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We study the theoretical foundations of composition in diffusion models, with a particular focus on out-of-distribution extrapolation and length-generalization.
Brownian dynamics as smart monte carlo simulation
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Deep unsupervised learning using nonequilibrium thermodynamics
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Unpaired image-to-image translation using cycle-consistent adversarial networks
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Image style transfer using convolutional neural networks
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
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Isolating sources of disentanglement in variational autoencoders
Chen, R. T., Li, X., Grosse, R. B., and Duvenaud, D. K · 2018
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Disentangling by factorising
Kim, H. and Mnih, A · 2018
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
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Content and style disentanglement for artistic style transfer
Kotovenko, D., Sanakoyeu, A., Lang, S., and Ommer, B · 2019
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Challenging common assumptions in the unsupervised learning of disentangled representations
Locatello, F., Bauer, S., Lucic, M., Raetsch, G., Gelly, S., Schölkopf, B., and Bachem, O · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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A wasserstein-type distance in the space of gaussian mixture models
Delon, J. and Desolneux, A · 2020
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Compositional visual generation and inference with energy based models
Du, Y., Li, S., and Mordatch, I · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Learning to compose visual relations
Liu, N., Li, S., Du, Y., Tenenbaum, J., and Torralba, A · 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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Compositional foundation models for hierarchical planning
Ajay, A., Han, S., Du, Y., Li, S., Gupta, A., Jaakkola, T., Tenenbaum, J., Kaelbling, L., Srivastava, A., and Agrawal, P · 2024
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Position: Compositional generative modeling: A single model is not all you need
Du, Y. and Kaelbling, L. P · 2024
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An analytic theory of creativity in convolutional diffusion models
Kamb, M. and Ganguli, S · 2024
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Analyzing and improving the training dynamics of diffusion models
Karras, T., Aittala, M., Lehtinen, J., Hellsten, J., Aila, T., and Laine, S · 2024
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Correcting diffusion generation through resampling
Liu, Y., Zhang, Y., Jaakkola, T., and Chang, S · 2024
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Nie, W., Vahdat, A., and Anandkumar, A · 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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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2021
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Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2022
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Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W., et al · 2022
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Editing models with task arithmetic
Ilharco, G., Ribeiro, M. T., Wortsman, M., Gururangan, S., Schmidt, L., Hajishirzi, H., and Farhadi, A · 2022
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Planning with diffusion for flexible behavior synthesis
Janner, M., Du, Y., Tenenbaum, J. B., and Levine, S · 2022
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Niedoba, M., Zwartsenberg, B., Murphy, K., and Wood, F · 2024
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Compositional abilities emerge multiplicatively: Exploring diffusion models on a synthetic task
Okawa, M., Lubana, E. S., Dick, R., and Tanaka, H · 2024
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Emergence of hidden capabilities: Exploring learning dynamics in concept space
Park, C. F., Okawa, M., Lee, A., Lubana, E. S., and Tanaka, H · 2024
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The superposition of diffusion models using the itô density estimator
Skreta, M., Atanackovic, L., Bose, A. J., Tong, A., and Neklyudov, K · 2024
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Ctrloralter: Conditional loradapter for efficient 0-shot control and altering of t2i models
Stracke, N., Baumann, S. A., Susskind, J., Bautista, M. A., and Ommer, B · 2024
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Compositional image decomposition with diffusion models
Su, J., Liu, N., Wang, Y., Tenenbaum, J. B., and Du, Y · 2024
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Concept algebra for (score-based) text-controlled generative models
Wang, Z., Gui, L., Negrea, J., and Veitch, V · 2024
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Compositional generalization from first principles
Wiedemer, T., Mayilvahanan, P., Bethge, M., and Brendel, W · 2024
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Compositional generative inverse design
Wu, T., Maruyama, T., Wei, L., Zhang, T., Du, Y., Iaccarino, G., and Leskovec, J · 2024
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Local mechanisms of compositional generalization in conditional diffusion
Bradley, A · 2025
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Decentralized diffusion models
McAllister, D., Tancik, M., Song, J., and Kanazawa, A · 2025
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Scaling in-the-wild training for diffusion-based illumination harmonization and editing by imposing consistent light transport
Zhang, L., Rao, A., and Agrawala, M · 2025
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