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Diffusion models have emerged as a dominant approach for text-to-image generation.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Bradley, R. A. and Terry, M. E · 1952
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Ganguli, S · 2015
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Sequence Tutor: Conservative fine-tuning of sequence generation models with KL-control
Jaques, N., Gu, S., Bahdanau, D., Hernández-Lobato, J. M., Turner, R. E., and Eck, D · 2017
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Adafactor: Adaptive learning rates with sublinear memory cost
Shazeer, N. and Stern, M · 2018
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2019
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Language models are few-shot learners
Brown, T. B., Mann, B., Ryder, N., et al · 2020
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Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Human-centric dialog training via offline reinforcement learning
Jaques, N., Shen, J. H., Ghandeharioun, A., et al · 2020
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Diffusion models beat GANs on image synthesis
Dhariwal, P. and Nichol, A · 2021
Earlier work this paper cites.
Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2022
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Nichol, A., Dhariwal, P., Ramesh, A., et al · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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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 · 2022
Earlier work this paper cites.
Universal guidance for diffusion models
Bansal, A., Chu, H.-M., Schwarzschild, A., Sengupta, S., Goldblum, M., Geiping, J., and Goldstein, T · 2023
Cited alongside, same era.
InstructPix2Pix: Learning to follow image editing instructions
Brooks, T., Holynski, A., and Efros, A. A · 2023
Cited alongside, same era.
RAFT: Reward rAnked FineTuning for generative foundation model alignment
Dong, H., Xiong, W., Goyal, D., et al · 2023
Cited alongside, same era.
DPOK: Reinforcement learning for fine-tuning text-to-image diffusion models
Fan, Y., Watkins, O., Du, Y., et al · 2023
Cited alongside, same era.
GENEVAL: An object-focused framework for evaluating text-to-image alignment
Ghosh, D., Hajishirzi, H., and Schmidt, L · 2023
Cited alongside, same era.
Improving sample quality of diffusion models using self-attention guidance
Hong, S., Lee, G., Jang, W., and Kim, S · 2023
Training diffusion models with reinforcement learning
Black, K., Janner, M., Du, Y., Kostrikov, I., and Levine, S · 2024
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A survey on generative diffusion models
Cao, H., Tan, C., Gao, Z., and others · 2024
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Emu: Enhancing image generation models using photogenic needles in a haystack
Dai, X., Hou, J., Ma, C.-Y., et al · 2024
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Open Image Preferences
Data is Better Together · 2024
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Scaling rectified flow Transformers for high-resolution image synthesis
Esser, P., Kulal, S., Blattmann, A., et al · 2024
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EllA: Equip diffusion models with LLM for enhanced semantic alignment
Hu, X., Wang, R., Fang, Y., Fu, B., Cheng, P., and Yu, G · 2024
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Cited alongside, same era.
Aligning text-to-image models using human feedback
Lee, K., Liu, H., Ryu, M., et al · 2023
Cited alongside, same era.
Flow matching for generative modeling
Lipman, Y., Chen, R. T., Ben-Hamu, H., Nickel, M., and Le, M · 2023
Cited alongside, same era.
Flow straight and fast: Learning to generate and transfer data with rectified flow
Liu, X., Gong, C., and Liu, Q · 2023
Cited alongside, same era.
On distillation of guided diffusion models
Meng, C., Rombach, R., Gao, R., Kingma, D., Ermon, S., Ho, J., and Salimans, T · 2023
Cited alongside, same era.
Scalable diffusion models with Transformers
Peebles, W. and Xie, S · 2023
Cited alongside, same era.
Direct preference optimization: your language model is secretly a reward model
Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., and Finn, C · 2023
Cited alongside, same era.
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FLUX.1-dev
Labs, B. F · 2024
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Readout Guidance: Learning control from diffusion features
Luo, G., Darrell, T., Wang, O., Goldman, D. B., and Holynski, A · 2024
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SDXL: Improving latent diffusion models for high-resolution image synthesis
Podell, D., English, Z., Lacey, K., et al · 2024
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Diffusion model alignment using direct preference optimization
Wallace, B., Dang, M., Rafailov, R., et al · 2024
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ImageReward: Learning and evaluating human preferences for text-to-image generation
Xu, J., Liu, X., Wu, Y., et al · 2024
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Using human feedback to fine-tune diffusion models without any reward model
Yang, K., Tao, J., Lyu, J., et al · 2024
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Negative preference optimization: From catastrophic collapse to effective unlearning
Zhang, R., Lin, L., Bai, Y., and Mei, S · 2024
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Diffusion-NPO: Negative preference optimization for better preference aligned generation of diffusion models
Wang, F.-Y., Shui, Y., Piao, J., Sun, K., and Li, H · 2025
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