AutoStory: Generating Diverse Storytelling Images with Minimal Effort: Conclusion and References

Table of Links

• Abstract and Introduction
• Related Work
• Our Method
• Experiments
• Conclusion and References
• Appendix

5 CONCLUSION

The main focus of our AutoStory is to create diverse story visualizations that meet specific user requirements with minimal human effort. By combining the capabilities of the LLMs and diffusion models, we managed to obtain text-aligned, identity-consistent, and high-quality story images. Furthermore, with our well-designed story visualization pipeline and the proposed character data generation module, our approach streamlines the generation process and reduces the burden on the user, effectively eliminating the need for users to perform labor-intensive data collection. Sufficient experiments demonstrate that our method outperforms existing approaches in terms of the quality of the generated stories and the preservation of the subject characteristics. Moreover, our superior results are achieved without requiring time-consuming and computationally expensive large-scale training, making it easy to generalize to varying characters, scenes, and styles. In future work, we plan to accelerate the multi-concept customization process and make our AutoStory run in real-time.

REFERENCES

Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Proc. Advances in neural information processing systems 33 (2020), 1877–1901.

Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, and Daniel Cohen-Or. 2023. Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models.

Hong Chen, Rujun Han, Te-Lin Wu, Hideki Nakayama, and Nanyun Peng. 2022. Character-centric story visualization via visual planning and token alignment. arXiv preprint arXiv:2210.08465 (2022).

Rohan Anil et al. 2023. PaLM 2 Technical Report. arXiv:2305.10403

Weixi Feng, Wanrong Zhu, Tsu-jui Fu, Varun Jampani, Arjun Akula, Xuehai He, Sugato Basu, Xin Eric Wang, and William Yang Wang. 2023. LayoutGPT: Compositional Visual Planning and Generation with Large Language Models. arXiv preprint arXiv:2305.15393 (2023).

Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. 2022. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618 (2022).

Yuan Gong, Youxin Pang, Xiaodong Cun, Menghan Xia, Haoxin Chen, Longyue Wang, Yong Zhang, Xintao Wang, Ying Shan, and Yujiu Yang. 2023. TaleCrafter: Interactive Story Visualization with Multiple Characters. arXiv preprint arXiv:2305.18247 (2023).

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David WardeFarley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020. Generative adversarial networks. Commun. ACM 63, 11 (2020), 139–144.

Yuchao Gu, Xintao Wang, Jay Zhangjie Wu, Yujun Shi, Yunpeng Chen, Zihan Fan, Wuyou Xiao, Rui Zhao, Shuning Chang, Weijia Wu, et al. 2023. Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models. arXiv preprint arXiv:2305.18292 (2023).

Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. LoRA: Low-Rank Adaptation of Large Language Models. In Proc. Int. Conf. Learning Representations.

Hyeonho Jeong, Gihyun Kwon, and Jong Chul Ye. 2023. Zero-shot Generation of Coherent Storybook from Plain Text Story using Diffusion Models. arXiv preprint arXiv:2302.03900 (2023).

Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. 2023. Segment anything. arXiv preprint arXiv:2304.02643 (2023).

Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, and Jun-Yan Zhu. 2022. Multi-Concept Customization of Text-to-Image Diffusion. arXiv preprint arXiv:2212.04488 (2022).

Bowen Li. 2022. Word-Level Fine-Grained Story Visualization. In Proc. Eur. Conf. Comp. Vis. Springer, 347–362.

Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David Carlson, and Jianfeng Gao. 2019. Storygan: A sequential conditional gan for story visualization. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn. 6329–6338.

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. arXiv preprint arXiv:2301.07093 (2023).

Long Lian, Boyi Li, Adam Yala, and Trevor Darrell. 2023. LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models. arXiv preprint arXiv:2305.13655 (2023).

Chang Liu, Haoning Wu, Yujie Zhong, Xiaoyun Zhang, and Weidi Xie. 2023c. Intelligent Grimm–Open-ended Visual Storytelling via Latent Diffusion Models. arXiv preprint arXiv:2306.00973 (2023).

Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, Zexiang Xu, Hao Su, et al. 2023d. One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization. arXiv preprint arXiv:2306.16928 (2023).

Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. 2023b. Zero-1-to-3: Zero-shot One Image to 3D Object.

Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, et al. 2023e. Grounding dino: Marrying dino with grounded pre-training for open-set object detection. arXiv preprint arXiv:2303.05499 (2023).

Zhiheng Liu, Ruili Feng, Kai Zhu, Yifei Zhang, Kecheng Zheng, Yu Liu, Deli Zhao, Jingren Zhou, and Yang Cao. 2023a. Cones: Concept neurons in diffusion models for customized generation. arXiv preprint arXiv:2303.05125 (2023).

Zhiheng Liu, Yifei Zhang, Yujun Shen, Kecheng Zheng, Kai Zhu, Ruili Feng, Yu Liu, Deli Zhao, Jingren Zhou, and Yang Cao. 2023f. Cones 2: Customizable Image Synthesis with Multiple Subjects. arXiv preprint arXiv:2305.19327 (2023).

Adyasha Maharana and Mohit Bansal. 2021. Integrating visuospatial, linguistic and commonsense structure into story visualization. arXiv preprint arXiv:2110.10834 (2021).

Adyasha Maharana, Darryl Hannan, and Mohit Bansal. 2021. Improving generation and evaluation of visual stories via semantic consistency. arXiv preprint arXiv:2105.10026 (2021).

Adyasha Maharana, Darryl Hannan, and Mohit Bansal. 2022. Storydall-e: Adapting pretrained text-to-image transformers for story continuation. In Proc. Eur. Conf. Comp. Vis. Springer, 70–87.

Chong Mou, Xintao Wang, Liangbin Xie, Jian Zhang, Zhongang Qi, Ying Shan, and Xiaohu Qie. 2023. T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. arXiv preprint arXiv:2302.08453 (2023).

OpenAI. 2023. GPT-4 Technical Report. arXiv:2303.08774

Xichen Pan, Pengda Qin, Yuhong Li, Hui Xue, and Wenhu Chen. 2022. Synthesizing Coherent Story with Auto-Regressive Latent Diffusion Models. arXiv preprint arXiv:2211.10950 (2022).

Quynh Phung, Songwei Ge, and Jia-Bin Huang. 2023. Grounded Text-to-Image Synthesis with Attention Refocusing. arXiv preprint arXiv:2306.05427 (2023).

Tanzila Rahman, Hsin-Ying Lee, Jian Ren, Sergey Tulyakov, Shweta Mahajan, and Leonid Sigal. 2022. Make-A-Story: Visual Memory Conditioned Consistent Story Generation. arXiv preprint arXiv:2211.13319 (2022).

Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 (2022).

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 Proc. Int. Conf. Mach. Learn. PMLR, 8821–8831.

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-resolution image synthesis with latent diffusion models. In Proc. IEEE Conf. Comp. Vis. Patt. Recogn. 10684–10695.

Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. 2022. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. arXiv preprint arXiv:2208.12242 (2022).

Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. 2022. Photorealistic text-to-image diffusion models with deep language understanding. Proc. Advances in Neural Information Processing Systems 35 (2022), 36479–36494.

Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. 2022. Laion-5b: An open large-scale dataset for training next generation image-text models. arXiv preprint arXiv:2210.08402 (2022).

Yun-Zhu Song, Zhi Rui Tam, Hung-Jen Chen, Huiao-Han Lu, and Hong-Han Shuai. 2020. Character-preserving coherent story visualization. In Proc. Eur. Conf. Comp. Vis. 18–33.

Zhuo Su, Wenzhe Liu, Zitong Yu, Dewen Hu, Qing Liao, Qi Tian, Matti Pietikäinen, and Li Liu. 2021. Pixel difference networks for efficient edge detection. In Proc. IEEE Int. Conf. Comp. Vis. 5117–5127.

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Proc. Advances in Neural Information Processing Systems 30 (2017).

Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, et al. 2020. Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43, 10 (2020), 3349–3364.

Wen Wang, kangyang Xie, Zide Liu, Hao Chen, Yue Cao, Xinlong Wang, and Chunhua Shen. 2023. Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models. arXiv preprint arXiv:2303.17599 (2023).

Jay Zhangjie Wu, Yixiao Ge, Xintao Wang, Weixian Lei, Yuchao Gu, Wynne Hsu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. 2022. Tune-a-video: One-shot tuning of image diffusion models for text-to-video generation. arXiv preprint arXiv:2212.11565 (2022).

Jinheng Xie, Yuexiang Li, Yawen Huang, Haozhe Liu, Wentian Zhang, Yefeng Zheng, and Mike Zheng Shou. 2023. BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained Diffusion. arXiv preprint arXiv:2307.10816 (2023).

Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, and Fang Wen. 2022. Paint by Example: Exemplar-based Image Editing with Diffusion Models. arXiv preprint arXiv:2211.13227 (2022).

Lvmin Zhang and Maneesh Agrawala. 2023. Adding conditional control to text-to-image diffusion models. arXiv preprint arXiv:2302.05543 (2023).