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FaceStudio: Put Your Face Everywhere in Seconds: Method and Hybrid Guidance Strategy
by DilutionAugust 14th, 2024
Too Long; Didn't Read
This paper is available on arxiv(https://arxiv.org/abs/2312.02663) under CC0 1.0 DEED license. 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.
Authors:
(1) Yuxuan Yan,Tencent with Equal contributions and [email protected];
(2) Chi Zhang, Tencent with Equal contributions and Corresponding Author, [email protected];
(3) Rui Wang, Tencent and [email protected];
(4) Yichao Zhou, Tencent and [email protected];
(5) Gege Zhang, Tencent and [email protected];
(6) Pei Cheng, Tencent and [email protected];
(7) Bin Fu, Tencent and [email protected];
(8) Gang Yu, Tencentm and [email protected].
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
This paper is available on arxiv under CC0 1.0 DEED license.
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