This Open-Source Image Generator From Russian Scientists Uses A Novel Approach to Diffusion

Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is the final part of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Re-read the paper below. Table of Links Abstract and Introduction Related Work Demo System Kandinsky Architecture Experiments Results Conclusion & Limitations Ethical Considerations, Acknowledgements and References 9 Ethical Considerations We performed multiple efforts to ensure that the generated images do not contain harmful, offensive, or abusive content by (1) cleansing the training dataset from samples that were marked to be harmful/offensive/abusive, and (2) detecting abusive textual prompts. While obvious queries, according to our tests, almost never generate abusive content, technically it is not guaranteed that certain carefully engineered prompts may not yield undesirable content. We, therefore, recommend using an additional layer of classifiers, depending on the application, which would filter out the undesired content and/or use image/representation transformation methods tailored to a given application. Acknowledgements As usual, we would like to thank the anonymous reviewers for their useful comments. We would also like to thank Sergey Markov and his team for helpful feedback and discussions, for collaboration in multimodal dataset collecting, labelling and processing. References Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, Tero Karras, and Ming-Yu Liu. 2022. ediff-i: Text-toimage diffusion models with an ensemble of expert denoisers. Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, and Christian Etmann. 2021. Conditional image generation with score-based diffusion models. CoRR, abs/2111.13606. Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. 2023. Align your latents: Highresolution video synthesis with latent diffusion models. CoRR, abs/2304.08818. Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. 2023. Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation. CoRR, abs/2303.13873. Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 8440–8451. Association for Computational Linguistics. Prafulla Dhariwal and Alexander Quinn Nichol. 2021. Diffusion models beat gans on image synthesis. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6- 14, 2021, virtual, pages 8780–8794. Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, and Jie Tang. 2021. Cogview: Mastering text-to-image generation via transformers. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 19822–19835. Patrick Esser, Johnathan Chiu, Parmida Atighehchian, Jonathan Granskog, and Anastasis Germanidis. 2023. Structure and content-guided video synthesis with diffusion models. CoRR, abs/2302.03011. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, pages 2672– 2680. Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. 2023. Prompt-to-prompt image editing with cross-attention control. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net. Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey A. Gritsenko, Diederik P. Kingma, Ben Poole, Mohammad Norouzi, David J. Fleet, and Tim Salimans. 2022a. Imagen video: High definition video generation with diffusion models. CoRR, abs/2210.02303. Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. Jonathan Ho and Tim Salimans. 2022. Classifier-free diffusion guidance. volume abs/2207.12598. Jonathan Ho, Tim Salimans, Alexey A. Gritsenko, William Chan, Mohammad Norouzi, and David J. Fleet. 2022b. Video diffusion models. In NeurIPS. Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park. 2023. Scaling up gans for text-to-image synthesis. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023, pages 10124–10134. IEEE. Johanna Karras, Aleksander Holynski, Ting-Chun Wang, and Ira Kemelmacher-Shlizerman. 2023. Dreampose: Fashion image-to-video synthesis via stable diffusion. Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. 2023. GLIGEN: open-set grounded text-to-image generation. CoRR, abs/2301.07093. Jun Hao Liew, Hanshu Yan, Daquan Zhou, and Jiashi Feng. 2022. Magicmix: Semantic mixing with diffusion models. CoRR, abs/2210.16056. Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. 2022. Magic3d: High-resolution text-to-3d content creation. CoRR, abs/2211.10440. Shilin Lu, Yanzhu Liu, and Adams Wai-Kin Kong. 2023. TF-ICON: diffusion-based training-free crossdomain image composition. CoRR, abs/2307.12493. Zhengxiong Luo, Dayou Chen, Yingya Zhang, Yan Huang, Liang Wang, Yujun Shen, Deli Zhao, Jingren Zhou, and Tieniu Tan. 2023. Videofusion: Decomposed diffusion models for high-quality video generation. CoRR, abs/2303.08320. Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, and Jian Zhang. 2023. Dragondiffusion: Enabling drag-style manipulation on diffusion models. CoRR, abs/2307.02421. Alexander Quinn Nichol and Prafulla Dhariwal. 2021. Improved denoising diffusion probabilistic models. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 8162–8171. PMLR. Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. 2022. GLIDE: towards photorealistic image generation and editing with text-guided diffusion models. In International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, volume 162 of Proceedings of Machine Learning Research, pages 16784–16804. PMLR. Gaurav Parmar, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, and Jun-Yan Zhu. 2023. Zero-shot image-to-image translation. In ACM SIGGRAPH 2023 Conference Proceedings, SIGGRAPH 2023, Los Angeles, CA, USA, August 6-10, 2023, pages 11:1–11:11. ACM. William Peebles and Saining Xie. 2022. Scalable diffusion models with transformers. CoRR, abs/2212.09748. Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. 2023. Dreamfusion: Text-to-3d using 2d diffusion. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 8748–8763. PMLR. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21:140:1–140:67. Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022. Hierarchical textconditional image generation with CLIP latents. Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. Zero-shot text-to-image generation. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 8821–8831. PMLR. Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. Highresolution image synthesis with latent diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, pages 10674–10685. IEEE. Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. 2023. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023, pages 22500–22510. IEEE. Chitwan Saharia, William Chan, Huiwen Chang, Chris A. Lee, Jonathan Ho, Tim Salimans, David J. Fleet, and Mohammad Norouzi. 2022a. Palette: Image-to-image diffusion models. In SIGGRAPH ’22: Special Interest Group on Computer Graphics and Interactive Techniques Conference, Vancouver, BC, Canada, August 7 - 11, 2022, pages 15:1–15:10. ACM. Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L. Denton, Seyed Kamyar Seyed Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi. 2022b. Photorealistic text-to-image diffusion models with deep language understanding. Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev. 2022. LAION-5B: an open large-scale dataset for training next generation image-text models. In NeurIPS. Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, Devi Parikh, Sonal Gupta, and Yaniv Taigman. 2023. Make-a-video: Text-to-video generation without text-video data. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net. Junshu Tang, Tengfei Wang, Bo Zhang, Ting Zhang, Ran Yi, Lizhuang Ma, and Dong Chen. 2023. Makeit-3d: High-fidelity 3d creation from A single image with diffusion prior. CoRR, abs/2303.14184. Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021. mt5: A massively multilingual pre-trained text-to-text transformer. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, pages 483–498. Association for Computational Linguistics. Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason Baldridge, and Yonghui Wu. 2022. Scaling autoregressive models for content-rich textto-image generation. Trans. Mach. Learn. Res., 2022. Chuanxia Zheng, Tung-Long Vuong, Jianfei Cai, and Dinh Phung. 2022. Movq: Modulating quantized vectors for high-fidelity image generation. In NeurIPS. A. Additional generation examples This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Authors: Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is the final part of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Re-read the paper below. Editor's Note: This is the final part of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Re-read the paper below. Editor's Note: This is the final part of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Re-read the paper below. Table of Links Abstract and Introduction Related Work Demo System Kandinsky Architecture Experiments Results Conclusion & Limitations Ethical Considerations, Acknowledgements and References Abstract and Introduction Abstract and Introduction Related Work Related Work Demo System Demo System Kandinsky Architecture Kandinsky Architecture Experiments Experiments Results Results Conclusion & Limitations Conclusion & Limitations Ethical Considerations, Acknowledgements and References Ethical Considerations, Acknowledgements and References 9 Ethical Considerations We performed multiple efforts to ensure that the generated images do not contain harmful, offensive, or abusive content by (1) cleansing the training dataset from samples that were marked to be harmful/offensive/abusive, and (2) detecting abusive textual prompts. While obvious queries, according to our tests, almost never generate abusive content, technically it is not guaranteed that certain carefully engineered prompts may not yield undesirable content. We, therefore, recommend using an additional layer of classifiers, depending on the application, which would filter out the undesired content and/or use image/representation transformation methods tailored to a given application. Acknowledgements As usual, we would like to thank the anonymous reviewers for their useful comments. We would also like to thank Sergey Markov and his team for helpful feedback and discussions, for collaboration in multimodal dataset collecting, labelling and processing. References Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat, Jiaming Song, Karsten Kreis, Miika Aittala, Timo Aila, Samuli Laine, Bryan Catanzaro, Tero Karras, and Ming-Yu Liu. 2022. ediff-i: Text-toimage diffusion models with an ensemble of expert denoisers. Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, and Christian Etmann. 2021. Conditional image generation with score-based diffusion models. CoRR, abs/2111.13606. Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. 2023. Align your latents: Highresolution video synthesis with latent diffusion models. CoRR, abs/2304.08818. Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. 2023. Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation. CoRR, abs/2303.13873. Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020. Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 8440–8451. Association for Computational Linguistics. Prafulla Dhariwal and Alexander Quinn Nichol. 2021. Diffusion models beat gans on image synthesis. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6- 14, 2021, virtual, pages 8780–8794. Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, and Jie Tang. 2021. Cogview: Mastering text-to-image generation via transformers. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 19822–19835. Patrick Esser, Johnathan Chiu, Parmida Atighehchian, Jonathan Granskog, and Anastasis Germanidis. 2023. Structure and content-guided video synthesis with diffusion models. CoRR, abs/2302.03011. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, pages 2672– 2680. Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. 2023. Prompt-to-prompt image editing with cross-attention control. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net. Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey A. Gritsenko, Diederik P. Kingma, Ben Poole, Mohammad Norouzi, David J. Fleet, and Tim Salimans. 2022a. Imagen video: High definition video generation with diffusion models. CoRR, abs/2210.02303. Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. Jonathan Ho and Tim Salimans. 2022. Classifier-free diffusion guidance. volume abs/2207.12598. Jonathan Ho, Tim Salimans, Alexey A. Gritsenko, William Chan, Mohammad Norouzi, and David J. Fleet. 2022b. Video diffusion models. In NeurIPS. Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park. 2023. Scaling up gans for text-to-image synthesis. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023, pages 10124–10134. IEEE. Johanna Karras, Aleksander Holynski, Ting-Chun Wang, and Ira Kemelmacher-Shlizerman. 2023. Dreampose: Fashion image-to-video synthesis via stable diffusion. Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. 2023. GLIGEN: open-set grounded text-to-image generation. CoRR, abs/2301.07093. Jun Hao Liew, Hanshu Yan, Daquan Zhou, and Jiashi Feng. 2022. Magicmix: Semantic mixing with diffusion models. CoRR, abs/2210.16056. Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. 2022. Magic3d: High-resolution text-to-3d content creation. CoRR, abs/2211.10440. Shilin Lu, Yanzhu Liu, and Adams Wai-Kin Kong. 2023. TF-ICON: diffusion-based training-free crossdomain image composition. CoRR, abs/2307.12493. Zhengxiong Luo, Dayou Chen, Yingya Zhang, Yan Huang, Liang Wang, Yujun Shen, Deli Zhao, Jingren Zhou, and Tieniu Tan. 2023. Videofusion: Decomposed diffusion models for high-quality video generation. CoRR, abs/2303.08320. Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, and Jian Zhang. 2023. Dragondiffusion: Enabling drag-style manipulation on diffusion models. CoRR, abs/2307.02421. Alexander Quinn Nichol and Prafulla Dhariwal. 2021. Improved denoising diffusion probabilistic models. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 8162–8171. PMLR. Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. 2022. GLIDE: towards photorealistic image generation and editing with text-guided diffusion models. In International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA, volume 162 of Proceedings of Machine Learning Research, pages 16784–16804. PMLR. Gaurav Parmar, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, and Jun-Yan Zhu. 2023. Zero-shot image-to-image translation. In ACM SIGGRAPH 2023 Conference Proceedings, SIGGRAPH 2023, Los Angeles, CA, USA, August 6-10, 2023, pages 11:1–11:11. ACM. William Peebles and Saining Xie. 2022. Scalable diffusion models with transformers. CoRR, abs/2212.09748. Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. 2023. Dreamfusion: Text-to-3d using 2d diffusion. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 8748–8763. PMLR. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., 21:140:1–140:67. Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022. Hierarchical textconditional image generation with CLIP latents. Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. Zero-shot text-to-image generation. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, volume 139 of Proceedings of Machine Learning Research, pages 8821–8831. PMLR. Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. Highresolution image synthesis with latent diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, pages 10674–10685. IEEE. Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. 2023. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023, pages 22500–22510. IEEE. Chitwan Saharia, William Chan, Huiwen Chang, Chris A. Lee, Jonathan Ho, Tim Salimans, David J. Fleet, and Mohammad Norouzi. 2022a. Palette: Image-to-image diffusion models. In SIGGRAPH ’22: Special Interest Group on Computer Graphics and Interactive Techniques Conference, Vancouver, BC, Canada, August 7 - 11, 2022, pages 15:1–15:10. ACM. Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L. Denton, Seyed Kamyar Seyed Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi. 2022b. Photorealistic text-to-image diffusion models with deep language understanding. Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev. 2022. LAION-5B: an open large-scale dataset for training next generation image-text models. In NeurIPS. Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, Devi Parikh, Sonal Gupta, and Yaniv Taigman. 2023. Make-a-video: Text-to-video generation without text-video data. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net. Junshu Tang, Tengfei Wang, Bo Zhang, Ting Zhang, Ran Yi, Lizhuang Ma, and Dong Chen. 2023. Makeit-3d: High-fidelity 3d creation from A single image with diffusion prior. CoRR, abs/2303.14184. Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021. mt5: A massively multilingual pre-trained text-to-text transformer. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2021, Online, June 6-11, 2021, pages 483–498. Association for Computational Linguistics. Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason Baldridge, and Yonghui Wu. 2022. Scaling autoregressive models for content-rich textto-image generation. Trans. Mach. Learn. Res., 2022. Chuanxia Zheng, Tung-Long Vuong, Jianfei Cai, and Dinh Phung. 2022. Movq: Modulating quantized vectors for high-fidelity image generation. In NeurIPS. A. Additional generation examples This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv