title: Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in
    Text-to-Image Generation
summary: In this work, we share three insights for achieving state-of-the-art
  aesthetic quality in text-to-image generative models. We focus on three
  critical aspects for model improvement: enhancing color and contrast, improving
  generation across multiple aspect ratios, and improving human-centric fine
  details. First, we delve into the significance of the noise schedule in
  training a diffusion model, demonstrating its profound impact on realism and
  visual fidelity. Second, we address the challenge of accommodating various
  aspect ratios in image generation, emphasizing the importance of preparing a
  balanced bucketed dataset. Lastly, we investigate the crucial role of aligning
  model outputs with human preferences, ensuring that generated images resonate
  with human perceptual expectations. Through extensive analysis and experiments,
  Playground v2.5 demonstrates state-of-the-art performance in terms of aesthetic
  quality under various conditions and aspect ratios, outperforming both
  widely-used open-source models like SDXL and Playground v2, and closed-source
  commercial systems such as DALLE 3 and Midjourney v5.2. Our model is
  open-source, and we hope the development of Playground v2.5 provides valuable
  guidelines for researchers aiming to elevate the aesthetic quality of
  diffusion-based image generation models.
paper_authors._id: 65deb1fa06a8c85c5e9bb72e
paper_authors.name: Daiqing Li
paper_authors.status: admin_assigned
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paper_authors.fullname: Li
paper_authors.type: user
paper_authors.user: Daiqing
paper_authors._id: 65deb1fa06a8c85c5e9bb72f
paper_authors.name: Aleks Kamko
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paper_authors.fullname: Aleks Kamko
paper_authors.type: user
paper_authors.user: aykamko
paper_authors._id: 65deb1fa06a8c85c5e9bb730
paper_authors.name: Ehsan Akhgari
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paper_authors.fullname: Ehsan Akhgari
paper_authors.type: user
paper_authors.user: ehsanakh
paper_authors._id: 65deb1fa06a8c85c5e9bb731
paper_authors.name: Ali Sabet
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-28T12:42:59.815Z
paper_authors._id: 642dceb0355bb9a2a43954a4
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paper_authors.fullname: Ali Sabet
paper_authors.type: user
paper_authors.user: asabet-pai
paper_authors._id: 65deb1fa06a8c85c5e9bb732
paper_authors.name: Linmiao Xu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-28T12:43:06.121Z
paper_authors._id: 63653383a7a1324ccd56b370
paper_authors.avatarUrl: /avatars/d623a5bf1ee8fb8ebaeef26b661101ff.svg
paper_authors.fullname: Linmiao Xu
paper_authors.type: user
paper_authors.user: linrock
paper_authors._id: 65deb1fa06a8c85c5e9bb733
paper_authors.name: Suhail Doshi
paper_title: Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in
    Text-to-Image Generation
paper_summary: In this work, we share three insights for achieving state-of-the-art
  aesthetic quality in text-to-image generative models. We focus on three
  critical aspects for model improvement: enhancing color and contrast, improving
  generation across multiple aspect ratios, and improving human-centric fine
  details. First, we delve into the significance of the noise schedule in
  training a diffusion model, demonstrating its profound impact on realism and
  visual fidelity. Second, we address the challenge of accommodating various
  aspect ratios in image generation, emphasizing the importance of preparing a
  balanced bucketed dataset. Lastly, we investigate the crucial role of aligning
  model outputs with human preferences, ensuring that generated images resonate
  with human perceptual expectations. Through extensive analysis and experiments,
  Playground v2.5 demonstrates state-of-the-art performance in terms of aesthetic
  quality under various conditions and aspect ratios, outperforming both
  widely-used open-source models like SDXL and Playground v2, and closed-source
  commercial systems such as DALLE 3 and Midjourney v5.2. Our model is
  open-source, and we hope the development of Playground v2.5 provides valuable
  guidelines for researchers aiming to elevate the aesthetic quality of
  diffusion-based image generation models.
paper_ai_keywords: diffusion model noise schedule realism visual fidelity aspect ratios balanced bucketed dataset human preferences perceptual expectations aesthetic quality SDXL DALLE 3 Midjourney v5.2 diffusion-based image generation models