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The Diffusion Transformers Models (DiTs) have transitioned the network architecture from traditional UNets to transformers, demonstrating exceptional capabilities in image generation.
And the bit goes down: Revisiting the quantization of neural networks
Stock, P.; Joulin, A.; Gribonval, R.; Graham, B.; and Jégou, H. 2019 · 1907
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Vector quantization and signal compression , volume 159
Gersho, A.; and Gray, R. M. 2012 · 2012
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Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2014 · 2014
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Han, S.; Mao, H.; and Dally, W. J. 2015 · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O.; Deng, J.; Su, H.; Krause, J.; Satheesh, S.; Ma, S.; Huang, Z.; Karpathy, A.; Khosla, A.; Bernstein, M.; et al. 2015 · 2015
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Ba, J. L.; Kiros, J. R.; and Hinton, G. E. 2016 · 2016
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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Improved techniques for training gans
Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A.; and Chen, X. 2016 · 2016
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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
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
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Barratt, S.; and Sharma, R. 2018 · 2018
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Film: Visual reasoning with a general conditioning layer
Perez, E.; Strub, F.; De Vries, H.; Dumoulin, V.; and Courville, A. 2018 · 2018
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End-to-end object detection with transformers
Carion, N.; Massa, F.; Synnaeve, G.; Usunier, N.; Kirillov, A.; and Zagoruyko, S. 2020 · 2020
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Denoising diffusion probabilistic models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P.; and Nichol, A. 2021 · 2021
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.; et al. 2021 · 2021
Cited alongside, same era.
Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z.; Lin, Y.; Cao, Y.; Hu, H.; Wei, Y.; Zhang, Z.; Lin, S.; and Guo, B. 2021 · 2021
Cited alongside, same era.
Permute, quantize, and fine-tune: Efficient compression of neural networks
Martinez, J.; Shewakramani, J.; Liu, T. W.; Bârsan, I. A.; Zeng, W.; and Urtasun, R. 2021 · 2021
Cited alongside, same era.
Ptq4vit: Post-training quantization for vision transformers with twin uniform quantization
Yuan, Z.; Xue, C.; Chen, Y.; Wu, Q.; and Sun, G. 2022 · 2022
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Diffusion models in vision: A survey
Croitoru, F.-A.; Hondru, V.; Ionescu, R. T.; and Shah, M. 2023 · 2023
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AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Lin, J.; Tang, J.; Tang, H.; Yang, S.; Dang, X.; and Han, S. 2023 · 2023
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Scalable diffusion models with transformers
Peebles, W.; and Xie, S. 2023 · 2023
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Diffusion models: A comprehensive survey of methods and applications
Yang, L.; Zhang, Z.; Song, Y.; Hong, S.; Xu, R.; Zhao, Y.; Zhang, W.; Cui, B.; and Yang, M.-H. 2023 · 2023
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Nash, C.; Menick, J.; Dieleman, S.; and Battaglia, P. W. 2021 · 2021
Cited alongside, same era.
Training data-efficient image transformers and distillation through attention
Touvron, H.; Cord, M.; Douze, M.; Massa, F.; Sablayrolles, A.; and Jégou, H. 2021 · 2021
Cited alongside, same era.
SegFormer: Simple and efficient design for semantic segmentation with transformers
Xie, E.; Wang, W.; Yu, Z.; Anandkumar, A.; Alvarez, J. M.; and Luo, P. 2021 · 2021
Cited alongside, same era.
Gptq: Accurate post-training quantization for generative pre-trained transformers
Frantar, E.; Ashkboos, S.; Hoefler, T.; and Alistarh, D. 2022 · 2022
Cited alongside, same era.
Cascaded diffusion models for high fidelity image generation
Ho, J.; Saharia, C.; Chan, W.; Fleet, D. J.; Norouzi, M.; and Salimans, T. 2022 · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents, 2022
Ramesh, A.; Dhariwal, P.; Nichol, A.; Chu, C.; and Chen, M. 2022 · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2022 · 2022
Cited alongside, same era.
Brooks, T.; Peebles, B.; Holmes, C.; DePue, W.; Guo, Y.; Jing, L.; Schnurr, D.; Taylor, J.; Luhman, T.; Luhman, E.; Ng, C.; Wang, R.; and Ramesh, A. 2024 · 2024
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Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers
Chen, L.; Meng, Y.; Tang, C.; Ma, X.; Jiang, J.; Wang, X.; Wang, Z.; and Zhu, W. 2024 · 2024
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Ptqd: Accurate post-training quantization for diffusion models
He, Y.; Liu, L.; Liu, J.; Wu, W.; Zhou, H.; and Zhuang, B. 2024 · 2024
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Snapfusion: Text-to-image diffusion model on mobile devices within two seconds
Li, Y.; Wang, H.; Jin, Q.; Hu, J.; Chemerys, P.; Fu, Y.; Wang, Y.; Tulyakov, S.; and Ren, J. 2024 · 2024
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Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
Liu, Y.; Zhang, K.; Li, Y.; Yan, Z.; Gao, C.; Chen, R.; Yuan, Z.; Huang, Y.; Sun, H.; Gao, J.; et al. 2024 · 2024
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QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning
Wang, H.; Shang, Y.; Yuan, Z.; Wu, J.; and Yan, Y. 2024 · 2024
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Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyond
Zhu, Z.; Wang, X.; Zhao, W.; Min, C.; Deng, N.; Dou, M.; Wang, Y.; Shi, B.; Wang, K.; Zhang, C.; et al. 2024 · 2024
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