title: Alchemist: Turning Public Text-to-Image Data into Generative Gold
summary: Pre-training equips text-to-image (T2I) models with broad world knowledge,
  but this alone is often insufficient to achieve high aesthetic quality and
  alignment. Consequently, supervised fine-tuning (SFT) is crucial for further
  refinement. However, its effectiveness highly depends on the quality of the
  fine-tuning dataset. Existing public SFT datasets frequently target narrow
  domains (e.g., anime or specific art styles), and the creation of high-quality,
  general-purpose SFT datasets remains a significant challenge. Current curation
  methods are often costly and struggle to identify truly impactful samples. This
  challenge is further complicated by the scarcity of public general-purpose
  datasets, as leading models often rely on large, proprietary, and poorly
  documented internal data, hindering broader research progress. This paper
  introduces a novel methodology for creating general-purpose SFT datasets by
  leveraging a pre-trained generative model as an estimator of high-impact
  training samples. We apply this methodology to construct and release Alchemist,
  a compact (3,350 samples) yet highly effective SFT dataset. Experiments
  demonstrate that Alchemist substantially improves the generative quality of
  five public T2I models while preserving diversity and style. Additionally, we
  release the fine-tuned models' weights to the public.
paper_authors._id: 68354c05f7b44d5d505262c7
paper_authors.name: Valerii Startsev
paper_authors.status: claimed_verified
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paper_authors.fullname: Valerii
paper_authors.type: user
paper_authors.user: sharfikeg
paper_authors._id: 68354c05f7b44d5d505262c8
paper_authors.name: Alexander Ustyuzhanin
paper_authors.status: admin_assigned
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paper_authors._id: 682605d2a677fa26bd17440a
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paper_authors.fullname: Alexander Ustyuzhanin
paper_authors.type: user
paper_authors.user: a-ustyuzhanin
paper_authors._id: 68354c05f7b44d5d505262c9
paper_authors.name: Alexey Kirillov
paper_authors.status: claimed_verified
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paper_authors._id: 643322f86c2a26ae66d4eb66
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paper_authors.fullname: Alexey
paper_authors.type: user
paper_authors.user: alexkkir
paper_authors._id: 68354c05f7b44d5d505262ca
paper_authors.name: Dmitry Baranchuk
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T13:01:25.481Z
paper_authors._id: 62b6cc49752323892323bc04
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paper_authors.fullname: Dmitry Baranchuk
paper_authors.type: user
paper_authors.user: dbaranchuk
paper_authors._id: 68354c05f7b44d5d505262cb
paper_authors.name: Sergey Kastryulin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-27T13:01:31.712Z
paper_authors._id: 64aeb6aa59d35c5f8180ba7c
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paper_authors.fullname: Sergey Kastryulin
paper_authors.type: user
paper_authors.user: snk4tr
paper_title: Alchemist: Turning Public Text-to-Image Data into Generative Gold
paper_summary: Pre-training equips text-to-image (T2I) models with broad world knowledge,
  but this alone is often insufficient to achieve high aesthetic quality and
  alignment. Consequently, supervised fine-tuning (SFT) is crucial for further
  refinement. However, its effectiveness highly depends on the quality of the
  fine-tuning dataset. Existing public SFT datasets frequently target narrow
  domains (e.g., anime or specific art styles), and the creation of high-quality,
  general-purpose SFT datasets remains a significant challenge. Current curation
  methods are often costly and struggle to identify truly impactful samples. This
  challenge is further complicated by the scarcity of public general-purpose
  datasets, as leading models often rely on large, proprietary, and poorly
  documented internal data, hindering broader research progress. This paper
  introduces a novel methodology for creating general-purpose SFT datasets by
  leveraging a pre-trained generative model as an estimator of high-impact
  training samples. We apply this methodology to construct and release Alchemist,
  a compact (3,350 samples) yet highly effective SFT dataset. Experiments
  demonstrate that Alchemist substantially improves the generative quality of
  five public T2I models while preserving diversity and style. Additionally, we
  release the fine-tuned models' weights to the public.
paper_ai_summary: A new method using a pre-trained generative model helps construct a high-impact SFT dataset, Alchemist, which improves the generative quality of text-to-image models while maintaining diversity.
paper_ai_keywords: text-to-image fine-tuning pre-trained generative model general-purpose datasets aesthetic quality alignment curated datasets