Subject-Diffusion: Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning-Reference-Cited by-同舟云学术

Subject-Diffusion: Open Domain Personalized Text-to-Image Generation without Test-time Fine-tuning

Published:2024-07-13 Issue: Volume:33 Page:1-12
ISSN:
Container-title:Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers '24
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Author:

Ma Jian¹^ORCID,Liang Junhao²^ORCID,Chen Chen¹^ORCID,Lu Haonan¹^ORCID

Affiliation:

1. OPPO AI Center, China

2. Southern University of Science and Technology, China

Publisher

ACM

Reference60 articles.

1. Yuval Alaluf, Elad Richardson, Gal Metzer, and Daniel Cohen-Or. 2023. A Neural Space-Time Representation for Text-to-Image Personalization. arXiv preprint arXiv:2305.15391 (2023).

2. Omri Avrahami, Kfir Aberman, Ohad Fried, Daniel Cohen-Or, and Dani Lischinski. 2023. Break-A-Scene: Extracting Multiple Concepts from a Single Image. arXiv preprint arXiv:2305.16311 (2023).

3. Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, 2022. ediffi: Text-to-image diffusion models with an ensemble of expert denoisers. arXiv preprint arXiv:2211.01324 (2022).

4. InstructPix2Pix: Learning to Follow Image Editing Instructions

5. COCO-Stuff: Thing and Stuff Classes in Context

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