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We propose Diffusion-Sharpening, a fine-tuning approach that enhances downstream alignment by optimizing sampling trajectories.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
Earlier work this paper cites.
Score-based generative modeling with critically-damped langevin diffusion
Dockhorn, T., Vahdat, A., and Kreis, K · 2021
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Variational diffusion models
Kingma, D., Salimans, T., Poole, B., and Ho, J · 2021
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Priorgrad: Improving conditional denoising diffusion models with data-dependent adaptive prior
Lee, S.-g., Kim, H., Shin, C., Tan, X., Liu, C., Meng, Q., Qin, T., Chen, W., Yoon, S., and Liu, T.-Y · 2021
Earlier work this paper cites.
Grad-tts: A diffusion probabilistic model for text-to-speech
Popov, V., Vovk, I., Gogoryan, V., Sadekova, T., and Kudinov, M · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Diffusion normalizing flow
Zhang, Q. and Chen, Y · 2021
Earlier work this paper cites.
Imagen video: High definition video generation with diffusion models
Ho, J., Chan, W., Saharia, C., Whang, J., Gao, R., Gritsenko, A., Kingma, D. P., Poole, B., Norouzi, M., Fleet, D. J., et al · 2022
Earlier work this paper cites.
Blurring diffusion models
Hoogeboom, E. and Salimans, T · 2022
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Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S · 2022
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Maximum likelihood training of implicit nonlinear diffusion model
Kim, D., Na, B., Kwon, S. J., Lee, D., Kang, W., and Moon, I.-c · 2022
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Flow straight and fast: Learning to generate and transfer data with rectified flow
Liu, X., Gong, C., et al · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 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
Cited alongside, same era.
Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al · 2022
Cited alongside, same era.
Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J · 2022
Cited alongside, same era.
Training diffusion models with reinforcement learning
Black, K., Janner, M., Du, Y., Kostrikov, I., and Levine, S · 2024
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Directly fine-tuning diffusion models on differentiable rewards
Clark, K., Vicol, P., Swersky, K., and Fleet, D. J · 2024
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Reinforcement learning for fine-tuning text-to-image diffusion models
Fan, Y., Watkins, O., Du, Y., Liu, H., Ryu, M., Boutilier, C., Abbeel, P., Ghavamzadeh, M., Lee, K., and Lee, K · 2024
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Rebel: Reinforcement learning via regressing relative rewards
Gao, Z., Chang, J. D., Zhan, W., Oertell, O., Swamy, G., Brantley, K., Joachims, T., Bagnell, J. A., Lee, J. D., and Sun, W · 2024
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Model-based diffusion for trajectory optimization
Pan, C., Yi, Z., Shi, G., and Qu, G · 2024
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Diffusiondb: A large-scale prompt gallery dataset for text-to-image generative models
Wang, Z. J., Montoya, E., Munechika, D., Yang, H., Hoover, B., and Chau, D. H · 2022
Cited alongside, same era.
Align your latents: High-resolution video synthesis with latent diffusion models
Blattmann, A., Rombach, R., Ling, H., Dockhorn, T., Kim, S. W., Fidler, S., and Kreis, K · 2023
Cited alongside, same era.
T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation
Huang, K., Sun, K., Xie, E., Li, Z., and Liu, X · 2023
Cited alongside, same era.
pokeman-blip-captions, 2023
lambdalabs · 2023
Cited alongside, same era.
Journeydb: A benchmark for generative image understanding, 2023
Pan, J., Sun, K., Ge, Y., Li, H., Duan, H., Wu, X., Zhang, R., Zhou, A., Qin, Z., Wang, Y., Dai, J., Qiao, Y., and Li, H · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Aligning text-to-image diffusion models with reward backpropagation
Prabhudesai, M., Goyal, A., Pathak, D., and Fragkiadaki, K · 2023
Cited alongside, same era.
Uehara, M., Zhao, Y., Black, K., Hajiramezanali, E., Scalia, G., Diamant, N. L., Tseng, A. M., Levine, S., and Biancalani, T · 2024
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Diffusion model alignment using direct preference optimization
Wallace, B., Dang, M., Rafailov, R., Zhou, L., Lou, A., Purushwalkam, S., Ermon, S., Xiong, C., Joty, S., and Naik, N · 2024
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Imagereward: Learning and evaluating human preferences for text-to-image generation
Xu, J., Liu, X., Wu, Y., Tong, Y., Li, Q., Ding, M., Tang, J., and Dong, Y · 2024
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Training-free diffusion model alignment with sampling demons
Yeh, P.-H., Lee, K.-H., and Chen, J.-C · 2024
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zk · 2024
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Inference-time scaling for diffusion models beyond scaling denoising steps
Ma, N., Tong, S., Jia, H., Hu, H., Su, Y.-C., Zhang, M., Yang, X., Li, Y., Jaakkola, T., Jia, X., et al · 2025
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Itercomp: Iterative composition-aware feedback learning from model gallery for text-to-image generation
Zhang, X., Yang, L., Li, G., Cai, Y., Xie, J., Tang, Y., Yang, Y., Wang, M., and Cui, B · 2025
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