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Recent advancements in the domain of text-to-image synthesis have culminated in a multitude of enhancements pertaining to quality, fidelity, and diversity.
Microsoft COCO captions: Data collection and evaluation server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollár, P., and Zitnick, C. L. (2015) · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T. (2015) · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Earlier work this paper cites.
Generative adversarial text to image synthesis
Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., and Lee, H. (2016) · 2016
Earlier work this paper cites.
GANs trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017) · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017) · 2017
Earlier work this paper cites.
Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., and Metaxas, D. N. (2017) · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F. (2019) · 2019
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020) · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P. J., et al. (2020) · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A. (2021) · 2021
Cited alongside, same era.
Cogview: Mastering text-to-image generation via transformers
Ding, M., Yang, Z., Hong, W., Zheng, W., Zhou, C., Yin, D., Lin, J., Zou, X., Shao, Z., Yang, H., et al. (2021) · 2021
Cited alongside, same era.
Taming transformers for high-resolution image synthesis
Esser, P., Rombach, R., and Ommer, B. (2021) · 2021
Cited alongside, same era.
OpenCLIP
Ilharco, G., Wortsman, M., Carlini, N., Taori, R., Dave, A., Shankar, V., Namkoong, H., Miller, J., Hajishirzi, H., Farhadi, A., and Schmidt, L. (2021) · 2021
Cited alongside, same era.
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Make-a-scene: Scene-based text-to-image generation with human priors
Gafni, O., Polyak, A., Ashual, O., Sheynin, S., Parikh, D., and Taigman, Y. (2022) · 2022
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Classifier-free diffusion guidance
Ho, J. and Salimans, T. (2022) · 2022
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Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S. (2022) · 2022
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Hierarchical text-conditional image generation with CLIP latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M. (2022) · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022) · 2022
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Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., and Chen, M. (2021) · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al. (2021) · 2021
Cited alongside, same era.
Zero-shot text-to-image generation
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., and Sutskever, I. (2021) · 2021
Cited alongside, same era.
MaskGIT: Masked generative image transformer
Chang, H., Zhang, H., Jiang, L., Liu, C., and Freeman, W. T. (2022) · 2022
Cited alongside, same era.
Cogview2: Faster and better text-to-image generation via hierarchical transformers
Ding, M., Zheng, W., Hong, W., and Tang, J. (2022) · 2022
Cited alongside, same era.
Pseudo numerical methods for diffusion models on manifolds
Liu, L., Ren, Y., Lin, Z., and Zhao, Z. (2022a)
Cited in the paper.
Character-aware models improve visual text rendering
Liu, R., Garrette, D., Saharia, C., Chan, W., Roberts, A., Narang, S., Blok, I., Mical, R., Norouzi, M., and Constant, N. (2022b)
Cited in the paper.
Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., et al. (2022) · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al. (2022) · 2022
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Byt5: Towards a token-free future with pre-trained byte-to-byte models
Xue, L., Barua, A., Constant, N., Al-Rfou, R., Narang, S., Kale, M., Roberts, A., and Raffel, C. (2022) · 2022
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Scaling autoregressive models for content-rich text-to-image generation
Yu, J., Xu, Y., Koh, J. Y., Luong, T., Baid, G., Wang, Z., Vasudevan, V., Ku, A., Yang, Y., Ayan, B. K., Hutchinson, B., Han, W., Parekh, Z., Li, X., Zhang, H., Baldridge, J., and Wu, Y. (2022) · 2022
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Muse: Text-to-image generation via masked generative transformers
Chang, H. et al. (2023) · 2023
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