Alchemist: Turning Public Text-to-Image Data into Generative Gold
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.
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Valerii Startsev
claimed_verified
2025-05-27T07:51:23.082Z
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Valerii
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sharfikeg
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Alexander Ustyuzhanin
admin_assigned
2025-05-27T13:01:13.393Z
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Alexander Ustyuzhanin
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a-ustyuzhanin
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Alexey Kirillov
claimed_verified
2025-05-27T15:50:00.349Z
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Alexey
user
alexkkir
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Dmitry Baranchuk
admin_assigned
2025-05-27T13:01:25.481Z
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Dmitry Baranchuk
user
dbaranchuk
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Sergey Kastryulin
admin_assigned
2025-05-27T13:01:31.712Z
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Sergey Kastryulin
user
snk4tr
Alchemist: Turning Public Text-to-Image Data into Generative Gold
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.
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.
text-to-image fine-tuning pre-trained generative model general-purpose datasets aesthetic quality alignment curated datasets