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Recent advances in diffusion models can generate high-quality and stunning images from text.
2014
Earlier work this paper cites.
Gupta, T., Schwenk, D., Farhadi, A., Hoiem, D., Kembhavi, A.: Imagine this! scripts to compositions to videos. In: Proceedings of the European conference on computer vision (ECCV). pp. 598–613 (2018)
2018
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El-Nouby, A., Sharma, S., Schulz, H., Hjelm, D., Asri, L.E., Kahou, S.E., Bengio, Y., Taylor, G.W.: Tell, draw, and repeat: Generating and modifying images based on continual linguistic instruction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10304–10312 (2019)
2019
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Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4401–4410 (2019)
2019
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Li, Y., Gan, Z., Shen, Y., Liu, J., Cheng, Y., Wu, Y., Carin, L., Carlson, D., Gao, J.: Storygan: A sequential conditional gan for story visualization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6329–6338 (2019)
2019
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2020
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Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Communications of the ACM pp. 139–144 (2020)
2020
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
2020
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Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 8110–8119 (2020)
2020
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Lu, Y., Huang, B.: Structured output learning with conditional generative flows. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 5005–5012 (2020)
2020
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He, S., Liao, W., Yang, M.Y., Yang, Y., Song, Y.Z., Rosenhahn, B., Xiang, T.: Context-aware layout to image generation with enhanced object appearance. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 15049–15058 (2021)
2021
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Jia, C., Yang, Y., Xia, Y., Chen, Y.T., Parekh, Z., Pham, H., Le, Q., Sung, Y.H., Li, Z., Duerig, T.: Scaling up visual and vision-language representation learning with noisy text supervision. In: International conference on machine learning. pp. 4904–4916 (2021)
2021
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Li, J., Selvaraju, R., Gotmare, A., Joty, S., Xiong, C., Hoi, S.C.H.: Align before fuse: Vision and language representation learning with momentum distillation. Advances in neural information processing systems 34
2021
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Matsumori, S., Abe, Y., Shingyouchi, K., Sugiura, K., Imai, M.: Lattegan: Visually guided language attention for multi-turn text-conditioned image manipulation. IEEE Access 9
2021
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763 (2021)
2021
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Gafni, O., Polyak, A., Ashual, O., Sheynin, S., Parikh, D., Taigman, Y.: Make-a-scene: Scene-based text-to-image generation with human priors. In: European Conference on Computer Vision (2022)
2022
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Li, J., Li, D., Xiong, C., Hoi, S.: Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In: International Conference on Machine Learning. pp. 12888–12900. PMLR (2022)
2022
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2022
Cited alongside, same era.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10684–10695 (2022)
2022
Cited alongside, same era.
Tibebu, H., Malik, A., De Silva, V.: Text to image synthesis using stacked conditional variational autoencoders and conditional generative adversarial networks. In: Science and Information Conference. pp. 560–580 (2022)
2022
Cited alongside, same era.
Betker, J., Goh, G., Jing, L., Brooks, T., Wang, J., Li, L., Ouyang, L., Zhuang, J., Lee, J., Guo, Y., et al.: Improving image generation with better captions. Computer Science. https://cdn. openai. com/papers/dall-e-3. pdf 2
2023
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2023
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2023
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2023
Later among the works it cites.
2023
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2023
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2023
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2023
Cited alongside, same era.
Kang, M., Zhu, J.Y., Zhang, R., Park, J., Shechtman, E., Paris, S., Park, T.: Scaling up gans for text-to-image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10124–10134 (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3836–3847 (2023)
2023
Later among the works it cites.
Zheng, G., Zhou, X., Li, X., Qi, Z., Shan, Y., Li, X.: Layoutdiffusion: Controllable diffusion model for layout-to-image generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22490–22499 (2023)
2023
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2023
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2024
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2024
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Koh, J.Y., Fried, D., Salakhutdinov, R.R.: Generating images with multimodal language models. Advances in Neural Information Processing Systems 36
2024
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2024
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2024
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Wang, C.: A diffusion-based method for multi-turn compositional image generation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 381–391 (2024)
2024
Closest in time.
Wang, C.: A diffusion-based method for multi-turn compositional image generation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops. pp. 381–391 (January 2024)
2024
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Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T., Cao, Y., Narasimhan, K.: Tree of thoughts: Deliberate problem solving with large language models. Advances in Neural Information Processing Systems 36
2024
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2025
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