Fetching the paper…
Reading the bibliography…
We present Diffusion Model Patching (DMP), a simple method to boost the performance of pre-trained diffusion models that have already reached convergence, with a negligible increase in parameters.
Language models are few-shot learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 1901
Earlier work this paper cites.
Language models as knowledge bases?
Petroni, F.; Rocktäschel, T.; Lewis, P.; Bakhtin, A.; Wu, Y.; Miller, A. H.; and Riedel, S. 2019 · 1909
Earlier work this paper cites.
Multi-concept customization of text-to-image diffusion
Kumari, N.; Zhang, B.; Zhang, R.; Shechtman, E.; and Zhu, J.-Y. 2023 · 1941
Earlier work this paper cites.
Adaptive mixtures of local experts
Jacobs, R. A.; Jordan, M. I.; Nowlan, S. J.; and Hinton, G. E. 1991 · 1991
Earlier work this paper cites.
Hierarchical mixtures of experts and the EM algorithm
Jordan, M. I.; and Jacobs, R. A. 1994 · 1994
Earlier work this paper cites.
Multitask learning
Caruana, R. 1997 · 1997
Earlier work this paper cites.
Learning to prompt for vision-language models
Zhou, K.; Yang, J.; Loy, C. C.; and Liu, Z. 2022b · 1997
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
Earlier work this paper cites.
Diffwave: A versatile diffusion model for audio synthesis
Kong, Z.; Ping, W.; Huang, J.; Zhao, K.; and Catanzaro, B. 2020 · 2009
Earlier work this paper cites.
A survey on transfer learning
Pan, S. J.; and Yang, Q. 2009 · 2009
Earlier work this paper cites.
It’s not just size that matters: Small language models are also few-shot learners
Schick, T.; and Schütze, H. 2020 · 2009
Earlier work this paper cites.
Denoising diffusion implicit models
Song, J.; Meng, C.; and Ermon, S. 2020 · 2010
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
Vincent, P. 2011 · 2011
Earlier work this paper cites.
Learning factored representations in a deep mixture of experts
Eigen, D.; Ranzato, M.; and Sutskever, I. 2013 · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P.; and Welling, M. 2013 · 2013
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J.; Weiss, E.; Maheswaranathan, N.; and Ganguli, S. 2015 · 2015
Earlier work this paper cites.
Ba, J. L.; Kiros, J. R.; and Hinton, G. E. 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; and Wojna, Z. 2016 · 2016
Earlier work this paper cites.
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
Earlier work this paper cites.
Decoupled weight decay regularization
Loshchilov, I.; and Hutter, F. 2017 · 2017
Earlier work this paper cites.
Outrageously large neural networks: The sparsely-gated mixture-of-experts layer
Shazeer, N.; Mirhoseini, A.; Maziarz, K.; Davis, A.; Le, Q.; Hinton, G.; and Dean, J. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Earlier work this paper cites.
Parameter-efficient transfer learning for NLP
Houlsby, N.; Giurgiu, A.; Jastrzebski, S.; Morrone, B.; De Laroussilhe, Q.; Gesmundo, A.; Attariyan, M.; and Gelly, S. 2019 · 2019
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks
Karras, T.; Laine, S.; and Aila, T. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; Sutskever, I.; et al. 2019 · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Song, Y.; and Ermon, S. 2019 · 2019
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
Cited alongside, same era.
Side-tuning: a baseline for network adaptation via additive side networks
Zhang, J. O.; Sax, A.; Zamir, A.; Guibas, L.; and Malik, J. 2020 · 2020
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P.; and Nichol, A. 2021 · 2021
Cited alongside, same era.
Pseudo numerical methods for diffusion models on manifolds
Liu, L.; Ren, Y.; Lin, Z.; and Zhao, Z. 2022 · 2022
Later among the works it cites.
Scalable Diffusion Models with Transformers
Peebles, W.; and Xie, S. 2022 · 2022
Later among the works it cites.
Dreamfusion: Text-to-3d using 2d diffusion
Poole, B.; Jain, A.; Barron, J. T.; and Mildenhall, B. 2022 · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2022 · 2022
Later among the works it cites.
Laion-5b: An open large-scale dataset for training next generation image-text models
Schuhmann, C.; Beaumont, R.; Vencu, R.; Gordon, C.; Wightman, R.; Cherti, M.; Coombes, T.; Katta, A.; Mullis, C.; Wortsman, M.; et al. 2022 · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Lora: Low-rank adaptation of large language models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2021 · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
Lester, B.; Al-Rfou, R.; and Constant, N. 2021 · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Li, X. L.; and Liang, P. 2021 · 2021
Cited alongside, same era.
P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks
Liu, X.; Ji, K.; Fu, Y.; Tam, W. L.; Du, Z.; Yang, Z.; and Tang, J. 2021 · 2021
Cited alongside, same era.
Cutting down on prompts and parameters: Simple few-shot learning with language models
Logan IV, R. L.; Balažević, I.; Wallace, E.; Petroni, F.; Singh, S.; and Riedel, S. 2021 · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Nichol, A. Q.; and Dhariwal, P. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
Lst: Ladder side-tuning for parameter and memory efficient transfer learning
Sung, Y.-L.; Cho, J.; and Bansal, M. 2022 · 2022
Later among the works it cites.
Learning to prompt for continual learning
Wang, Z.; Zhang, Z.; Lee, C.-Y.; Zhang, H.; Sun, R.; Ren, X.; Su, G.; Perot, V.; Dy, J.; and Pfister, T. 2022 · 2022
Later among the works it cites.
ERNIE-ViLG 2.0: Improving text-to-image diffusion model with knowledge-enhanced mixture-of-denoising-experts
Feng, Z.; Zhang, Z.; Yu, X.; Fang, Y.; Li, L.; Chen, X.; Lu, Y.; Liu, J.; Yin, W.; Feng, S.; et al. 2023 · 2023
Later among the works it cites.
Addressing Negative Transfer in Diffusion Models
Go, H.; Kim, J.; Lee, Y.; Lee, S.; Oh, S.; Moon, H.; and Choi, S. 2023 · 2023
Later among the works it cites.
Training data protection with compositional diffusion models
Golatkar, A.; Achille, A.; Swaminathan, A.; and Soatto, S. 2023 · 2023
Later among the works it cites.
Svdiff: Compact parameter space for diffusion fine-tuning
Han, L.; Li, Y.; Zhang, H.; Milanfar, P.; Metaxas, D.; and Yang, F. 2023 · 2023
Later among the works it cites.
Efficient diffusion training via min-snr weighting strategy
Hang, T.; Gu, S.; Li, C.; Bao, J.; Chen, D.; Hu, H.; Geng, X.; and Guo, B. 2023 · 2023
Later among the works it cites.
SCEdit: Efficient and Controllable Image Diffusion Generation via Skip Connection Editing
Jiang, Z.; Mao, C.; Pan, Y.; Han, Z.; and Zhang, J. 2023 · 2023
Later among the works it cites.
Mou, C.; Wang, X.; Xie, L.; Zhang, J.; Qi, Z.; Shan, Y.; and Qie, X. 2023 · 2023
Later among the works it cites.
Denoising Task Routing for Diffusion Models
Park, B.; Woo, S.; Go, H.; Kim, J.-Y.; and Kim, C. 2023 · 2023
Later among the works it cites.
HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3D
Woo, S.; Park, B.; Go, H.; Kim, J.-Y.; and Kim, C. 2023 · 2023
Later among the works it cites.
A closer look at parameter-efficient tuning in diffusion models
Xiang, C.; Bao, F.; Li, C.; Su, H.; and Zhu, J. 2023 · 2023
Later among the works it cites.
Xie, E.; Yao, L.; Shi, H.; Liu, Z.; Zhou, D.; Liu, Z.; Li, J.; and Li, Z. 2023 · 2023
Later among the works it cites.
Raphael: Text-to-image generation via large mixture of diffusion paths
Xue, Z.; Song, G.; Guo, Q.; Liu, B.; Zong, Z.; Liu, Y.; and Luo, P. 2023 · 2023
Later among the works it cites.
Adding conditional control to text-to-image diffusion models
Zhang, L.; Rao, A.; and Agrawala, M. 2023 · 2023
Later among the works it cites.
Gao, P.; Zhuo, L.; Lin, Z.; Liu, C.; Chen, J.; Du, R.; Xie, E.; Luo, X.; Qiu, L.; Zhang, Y.; et al. 2024 · 2024
Closest in time.
Fit: Flexible vision transformer for diffusion model
Lu, Z.; Wang, Z.; Huang, D.; Wu, C.; Liu, X.; Ouyang, W.; and Bai, L. 2024 · 2024
Closest in time.
Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts
Park, B.; Go, H.; Kim, J.-Y.; Woo, S.; Ham, S.; and Kim, C. 2024 · 2024
Closest in time.
Any-size-diffusion: Toward efficient text-driven synthesis for any-size hd images
Zheng, Q.; Guo, Y.; Deng, J.; Han, J.; Li, Y.; Xu, S.; and Xu, H. 2024 · 2024
Closest in time.