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Diffusion models have emerged as dominant performers for image generation.
A computational approach to edge detection
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Imagenet: A large-scale hierarchical image database
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Auto-encoding variational bayes
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Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
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Tensorflow: a system for large-scale machine learning
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Horovod: fast and easy distributed deep learning in tensorflow
Sergeev, A. and Del Balso, M · 2018
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PyTorch Lightning, March 2019
Falcon, W. and The PyTorch Lightning team · 2019
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Gpipe: Efficient training of giant neural networks using pipeline parallelism
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Pipedream: Generalized pipeline parallelism for dnn training
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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 · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Rasley, J., Rajbhandari, S., Ruwase, O., and He, Y · 2020
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2020
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Colossal-ai: A unified deep learning system for large-scale parallel training
Bian, Z., Liu, H., Wang, B., Huang, H., Li, Y., Wang, C., Cui, F., and You, Y · 2021
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Ilvr: Conditioning method for denoising diffusion probabilistic models
Choi, J., Kim, S., Jeong, Y., Gwon, Y., and Yoon, S · 2021
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Dapple: A pipelined data parallel approach for training large models
Fan, S., Rong, Y., Meng, C., Cao, Z., Wang, S., Zheng, Z., Wu, C., Long, G., Yang, J., Xia, L., et al · 2021
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., et al · 2022
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Diffusers: State-of-the-art diffusion models, 2022
von Platen, P., Patil, S., Lozhkov, A., Cuenca, P., Lambert, N., Rasul, K., Davaadorj, M., and Wolf, T · 2022
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Seqdiffuseq: Text diffusion with encoder-decoder transformers
Yuan, H., Yuan, Z., Tan, C., Huang, F., and Huang, S · 2022
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All are worth words: A vit backbone for diffusion models
Bao, F., Nie, S., Xue, K., Cao, Y., Li, C., Su, H., and Zhu, J · 2023
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Openpifpaf: Composite fields for semantic keypoint detection and spatio-temporal association
Kreiss, S., Bertoni, L., and Alahi, A · 2021
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Chimera: efficiently training large-scale neural networks with bidirectional pipelines
Li, S. and Hoefler, T · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., and Chen, M · 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., et al · 2021
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Zero-infinity: Breaking the gpu memory wall for extreme scale deep learning
Rajbhandari, S., Ruwase, O., Rasley, J., Smith, S., and He, Y · 2021
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Analog bits: Generating discrete data using diffusion models with self-conditioning
Chen, T., Zhang, R., and Hinton, G · 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
Cited alongside, same era.
Pixart- α \alpha : Fast training of diffusion transformer for photorealistic text-to-image synthesis
Chen, J., Yu, J., Ge, C., Yao, L., Xie, E., Wu, Y., Wang, Z., Kwok, J. T., Luo, P., Lu, H., and Li, Z · 2023
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Scalable diffusion models with transformers
Peebles, W. S. and Xie, S · 2023
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., and Rombach, R · 2023
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Visual chatgpt: Talking, drawing and editing with visual foundation models
Wu, C., Yin, S., Qi, W., Wang, X., Tang, Z., and Duan, N · 2023
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Spae: Semantic pyramid autoencoder for multimodal generation with frozen llms
Yu, L., Cheng, Y., Wang, Z., Kumar, V., Macherey, W., Huang, Y., Ross, D. A., Essa, I., Bisk, Y., Yang, M.-H., et al · 2023
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Adding conditional control to text-to-image diffusion models
Zhang, L. and Agrawala, M · 2023
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Pixart- δ \delta : Fast and controllable image generation with latent consistency models
Chen, J., Wu, Y., Luo, S., Xie, E., Paul, S., Luo, P., Zhao, H., and Li, Z · 2024
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Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers
Ma, N., Goldstein, M., Albergo, M. S., Boffi, N. M., Vanden-Eijnden, E., and Xie, S · 2024
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