title: Kandinsky 3.0 Technical Report
summary: We present Kandinsky 3.0, a large-scale text-to-image generation model based
  on latent diffusion, continuing the series of text-to-image Kandinsky models
  and reflecting our progress to achieve higher quality and realism of image
  generation. Compared to previous versions of Kandinsky 2.x, Kandinsky 3.0
  leverages a two times larger U-Net backbone, a ten times larger text encoder
  and removes diffusion mapping. We describe the architecture of the model, the
  data collection procedure, the training technique, and the production system of
  user interaction. We focus on the key components that, as we have identified as
  a result of a large number of experiments, had the most significant impact on
  improving the quality of our model compared to the others. By our side-by-side
  comparisons, Kandinsky becomes better in text understanding and works better on
  specific domains. Project page: https://ai-forever.github.io/Kandinsky-3
paper_authors._id: 6571304087a92b76924c7a31
paper_authors.name: Vladimir Arkhipkin
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paper_authors.fullname: Arkhipkin Vladimir
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paper_authors.name: Andrei Filatov
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paper_authors.fullname: Andrei Filatov
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paper_authors.name: Viacheslav Vasilev
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paper_authors.fullname: Viacheslav Vasilev
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paper_authors.name: Anastasia Maltseva
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paper_authors.name: Said Azizov
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paper_authors.fullname: Said Azizov
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paper_authors.user: stazizov
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paper_authors.name: Igor Pavlov
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paper_authors.fullname: Igor Pavlov
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paper_authors.user: boomb0om
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paper_authors.name: Julia Agafonova
paper_authors._id: 6571304087a92b76924c7a38
paper_authors.name: Andrey Kuznetsov
paper_authors.status: admin_assigned
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paper_authors.fullname: Andrey Kuznetsov
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paper_authors.user: kuznetsoffandrey
paper_authors._id: 6571304087a92b76924c7a39
paper_authors.name: Denis Dimitrov
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paper_authors.fullname: Denis Dimitrov
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paper_title: Kandinsky 3.0 Technical Report
paper_summary: We present Kandinsky 3.0, a large-scale text-to-image generation model based
  on latent diffusion, continuing the series of text-to-image Kandinsky models
  and reflecting our progress to achieve higher quality and realism of image
  generation. Compared to previous versions of Kandinsky 2.x, Kandinsky 3.0
  leverages a two times larger U-Net backbone, a ten times larger text encoder
  and removes diffusion mapping. We describe the architecture of the model, the
  data collection procedure, the training technique, and the production system of
  user interaction. We focus on the key components that, as we have identified as
  a result of a large number of experiments, had the most significant impact on
  improving the quality of our model compared to the others. By our side-by-side
  comparisons, Kandinsky becomes better in text understanding and works better on
  specific domains. Project page: https://ai-forever.github.io/Kandinsky-3
paper_ai_summary: Kandinsky 3.0, a large-scale text-to-image model based on latent diffusion, improves quality and realism through a larger architecture and advanced text understanding.
paper_ai_keywords: latent diffusion U-Net text encoder diffusion mapping text-to-image model architecture text understanding specific domains