FaceStudio: Put Your Face Everywhere in Seconds: Method and Hybrid Guidance Strategy

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 Abstract and 1 Introduction 2. Related Work 3. Method and 3.1. Hybrid Guidance Strategy 3.2. Handling Multiple Identities 3.3. Training 4. Experiments 4.1. Implementation details. 4.2. Results 5. Conclusion and References 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. 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. Authors: 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 Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Work 2. Related Work 3. Method and 3.1. Hybrid Guidance Strategy 3. Method and 3.1. Hybrid Guidance Strategy 3.2. Handling Multiple Identities 3.2. Handling Multiple Identities 3.3. Training 3.3. Training 4. Experiments 4.1. Implementation details. 4.1. Implementation details. 4.2. Results 4.2. Results 5. Conclusion and References 5. Conclusion and References 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. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv