title: YaART: Yet Another ART Rendering Technology
summary: In the rapidly progressing field of generative models, the development of
  efficient and high-fidelity text-to-image diffusion systems represents a
  significant frontier. This study introduces YaART, a novel production-grade
  text-to-image cascaded diffusion model aligned to human preferences using
  Reinforcement Learning from Human Feedback (RLHF). During the development of
  YaART, we especially focus on the choices of the model and training dataset
  sizes, the aspects that were not systematically investigated for text-to-image
  cascaded diffusion models before. In particular, we comprehensively analyze how
  these choices affect both the efficiency of the training process and the
  quality of the generated images, which are highly important in practice.
  Furthermore, we demonstrate that models trained on smaller datasets of
  higher-quality images can successfully compete with those trained on larger
  datasets, establishing a more efficient scenario of diffusion models training.
  From the quality perspective, YaART is consistently preferred by users over
  many existing state-of-the-art models.
paper_authors._id: 6614c86dcf3fef4fa84e5fd1
paper_authors.name: Sergey Kastryulin
paper_authors._id: 6614c86dcf3fef4fa84e5fd2
paper_authors.name: Artem Konev
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paper_authors.name: Alexander Shishenya
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paper_authors.name: Eugene Lyapustin
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paper_authors.name: Artem Khurshudov
paper_authors._id: 6614c86dcf3fef4fa84e5fd6
paper_authors.name: Alexander Tselousov
paper_authors._id: 6614c86dcf3fef4fa84e5fd7
paper_authors.name: Nikita Vinokurov
paper_authors._id: 6614c86dcf3fef4fa84e5fd8
paper_authors.name: Denis Kuznedelev
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paper_authors.fullname: Denis Kuznedelev
paper_authors.type: user
paper_authors.user: SpiridonSunRotator
paper_authors._id: 6614c86dcf3fef4fa84e5fd9
paper_authors.name: Alexander Markovich
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paper_authors.name: Grigoriy Livshits
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paper_authors.name: Alexey Kirillov
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paper_authors.statusLastChangedAt: 2025-05-27T15:50:55.964Z
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paper_authors.fullname: Alexey
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paper_authors.user: alexkkir
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paper_authors.name: Anastasiia Tabisheva
paper_authors._id: 6614c86dcf3fef4fa84e5fdd
paper_authors.name: Liubov Chubarova
paper_authors._id: 6614c86dcf3fef4fa84e5fde
paper_authors.name: Marina Kaminskaia
paper_authors._id: 6614c86dcf3fef4fa84e5fdf
paper_authors.name: Alexander Ustyuzhanin
paper_authors._id: 6614c86dcf3fef4fa84e5fe0
paper_authors.name: Artemii Shvetsov
paper_authors._id: 6614c86dcf3fef4fa84e5fe1
paper_authors.name: Daniil Shlenskii
paper_authors._id: 6614c86dcf3fef4fa84e5fe2
paper_authors.name: Valerii Startsev
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T10:03:51.445Z
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63725a2eacef705233c62876/QlRm8oq7O8THzUhATYQlH.jpeg
paper_authors.fullname: Valerii
paper_authors.type: user
paper_authors.user: sharfikeg
paper_authors._id: 6614c86dcf3fef4fa84e5fe3
paper_authors.name: Dmitrii Kornilov
paper_authors._id: 6614c86dcf3fef4fa84e5fe4
paper_authors.name: Mikhail Romanov
paper_authors._id: 6614c86dcf3fef4fa84e5fe5
paper_authors.name: Artem Babenko
paper_authors._id: 6614c86dcf3fef4fa84e5fe6
paper_authors.name: Sergei Ovcharenko
paper_authors._id: 6614c86dcf3fef4fa84e5fe7
paper_authors.name: Valentin Khrulkov
paper_title: YaART: Yet Another ART Rendering Technology
paper_summary: In the rapidly progressing field of generative models, the development of
  efficient and high-fidelity text-to-image diffusion systems represents a
  significant frontier. This study introduces YaART, a novel production-grade
  text-to-image cascaded diffusion model aligned to human preferences using
  Reinforcement Learning from Human Feedback (RLHF). During the development of
  YaART, we especially focus on the choices of the model and training dataset
  sizes, the aspects that were not systematically investigated for text-to-image
  cascaded diffusion models before. In particular, we comprehensively analyze how
  these choices affect both the efficiency of the training process and the
  quality of the generated images, which are highly important in practice.
  Furthermore, we demonstrate that models trained on smaller datasets of
  higher-quality images can successfully compete with those trained on larger
  datasets, establishing a more efficient scenario of diffusion models training.
  From the quality perspective, YaART is consistently preferred by users over
  many existing state-of-the-art models.
paper_ai_summary: YaART, a text-to-image cascaded diffusion model enhanced with RLHF, achieves high-quality image generation efficiently and outperforms existing state-of-the-art models in user evaluations.
paper_ai_keywords: text-to-image diffusion systems cascaded diffusion model Reinforcement Learning from Human Feedback (RLHF) model and training dataset sizes quality of generated images user preferences