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Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity.
Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 1907
Earlier work this paper cites.
Analyzing and improving the image quality of stylegan
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T · 1912
Earlier work this paper cites.
Improved techniques for training score-based generative models
Song, Y. and Ermon, S · 2006
Earlier work this paper cites.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2010
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2011
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 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
Earlier work this paper cites.
Micikevicius, P., Narang, S., Alben, J., Diamos, G., Elsen, E., Garcia, D., Ginsburg, B., Houston, M., Kuchaiev, O., Venkatesh, G., and Wu, H · 2017
Earlier work this paper cites.
Neural discrete representation learning
van den Oord, A., Vinyals, O., and Kavukcuoglu, K · 2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Earlier work this paper cites.
Attngan: Fine-grained text to image generation with attentional generative adversarial networks
Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., and He, X · 2017
Earlier work this paper cites.
Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
Earlier work this paper cites.
Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J. and Gebru, T · 2018
Earlier work this paper cites.
Improved precision and recall metric for assessing generative models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T · 2019
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Generating diverse high-fidelity images with VQ-VAE-2
Razavi, A., van den Oord, A., and Vinyals, O · 2019
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Image synthesis with a single (robust) classifier
Santurkar, S., Tsipras, D., Tran, B., Ilyas, A., Engstrom, L., and Madry, A · 2019
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Hype: A benchmark for human eye perceptual evaluation of generative models, 2019
Zhou, S., Gordon, M. L., Krishna, R., Narcomey, A., Fei-Fei, L., and Bernstein, M. S · 2019
Cited alongside, same era.
Dm-gan: Dynamic memory generative adversarial networks for text-to-image synthesis
Generating images from caption and vice versa via clip-guided generative latent space search
Galatolo, F. A., Cimino, M. G. C. A., and Vaglini, G · 2021
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Vector quantized diffusion model for text-to-image synthesis
Gu, S., Chen, D., Bao, J., Wen, F., Zhang, B., Chen, D., Yuan, L., and Guo, B · 2021
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Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2021
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Cascaded diffusion models for high fidelity image generation
Ho, J., Saharia, C., Chan, W., Fleet, D. J., Norouzi, M., and Salimans, T · 2021
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Diffusionclip: Text-guided image manipulation using diffusion models
Kim, G. and Ye, J. C · 2021
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Zhu, M., Pan, P., Chen, W., and Yang, Y · 2019
Cited alongside, same era.
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., Uszkoreit, J., and Houlsby, N · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Conditional image generation and manipulation for user-specified content
Stap, D., Bleeker, M., Ibrahimi, S., and ter Hoeve, M · 2020
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Df-gan: Deep fusion generative adversarial networks for text-to-image synthesis
Tao, M., Tang, H., Wu, S., Sebe, N., Jing, X.-Y., Wu, F., and Bao, B · 2020
Cited alongside, same era.
Text-guided neural image inpainting
Zhang, L., Chen, Q., Hu, B., and Jiang, S · 2020
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Blended diffusion for text-driven editing of natural images
Avrahami, O., Lischinski, D., and Fried, O · 2021
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Bau, D., Andonian, A., Cui, A., Park, Y., Jahanian, A., Oliva, A., and Torralba, A · 2021
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Sdedit: Image synthesis and editing with stochastic differential equations
Meng, C., Song, Y., Song, J., Wu, J., Zhu, J.-Y., and Ermon, S · 2021
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The big sleep
Murdock, R · 2021
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Improved denoising diffusion probabilistic models
Nichol, A. and Dhariwal, P · 2021
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Styleclip: Text-driven manipulation of stylegan imagery
Patashnik, O., Wu, Z., Shechtman, E., Cohen-Or, D., and Lischinski, D · 2021
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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., Krueger, G., and Sutskever, I · 2021
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Zero-shot text-to-image generation
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., and Sutskever, I · 2021
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Improving text-to-image synthesis using contrastive learning
Ye, H., Yang, X., Takac, M., Sunderraman, R., and Ji, S · 2021
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Cross-modal contrastive learning for text-to-image generation
Zhang, H., Koh, J. Y., Baldridge, J., Lee, H., and Yang, Y · 2021
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Lafite: Towards language-free training for text-to-image generation
Zhou, Y., Zhang, R., Chen, C., Li, C., Tensmeyer, C., Yu, T., Gu, J., Xu, J., and Sun, T · 2021
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