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
3. Method
In this section, we present the design and functionalities of our novel framework. Our method fundamentally builds on StableDiffusion [42], with several pivotal modifications, especially in the condition modules catering to hybridguidance image generation. We start by elaborating on our hybrid guidance design in the proposed condition module. Following that, we delve into the mechanism for managing multiple identities within images. Lastly, we discuss the training strategy of our models. The overview of our model structure is shown in Fig. 2.
3.1. Hybrid Guidance Strategy
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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 (2024-08-14T13:00:17+00:00) FaceStudio: Put Your Face Everywhere in Seconds: Method and Hybrid Guidance Strategy. Retrieved from https://www.scien.cx/2024/08/14/facestudio-put-your-face-everywhere-in-seconds-method-and-hybrid-guidance-strategy/
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