mlsanityData to 7 Oct 2026

DomainGallery: Few-shot Domain-driven Image Generation by Attribute-centric Finetuning

arXiv 2411.04571 ↗First posted 7 Nov 2024cs.CVcited by 1

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Abstract (arXiv)

The recent progress in text-to-image models pretrained on large-scale datasets has enabled us to generate various images as long as we provide a text prompt describing what we want. Nevertheless, the availability of these models is still limited when we expect to generate images that fall into a specific domain either hard to describe or just unseen to the models. In this work, we propose DomainGallery, a few-shot domain-driven image generation method which aims at finetuning pretrained Stable Diffusion on few-shot target datasets in an attribute-centric manner. Specifically, DomainGallery features prior attribute erasure, attribute disentanglement, regularization and enhancement. These techniques are tailored to few-shot domain-driven generation in order to solve key issues that previous works have failed to settle. Extensive experiments are given to validate the superior performance of DomainGallery on a variety of domain-driven generation scenarios. Codes are available at https://github.com/Ldhlwh/DomainGallery.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 49 of its 62 references that are in the map. First posted on arXiv 7 Nov 2024.

Builds on

49 of 62 references.

References · 49

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37 more
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Citing papers per year: 2025: 1.

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  5. 2023Image is All You Need to Empower Large-scale Diffusion Models for In-Domain Generation
  6. 2025Domain-RAG: Retrieval-Guided Compositional Image Generation for Cross-Domain Few-Shot Object Detection
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