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Flow models are effective at progressively generating realistic images, but they generally struggle to capture long-range dependencies during the generation process as they compress all the information from previous time steps into a single corrupted image.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Conditional image generation with pixelcnn decoders
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Neural discrete representation learning
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Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
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Building normalizing flows with stochastic interpolants
Michael S Albergo and Eric Vanden-Eijnden · 2022
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Flow matching for generative modeling
Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2022
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Flow straight and fast: Learning to generate and transfer data with rectified flow
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Progressive distillation for fast sampling of diffusion models
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Scaling autoregressive models for content-rich text-to-image generation
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Weizhe Hua, Zihang Dai, Hanxiao Liu, and Quoc Le · 2022
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Jamba: A hybrid transformer-mamba language model
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Autoregressive model beats diffusion: Llama for scalable image generation
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Transformer quality in linear time
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William Peebles and Saining Xie · 2023
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Llama: Open and efficient foundation language models
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Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers
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Samba: Simple hybrid state space models for efficient unlimited context language modeling
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Fla: A triton-based library for hardware-efficient implementations of linear attention mechanism, January 2024
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