Built on

Anderson, B.D.: Reverse-time diffusion equation models. Stochastic Processes and their Applications 12
1982
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1994
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Hyvärinen, A.: Estimation of non-normalized statistical models by score matching. JMLR 6
2005
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Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
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Vincent, P.: A connection between score matching and denoising autoencoders. Neural computation 23
2011
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Kingma, D.P., Welling, M.: Auto-Encoding variational bayes. arXiv preprint arXiv:1312.6114v10 (December 2013)
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2013
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Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems. pp. 2672–2680 (2014)
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Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. arXiv preprint arXiv:1505.04597 (May 2015)
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Sohl-Dickstein, J., Weiss, E.A., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. arXiv preprint arXiv:1503.03585 (March 2015)
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Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (July 2016)
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Brock, A., Donahue, J., Simonyan, K.: Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018)
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Elfwing, S., Uchibe, E., Doya, K.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Networks 107
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Perez, E., Strub, F., De Vries, H., Dumoulin, V., Courville, A.: Film: Visual reasoning with a general conditioning layer. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 32 (2018)
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Shaw, P., Uszkoreit, J., Vaswani, A.: Self-attention with relative position representations. arXiv preprint arXiv:1803.02155 (2018)
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Wu, Y., He, K.: Group normalization. arXiv preprint arXiv:1803.08494 (March 2018)
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Gong, X., Chang, S., Jiang, Y., Wang, Z.: Autogan: Neural architecture search for generative adversarial networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3224–3234 (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)
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Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. arXiv preprint arXiv:1907.05600 (July 2019)
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Chen, M., Radford, A., Child, R., Wu, J., Jun, H., Luan, D., Sutskever, I.: Generative pretraining from pixels. In: International Conference on Machine Learning. pp. 1691–1703. PMLR (2020)
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (2020)
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Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. arXiv preprint arXiv:2006.11239 (June 2020)
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Kong, Z., Ping, W., Huang, J., Zhao, K., Catanzaro, B.: Diffwave: A versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761 (2020)
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Vahdat, A., Kautz, J.: Nvae: A deep hierarchical variational autoencoder. Advances in neural information processing systems 33
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Wang, S., Li, B., Khabsa, M., Fang, H., Ma, H.: Linformer: Self-attention with linear complexity. arXiv preprint arXiv:2006.04768 (2020)
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2020
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Aneja, J., Schwing, A., Kautz, J., Vahdat, A.: A contrastive learning approach for training variational autoencoder priors. Advances in neural information processing systems 34
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Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems 34
2021
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Ding, M., Yang, Z., Hong, W., Zheng, W., Zhou, C., Yin, D., Lin, J., Zou, X., Shao, Z., Yang, H., et al.: Cogview: Mastering text-to-image generation via transformers. Advances in Neural Information Processing Systems 34
2021
Earlier work this paper cites.

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2022
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2023

DiffiT: Diffusion Vision Transformers for Image Generation

Hatamizadeh, Ali, Song, Jiaming, Liu, Guilin et al.

Understand

Diffusion models with their powerful expressivity and high sample quality have achieved State-Of-The-Art (SOTA) performance in the generative domain.

The pioneering Vision Transformer (ViT) has also demonstrated strong modeling capabilities and scalability, especially for recognition tasks.
In this paper, we study the effectiveness of ViTs in diffusion-based generative learning and propose a new model denoted as Diffusion Vision Transformers (DiffiT).
Specifically, we propose a methodology for finegrained control of the denoising process and introduce the Time-dependant Multihead Self Attention (TMSA) mechanism.

Built on

Anderson, B.D.: Reverse-time diffusion equation models. Stochastic Processes and their Applications 12
1982
Earlier work this paper cites.
Grenander, U., Miller, M.I.: Representations of knowledge in complex systems. Journal of the Royal Statistical Society: Series B (Methodological) 56
1994
Earlier work this paper cites.
Hyvärinen, A.: Estimation of non-normalized statistical models by score matching. JMLR 6
2005
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Vincent, P.: A connection between score matching and denoising autoencoders. Neural computation 23
2011
Earlier work this paper cites.
Kingma, D.P., Welling, M.: Auto-Encoding variational bayes. arXiv preprint arXiv:1312.6114v10 (December 2013)
Original
2013
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems. pp. 2672–2680 (2014)
2014
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (December 2014)
Original
2014
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-Net: Convolutional networks for biomedical image segmentation. arXiv preprint arXiv:1505.04597 (May 2015)
Original
2015
Earlier work this paper cites.
Sohl-Dickstein, J., Weiss, E.A., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. arXiv preprint arXiv:1503.03585 (March 2015)
Original
2015
Earlier work this paper cites.
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (July 2016)
Original
2016
Earlier work this paper cites.
Brock, A., Donahue, J., Simonyan, K.: Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018)
Original
2018
Earlier work this paper cites.
Elfwing, S., Uchibe, E., Doya, K.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Networks 107
2018
Earlier work this paper cites.
Perez, E., Strub, F., De Vries, H., Dumoulin, V., Courville, A.: Film: Visual reasoning with a general conditioning layer. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 32 (2018)
2018
Earlier work this paper cites.
Shaw, P., Uszkoreit, J., Vaswani, A.: Self-attention with relative position representations. arXiv preprint arXiv:1803.02155 (2018)
Original
2018
Earlier work this paper cites.
Wu, Y., He, K.: Group normalization. arXiv preprint arXiv:1803.08494 (March 2018)
Original
2018
Earlier work this paper cites.
Gong, X., Chang, S., Jiang, Y., Wang, Z.: Autogan: Neural architecture search for generative adversarial networks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3224–3234 (2019)
2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. arXiv preprint arXiv:1907.05600 (July 2019)
Original
2019
Earlier work this paper cites.
Chen, M., Radford, A., Child, R., Wu, J., Jun, H., Luan, D., Sutskever, I.: Generative pretraining from pixels. In: International Conference on Machine Learning. pp. 1691–1703. PMLR (2020)
2020
Earlier work this paper cites.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (2020)
2020
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. arXiv preprint arXiv:2006.11239 (June 2020)
Original
2020
Earlier work this paper cites.
Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Kong, Z., Ping, W., Huang, J., Zhao, K., Catanzaro, B.: Diffwave: A versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761 (2020)
Original
2020
Earlier work this paper cites.
Vahdat, A., Kautz, J.: Nvae: A deep hierarchical variational autoencoder. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Wang, S., Li, B., Khabsa, M., Fang, H., Ma, H.: Linformer: Self-attention with linear complexity. arXiv preprint arXiv:2006.04768 (2020)
Original
2020
Earlier work this paper cites.
Aneja, J., Schwing, A., Kautz, J., Vahdat, A.: A contrastive learning approach for training variational autoencoder priors. Advances in neural information processing systems 34
2021
Earlier work this paper cites.
Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems 34
2021
Earlier work this paper cites.
Ding, M., Yang, Z., Hong, W., Zheng, W., Zhou, C., Yin, D., Lin, J., Zou, X., Shao, Z., Yang, H., et al.: Cogview: Mastering text-to-image generation via transformers. Advances in Neural Information Processing Systems 34
2021
Earlier work this paper cites.

Similar

Dockhorn, T., Vahdat, A., Kreis, K.: Score-Based generative modeling with Critically-Damped langevin diffusion. arXiv preprint arXiv:2112.07068 (December 2021)
Original
2021
Cited alongside, same era.
Hudson, D.A., Zitnick, L.: Generative adversarial transformers. In: International conference on machine learning. pp. 4487–4499. PMLR (2021)
2021
Cited alongside, same era.
Jiang, Y., Chang, S., Wang, Z.: Transgan: Two transformers can make one strong gan. arXiv preprint arXiv:2102.07074 (2021)
Original
2021
Cited alongside, same era.
Lee, K., Chang, H., Jiang, L., Zhang, H., Tu, Z., Liu, C.: Vitgan: Training gans with vision transformers. arXiv preprint arXiv:2107.04589 (2021)
Original
2021
Cited alongside, same era.
Li, S., Chen, X., He, D., Hsieh, C.J.: Can vision transformers perform convolution? arXiv preprint arXiv:2111.01353 (2021)

Then

Kawar, B., Song, J., Ermon, S., Elad, M.: Jpeg artifact correction using denoising diffusion restoration models. arXiv preprint arXiv:2209.11888 (2022)
Original
2022
Later among the works it cites.
Kawar, B., Zada, S., Lang, O., Tov, O., Chang, H., Dekel, T., Mosseri, I., Irani, M.: Imagic: Text-based real image editing with diffusion models. arXiv preprint arXiv:2210.09276 (2022)
Original
2022
Later among the works it cites.
Kim, D., Na, B., Kwon, S.J., Lee, D., Kang, W., Moon, I.C.: Maximum likelihood training of implicit nonlinear diffusion models. arXiv preprint arXiv:2205.13699 (2022)
Original
2022
Later among the works it cites.
Kreis, K., Gao, R., Vahdat, A.: CVPR tutorial on denoising diffusion-based generative modeling: Foundations and applications. https://cvpr2022-tutorial-diffusion-models.github.io/ (July 2022)
2022
Later among the works it cites.

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Original

2021

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Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.Y., Ermon, S.: Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073 (2021)

Original

2021

Cited alongside, same era.

Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning. pp. 8162–8171. PMLR (2021)

2021

Cited alongside, same era.

Sinha, A., Song, J., Meng, C., Ermon, S.: D2c: Diffusion-decoding models for few-shot conditional generation. Advances in Neural Information Processing Systems 34

2021

Cited alongside, same era.

Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: International Conference on Learning Representations (2021)

2021

Cited alongside, same era.

Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. In: International Conference on Learning Representations (2021)

2021

Cited alongside, same era.

Tashiro, Y., Song, J., Song, Y., Ermon, S.: Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems 34

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Cited alongside, same era.

Vahdat, A., Kreis, K., Kautz, J.: Score-based generative modeling in latent space. arXiv preprint arXiv:2106.05931 (June 2021)

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Cited alongside, same era.

Lee, D., Kim, C., Kim, S., Cho, M., Han, W.S.: Autoregressive image generation using residual quantization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11523–11532 (2022)

2022