FaceStudio: Put Your Face Everywhere in Seconds: Conclusion and References

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 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. References [1] Stable diffusion models, embeddings, loras and more. 3 [2] Implementation of EulerAncestralDiscreteScheduler based on k-diffusion. 6 [3] Implementation of Hypernetwork. 3 [4] Yuval Alaluf, Elad Richardson, Gal Metzer, and Daniel Cohen-Or. A neural space-time representation for text-toimage personalization. arXiv preprint arXiv:2305.15391, 2023. 3 [5] Omri Avrahami, Kfir Aberman, Ohad Fried, Daniel CohenOr, and Dani Lischinski. Break-a-scene: Extracting multiple concepts from a single image. arXiv preprint arXiv:2305.16311, 2023. 3 [6] Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Universal guidance for diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 843–852, 2023. 3 [7] Mingdeng Cao, Xintao Wang, Zhongang Qi, Ying Shan, Xiaohu Qie, and Yinqiang Zheng. Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and editing. arXiv preprint arXiv:2304.08465, 2023. 3 [8] Hong Chen, Yipeng Zhang, Xin Wang, Xuguang Duan, Yuwei Zhou, and Wenwu Zhu. Disenbooth: Identitypreserving disentangled tuning for subject-driven text-toimage generation. arXiv preprint arXiv:2305.03374, 2023. 3 [9] Li Chen, Mengyi Zhao, Yiheng Liu, Mingxu Ding, Yangyang Song, Shizun Wang, Xu Wang, Hao Yang, Jing Liu, Kang Du, et al. Photoverse: Tuning-free image customization with text-to-image diffusion models. arXiv preprint arXiv:2309.05793, 2023. 3 [10] Xiaoyu Chen, Chi Zhang, Guosheng Lin, and Jing Han. Compositional prototype network with multi-view comparision for few-shot point cloud semantic segmentation. arXiv preprint arXiv:2012.14255, 2020. 4 [11] Xi Chen, Lianghua Huang, Yu Liu, Yujun Shen, Deli Zhao, and Hengshuang Zhao. Anydoor: Zero-shot object-level image customization. arXiv preprint arXiv:2307.09481, 2023. 3 [12] Jiankang Deng, J. Guo, and S. Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. Computer Vision and Pattern Recognition, 2018. 4, 6 [13] Ziyi Dong, Pengxu Wei, and Liang Lin. Dreamartist: Towards controllable one-shot text-to-image generation via contrastive prompt-tuning. arXiv preprint arXiv:2211.11337, 2022. 3 [14] Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel CohenOr. An image is worth one word: Personalizing text-toimage generation using textual inversion. arXiv preprint arXiv:2208.01618, 2022. 2, 3, 6, 8 [15] Rinon Gal, Moab Arar, Yuval Atzmon, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. Encoder-based domain tuning for fast personalization of text-to-image models. arXiv preprint arXiv:2302.12228, 2023. 3 [16] Jing Gu, Yilin Wang, Nanxuan Zhao, Tsu-Jui Fu, Wei Xiong, Qing Liu, Zhifei Zhang, He Zhang, Jianming Zhang, HyunJoon Jung, et al. Photoswap: Personalized subject swapping in images. arXiv preprint arXiv:2305.18286, 2023. 3 [17] Ligong Han, Song Wen, Qi Chen, Zhixing Zhang, Kunpeng Song, Mengwei Ren, Ruijiang Gao, Yuxiao Chen, Di Liu 0003, Qilong Zhangli, et al. Improving tuning-free real image editing with proximal guidance. CoRR, 2023. 3 [18] Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626, 2022. 3 [19] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022. 6 [20] Jonathan Ho, Ajay Jain, and P. Abbeel. Denoising diffusion probabilistic models. Neural Information Processing Systems, 2020. 5 [21] Lianghua Huang, Di Chen, Yu Liu, Yujun Shen, Deli Zhao, and Jingren Zhou. Composer: Creative and controllable image synthesis with composable conditions. arXiv preprint arXiv:2302.09778, 2023. 3 [22] Junha Hyung, Jaeyo Shin, and Jaegul Choo. Magicapture: High-resolution multi-concept portrait customization. arXiv preprint arXiv:2309.06895, 2023. 3 [23] Hyeonho Jeong, Gihyun Kwon, and Jong Chul Ye. Zero-shot generation of coherent storybook from plain text story using diffusion models. arXiv preprint arXiv:2302.03900, 2023. 3 [24] Xuhui Jia, Yang Zhao, Kelvin CK Chan, Yandong Li, Han Zhang, Boqing Gong, Tingbo Hou, Huisheng Wang, and Yu-Chuan Su. Taming encoder for zero fine-tuning image customization with text-to-image diffusion models. arXiv preprint arXiv:2304.02642, 2023. 3 [25] Tero Karras, S. Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. Computer Vision and Pattern Recognition, 2018. 2, 6 [26] Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani. Imagic: Text-based real image editing with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6007–6017, 2023. 3 [27] Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu. Multi-concept customization of text-to-image diffusion. 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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 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. References [1] Stable diffusion models, embeddings, loras and more. 3 [2] Implementation of EulerAncestralDiscreteScheduler based on k-diffusion. 6 [3] Implementation of Hypernetwork. 3 [4] Yuval Alaluf, Elad Richardson, Gal Metzer, and Daniel Cohen-Or. A neural space-time representation for text-toimage personalization. arXiv preprint arXiv:2305.15391, 2023. 3 [5] Omri Avrahami, Kfir Aberman, Ohad Fried, Daniel CohenOr, and Dani Lischinski. Break-a-scene: Extracting multiple concepts from a single image. arXiv preprint arXiv:2305.16311, 2023. 3 [6] Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Universal guidance for diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 843–852, 2023. 3 [7] Mingdeng Cao, Xintao Wang, Zhongang Qi, Ying Shan, Xiaohu Qie, and Yinqiang Zheng. Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and editing. arXiv preprint arXiv:2304.08465, 2023. 3 [8] Hong Chen, Yipeng Zhang, Xin Wang, Xuguang Duan, Yuwei Zhou, and Wenwu Zhu. Disenbooth: Identitypreserving disentangled tuning for subject-driven text-toimage generation. arXiv preprint arXiv:2305.03374, 2023. 3 [9] Li Chen, Mengyi Zhao, Yiheng Liu, Mingxu Ding, Yangyang Song, Shizun Wang, Xu Wang, Hao Yang, Jing Liu, Kang Du, et al. Photoverse: Tuning-free image customization with text-to-image diffusion models. arXiv preprint arXiv:2309.05793, 2023. 3 [10] Xiaoyu Chen, Chi Zhang, Guosheng Lin, and Jing Han. Compositional prototype network with multi-view comparision for few-shot point cloud semantic segmentation. arXiv preprint arXiv:2012.14255, 2020. 4 [11] Xi Chen, Lianghua Huang, Yu Liu, Yujun Shen, Deli Zhao, and Hengshuang Zhao. Anydoor: Zero-shot object-level image customization. arXiv preprint arXiv:2307.09481, 2023. 3 [12] Jiankang Deng, J. Guo, and S. Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. Computer Vision and Pattern Recognition, 2018. 4, 6 [13] Ziyi Dong, Pengxu Wei, and Liang Lin. Dreamartist: Towards controllable one-shot text-to-image generation via contrastive prompt-tuning. arXiv preprint arXiv:2211.11337, 2022. 3 [14] Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel CohenOr. An image is worth one word: Personalizing text-toimage generation using textual inversion. arXiv preprint arXiv:2208.01618, 2022. 2, 3, 6, 8 [15] Rinon Gal, Moab Arar, Yuval Atzmon, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. Encoder-based domain tuning for fast personalization of text-to-image models. arXiv preprint arXiv:2302.12228, 2023. 3 [16] Jing Gu, Yilin Wang, Nanxuan Zhao, Tsu-Jui Fu, Wei Xiong, Qing Liu, Zhifei Zhang, He Zhang, Jianming Zhang, HyunJoon Jung, et al. Photoswap: Personalized subject swapping in images. arXiv preprint arXiv:2305.18286, 2023. 3 [17] Ligong Han, Song Wen, Qi Chen, Zhixing Zhang, Kunpeng Song, Mengwei Ren, Ruijiang Gao, Yuxiao Chen, Di Liu 0003, Qilong Zhangli, et al. Improving tuning-free real image editing with proximal guidance. CoRR, 2023. 3 [18] Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control. arXiv preprint arXiv:2208.01626, 2022. 3 [19] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022. 6 [20] Jonathan Ho, Ajay Jain, and P. Abbeel. Denoising diffusion probabilistic models. Neural Information Processing Systems, 2020. 5 [21] Lianghua Huang, Di Chen, Yu Liu, Yujun Shen, Deli Zhao, and Jingren Zhou. Composer: Creative and controllable image synthesis with composable conditions. arXiv preprint arXiv:2302.09778, 2023. 3 [22] Junha Hyung, Jaeyo Shin, and Jaegul Choo. Magicapture: High-resolution multi-concept portrait customization. arXiv preprint arXiv:2309.06895, 2023. 3 [23] Hyeonho Jeong, Gihyun Kwon, and Jong Chul Ye. Zero-shot generation of coherent storybook from plain text story using diffusion models. arXiv preprint arXiv:2302.03900, 2023. 3 [24] Xuhui Jia, Yang Zhao, Kelvin CK Chan, Yandong Li, Han Zhang, Boqing Gong, Tingbo Hou, Huisheng Wang, and Yu-Chuan Su. Taming encoder for zero fine-tuning image customization with text-to-image diffusion models. arXiv preprint arXiv:2304.02642, 2023. 3 [25] Tero Karras, S. Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. Computer Vision and Pattern Recognition, 2018. 2, 6 [26] Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani. Imagic: Text-based real image editing with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6007–6017, 2023. 3 [27] Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu. Multi-concept customization of text-to-image diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1931–1941, 2023. 3 [28] Lvlong Lai, Jian Chen, Chi Zhang, Zehong Zhang, Guosheng Lin, and Qingyao Wu. Tackling background ambiguities in multi-class few-shot point cloud semantic segmentation. Knowledge-Based Systems, 253:109508, 2022. 4 [29] Lingzhi Li, Jianmin Bao, Hao Yang, Dong Chen, and Fang Wen. Faceshifter: Towards high fidelity and occlusion aware face swapping. arXiv preprint arXiv:1912.13457, 2019. 2 [30] Weide Liu, Chi Zhang, Guosheng Lin, and Fayao Liu. Crnet: Cross-reference networks for few-shot segmentation. In IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020. 4 [31] Weide Liu, Chi Zhang, Henghui Ding, Tzu-Yi Hung, and Guosheng Lin. Few-shot segmentation with optimal transport matching and message flow. IEEE Transactions on Multimedia (TMM), 2021. [32] Weide Liu, Chi Zhang, Guosheng Lin, and Fayao Liu. 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