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Diffusion Transformers (DiTs) have recently gained substantial attention in both industrial and academic fields for their superior visual generation capabilities, outperforming traditional diffusion models that use U-Net.
Deep unsupervised learning using nonequilibrium thermodynamics, 2015
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Up or down? adaptive rounding for post-training quantization
Markus Nagel, Rana Ali Amjad, Mart Van Baalen, Christos Louizos, and Tijmen Blankevoort · 2020
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Term revealing: Furthering quantization at run time on quantized dnns. corr abs/2007.06389 (2020)
HT Kung, Bradley McDanel, and Sai Qian Zhang · 2020
Earlier work this paper cites.
Term quantization: Furthering quantization at run time
Hsiang-Tsung Kung, Bradley McDanel, and Sai Qian Zhang · 2020
Earlier work this paper cites.
Algorithm-hardware co-design of adaptive floating-point encodings for resilient deep learning inference
Thierry Tambe, En-Yu Yang, Zishen Wan, Yuntian Deng, Vijay Janapa Reddi, Alexander Rush, David Brooks, and Gu-Yeon Wei · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffusion models beat gans on image synthesis, 2021
Prafulla Dhariwal and Alex Nichol · 2021
Earlier work this paper cites.
{BRECQ}: Pushing the limit of post-training quantization by block reconstruction
Yuhang Li, Ruihao Gong, Xu Tan, Yang Yang, Peng Hu, Qi Zhang, Fengwei Yu, Wei Wang, and Shi Gu · 2021
Earlier work this paper cites.
Fq-vit: Post-training quantization for fully quantized vision transformer
Yang Lin, Tianyu Zhang, Peiqin Sun, Zheng Li, and Shuchang Zhou · 2021
Earlier work this paper cites.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Earlier work this paper cites.
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
Earlier work this paper cites.
https://cloud.google.com/blog/products/ai-machine-learning/bfloat16-the-secret-to-high-performance-on-cloud-tpus
Bfloat16: The secret to high performance on cloud tpus · 2021
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https://developer.nvidia.com/blog/accelerating-ai-training-with-tf32-tensor-cores/
Accelerating ai training with nvidia tf32 tensor cores · 2021
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High-resolution image synthesis with latent diffusion models, 2022
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Analytic-dpm: an analytic estimate of the optimal reverse variance in diffusion probabilistic models, 2022
Fan Bao, Chongxuan Li, Jun Zhu, and Bo Zhang · 2022
Cited alongside, same era.
Fast: Dnn training under variable precision block floating point with stochastic rounding
Sai Qian Zhang, Bradley McDanel, and HT Kung · 2022
Cited alongside, same era.
Llm.int8(): 8-bit matrix multiplication for transformers at scale, 2022
Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer · 2022
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents, 2022
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Cited alongside, same era.
Ptqd: Accurate post-training quantization for diffusion models, 2023
Yefei He, Luping Liu, Jing Liu, Weijia Wu, Hong Zhou, and Bohan Zhuang · 2023
Later among the works it cites.
Qa-lora: Quantization-aware low-rank adaptation of large language models
Yuhui Xu, Lingxi Xie, Xiaotao Gu, Xin Chen, Heng Chang, Hengheng Zhang, Zhensu Chen, Xiaopeng Zhang, and Qi Tian · 2023
Later among the works it cites.
Adding conditional control to text-to-image diffusion models
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2023
Later among the works it cites.
Dit-3d: Exploring plain diffusion transformers for 3d shape generation
Shentong Mo, Enze Xie, Ruihang Chu, Lanqing Hong, Matthias Niessner, and Zhenguo Li · 2024
Closest in time.
Latent diffusion transformer for probabilistic time series forecasting
Shibo Feng, Chunyan Miao, Zhong Zhang, and Peilin Zhao · 2024
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Scalable diffusion models with transformers (dit), 2022
Facebook Research · 2022
Cited alongside, same era.
Video diffusion models
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet · 2022
Cited alongside, same era.
Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
Cited alongside, same era.
Masked diffusion transformer is a strong image synthesizer
Shanghua Gao, Pan Zhou, Ming-Ming Cheng, and Shuicheng Yan · 2023
Cited alongside, same era.
Pixart: Fast training of diffusion transformer for photorealistic text-to-image synthesis
Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, et al · 2023
Cited alongside, same era.
Q-diffusion: Quantizing diffusion models, 2023
Xiuyu Li, Yijiang Liu, Long Lian, Huanrui Yang, Zhen Dong, Daniel Kang, Shanghang Zhang, and Kurt Keutzer · 2023
Cited alongside, same era.
Afpq: Asymmetric floating point quantization for llms
Yijia Zhang, Sicheng Zhang, Shijie Cao, Dayou Du, Jianyu Wei, Ting Cao, and Ningyi Xu · 2023
Cited alongside, same era.
Medsegdiff-v2: Diffusion-based medical image segmentation with transformer
Junde Wu, Wei Ji, Huazhu Fu, Min Xu, Yueming Jin, and Yanwu Xu · 2024
Closest in time.
Sora: Creating video from text, 2024
OpenAI · 2024
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Peng Gao, Le Zhuo, Ziyi Lin, Chris Liu, Junsong Chen, Ruoyi Du, Enze Xie, Xu Luo, Longtian Qiu, Yuhang Zhang, et al · 2024
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Efficientdm: Efficient quantization-aware fine-tuning of low-bit diffusion models, 2024
Yefei He, Jing Liu, Weijia Wu, Hong Zhou, and Bohan Zhuang · 2024
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Smoothquant: Accurate and efficient post-training quantization for large language models, 2024
Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han · 2024
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Nvidia blackwell architecture, 2024
NVIDIA · 2024
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Parameter-efficient fine-tuning for large models: A comprehensive survey
Zeyu Han, Chao Gao, Jinyang Liu, Sai Qian Zhang, et al · 2024
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Quip#: Even better llm quantization with hadamard incoherence and lattice codebooks, 2024
Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov, and Christopher De Sa · 2024
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