This content originally appeared on HackerNoon and was authored by Dilution
:::info Authors:
(1) Yuxuan Yan,Tencent with Equal contributions and yuxuanyan@tencent.com;
(2) Chi Zhang, Tencent with Equal contributions and Corresponding Author, johnczhang@tencent.com;
(3) Rui Wang, Tencent and raywwang@tencent.com;
(4) Yichao Zhou, Tencent and yichaozhou@tencent.com;
(5) Gege Zhang, Tencent and gretazhang@tencent.com;
(6) Pei Cheng, Tencent and peicheng@tencent.com;
(7) Bin Fu, Tencent and brianfu@tencent.com;
(8) Gang Yu, Tencentm and skicyyu@tencent.com.
:::
Table of Links
3. Method and 3.1. Hybrid Guidance Strategy
3.2. Handling Multiple Identities
4. Experiments
5. Conclusion
In this paper, we present an innovative approach to text-to-image generation, specifically focusing on preserving identity in the synthesized images. Our method significantly accelerates and enhances the efficiency of the image generation process. Central to our approach is the hybrid guidance strategy, which combines stylized and facial images with textual prompts, guiding the image generation process in a cohesive manner. A standout feature of our method is its ability to synthesize multi-human images, thanks to our developed multi-identity cross-attention mechanisms. Our extensive experimental evaluations, both qualitative and quantitative, have shown the advantages of our method. It surpasses baseline models and previous works in several key aspects, most notably in efficiency and the ability to maintain identity integrity in the synthesized images.
Limitation and Social Impacts. Compared to existing works like DreamBooth [43], which synthesize images of diverse subjects such as animals and objects, our model is specifically tailored for identity-preserving generation, exclusively targeting human images. Our text-to-image generation research has two key societal impacts to consider: 1) Intellectual Property Concerns. The ability of our model to create detailed and stylized images raises potential issues with copyright infringement. 2) Ethical Considerations in Facial Generation. The model’s capability to replicate human faces brings up ethical issues, especially the potential for creating offensive or culturally inappropriate images. It’s crucial to use this technology responsibly and establish guidelines to prevent its misuse in sensitive contexts.
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:::info This paper is available on arxiv under CC0 1.0 DEED license.
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This content originally appeared on HackerNoon and was authored by Dilution
Dilution | Sciencx (2024-08-14T17:00:22+00:00) FaceStudio: Put Your Face Everywhere in Seconds: Conclusion and References. Retrieved from https://www.scien.cx/2024/08/14/facestudio-put-your-face-everywhere-in-seconds-conclusion-and-references/
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