title: SDXL: Improving Latent Diffusion Models for High-Resolution Image
    Synthesis
summary: We present SDXL, a latent diffusion model for text-to-image synthesis.
  Compared to previous versions of Stable Diffusion, SDXL leverages a three times
  larger UNet backbone: The increase of model parameters is mainly due to more
  attention blocks and a larger cross-attention context as SDXL uses a second
  text encoder. We design multiple novel conditioning schemes and train SDXL on
  multiple aspect ratios. We also introduce a refinement model which is used to
  improve the visual fidelity of samples generated by SDXL using a post-hoc
  image-to-image technique. We demonstrate that SDXL shows drastically improved
  performance compared the previous versions of Stable Diffusion and achieves
  results competitive with those of black-box state-of-the-art image generators.
  In the spirit of promoting open research and fostering transparency in large
  model training and evaluation, we provide access to code and model weights at
  https://github.com/Stability-AI/generative-models
paper_authors._id: 64a6318d711fc67c6a7d7896
paper_authors.name: Dustin Podell
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paper_authors.fullname: Dustin Podell
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paper_authors.name: Zion English
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paper_authors.name: Kyle Lacey
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paper_authors.user: TwoPerCent
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paper_authors.name: Andreas Blattmann
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paper_authors.fullname: Andreas Blattmann
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paper_authors.name: Tim Dockhorn
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paper_authors.fullname: Tim Dockhorn
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paper_authors.name: Jonas Müller
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paper_authors.fullname: Jonas Müller
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paper_authors.user: jaymueller
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paper_authors.name: Joe Penna
paper_authors.status: claimed_verified
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paper_authors._id: 63055f6d99870e13d3dece08
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paper_authors.fullname: Joe Penna
paper_authors.type: user
paper_authors.user: MysteryGuitarMan
paper_authors._id: 64a6318d711fc67c6a7d789d
paper_authors.name: Robin Rombach
paper_authors.status: admin_assigned
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paper_authors.fullname: Robin Rombach
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paper_title: SDXL: Improving Latent Diffusion Models for High-Resolution Image
    Synthesis
paper_summary: We present SDXL, a latent diffusion model for text-to-image synthesis.
  Compared to previous versions of Stable Diffusion, SDXL leverages a three times
  larger UNet backbone: The increase of model parameters is mainly due to more
  attention blocks and a larger cross-attention context as SDXL uses a second
  text encoder. We design multiple novel conditioning schemes and train SDXL on
  multiple aspect ratios. We also introduce a refinement model which is used to
  improve the visual fidelity of samples generated by SDXL using a post-hoc
  image-to-image technique. We demonstrate that SDXL shows drastically improved
  performance compared the previous versions of Stable Diffusion and achieves
  results competitive with those of black-box state-of-the-art image generators.
  In the spirit of promoting open research and fostering transparency in large
  model training and evaluation, we provide access to code and model weights at
  https://github.com/Stability-AI/generative-models
paper_ai_summary: SDXL, a latent diffusion model using a larger UNet with additional text encoders and attention mechanisms, improves text-to-image synthesis significantly.
paper_ai_keywords: latent diffusion model UNet attention blocks cross-attention context text encoder conditioning schemes refinement model image-to-image technique visual fidelity