45f9085fb
paper_authors.name: Arpit Sahni
paper_authors.status: claimed_verified
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paper_authors.fullname: arpit sahni
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paper_authors.user: arpit04
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paper_authors.name: Aarush Gupta
paper_authors._id: 675bc9374c7255e45f9085fd
paper_authors.name: Anujraaj Goyal
paper_authors._id: 675bc9374c7255e45f9085fe
paper_authors.name: Dishani Lahiri
paper_authors._id: 675bc9374c7255e45f9085ff
paper_authors.name: Rajesh Singh
paper_authors._id: 675bc9374c7255e45f908600
paper_authors.name: Yerlan Idelbayev
paper_authors._id: 675bc9374c7255e45f908601
paper_authors.name: Junli Cao
paper_authors._id: 675bc9374c7255e45f908602
paper_authors.name: Yanyu Li
paper_authors._id: 675bc9374c7255e45f908603
paper_authors.name: Kwang-Ting Cheng
paper_authors._id: 675bc9374c7255e45f908604
paper_authors.name: S. -H. Gary Chan
paper_authors._id: 675bc9374c7255e45f908605
paper_authors.name: Mingming Gong
paper_authors._id: 675bc9374c7255e45f908606
paper_authors.name: Sergey Tulyakov
paper_authors._id: 675bc9374c7255e45f908607
paper_authors.name: Anil Kag
paper_authors._id: 675bc9374c7255e45f908608
paper_authors.name: Yanwu Xu
paper_authors._id: 675bc9374c7255e45f908609
paper_authors.name: Jian Ren
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paper_authors.fullname: Jian Ren
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paper_authors.user: alanspike
paper_title: SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices
    with Efficient Architectures and Training
paper_summary: Existing text-to-image (T2I) diffusion models face several limitations,
  including large model sizes, slow runtime, and low-quality generation on mobile
  devices. This paper aims to address all of these challenges by developing an
  extremely small and fast T2I model that generates high-resolution and
  high-quality images on mobile platforms. We propose several techniques to
  achieve this goal. First, we systematically examine the design choices of the
  network architecture to reduce model parameters and latency, while ensuring
  high-quality generation. Second, to further improve generation quality, we
  employ cross-architecture knowledge distillation from a much larger model,
  using a multi-level approach to guide the training of our model from scratch.
  Third, we enable a few-step generation by integrating adversarial guidance with
  knowledge distillation. For the first time, our model SnapGen, demonstrates the
  generation of 1024x1024 px images on a mobile device around 1.4 seconds. On
  ImageNet-1K, our model, with only 372M parameters, achieves an FID of 2.06 for
  256x256 px generation. On T2I benchmarks (i.e., GenEval and DPG-Bench), our
  model with merely 379M parameters, surpasses large-scale models with billions
  of parameters at a significantly smaller size (e.g., 7x smaller than SDXL, 14x
  smaller than IF-XL).
paper_ai_summary: SnapGen, a compact and fast text-to-image diffusion model, generates high-resolution images on mobile devices using design optimizations, knowledge distillation, and adversarial guidance.
paper_ai_keywords: diffusion models network architecture model parameters latency cross-architecture knowledge distillation multi-level approach adversarial guidance SnapGen FID GenEval DPG-Bench SDXL IF-XL