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Recent text-to-image generation methods provide a simple yet exciting conversion capability between text and image domains.
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Inferring semantic layout for hierarchical text-to-image synthesis
Seunghoon Hong, Dingdong Yang, Jongwook Choi, and Honglak Lee · 2018
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Diffusion models beat gans on image synthesis
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Cogview: Mastering text-to-image generation via transformers
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Zero-shot text-to-image generation (ICML spotlight)
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Regularizing generative adversarial networks under limited data
Hung-Yu Tseng, Lu Jiang, Ce Liu, Ming-Hsuan Yang, and Weilong Yang · 2021
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Improving text-to-image synthesis using contrastive learning
Hui Ye, Xiulong Yang, Martin Takac, Rajshekhar Sunderraman, and Shihao Ji · 2021
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Cross-modal contrastive learning for text-to-image generation
Han Zhang, Jing Yu Koh, Jason Baldridge, Honglak Lee, and Yinfei Yang · 2021
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M6-ufc: Unifying multi-modal controls for conditional image synthesis
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Lafite: Towards language-free training for text-to-image generation
Yufan Zhou, Ruiyi Zhang, Changyou Chen, Chunyuan Li, Chris Tensmeyer, Tong Yu, Jiuxiang Gu, Jinhui Xu, and Tong Sun · 2021
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Cascaded diffusion models for high fidelity image generation
Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans · 2022
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