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Despite the success of diffusion models (DMs), we still lack a thorough understanding of their latent space.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2010
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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 · 2011
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Schubert varieties and distances between subspaces of different dimensions
Ye, K. and Lim, L.-H · 2016
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Latent space oddity: on the curvature of deep generative models
Arvanitidis, G., Hansen, L. K., and Hauberg, S · 2017
Earlier work this paper cites.
Metrics for deep generative models
Chen, N., Klushyn, A., Kurle, R., Jiang, X., Bayer, J., and Smagt, P · 2018
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Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., and Choo, J · 2018
Earlier work this paper cites.
Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2018
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A spectral regularizer for unsupervised disentanglement
Ramesh, A., Choi, Y., and LeCun, Y · 2018
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The riemannian geometry of deep generative models
Shao, H., Kumar, A., and Thomas Fletcher, P · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 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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Geometrically enriched latent spaces
Arvanitidis, G., Hauberg, S., and Schölkopf, B · 2020
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Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2020
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Ganspace: Discovering interpretable gan controls
Härkönen, E., Hertzmann, A., Lehtinen, J., and Paris, S · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Styleflow: Attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows
Abdal, R., Zhu, P., Mitra, N. J., and Wonka, P · 2021
Cited alongside, same era.
Label-efficient semantic segmentation with diffusion models
Baranchuk, D., Rubachev, I., Voynov, A., Khrulkov, V., and Babenko, A · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
Cited alongside, same era.
More control for free! image synthesis with semantic diffusion guidance
Liu, X., Park, D. H., Azadi, S., Zhang, G., Chopikyan, A., Hu, Y., Shi, H., Rohrbach, A., and Darrell, T · 2021
Cited alongside, same era.
ediffi: Text-to-image diffusion models with an ensemble of expert denoisers
Balaji, Y., Nah, S., Huang, X., Vahdat, A., Song, J., Kreis, K., Aittala, M., Aila, T., Laine, S., Catanzaro, B., et al · 2022
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Perception prioritized training of diffusion models
Choi, J., Lee, J., Shin, C., Kim, S., Kim, H., and Yoon, S · 2022
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Multiresolution textual inversion
Daras, G. and Dimakis, A. G · 2022
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Classifier-free diffusion guidance
Ho, J. 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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Meng, C., Song, Y., Song, J., Wu, J., Zhu, J.-Y., and Ermon, S · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Nichol, A. Q. and Dhariwal, P · 2021
Cited alongside, same era.
Styleclip: Text-driven manipulation of stylegan imagery
Patashnik, O., Wu, Z., Shechtman, E., Cohen-Or, D., and Lischinski, D · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Closed-form factorization of latent semantics in gans
Shen, Y. and Zhou, B · 2021
Cited alongside, same era.
Latentclr: A contrastive learning approach for unsupervised discovery of interpretable directions
Yüksel, O. K., Simsar, E., Er, E. G., and Yanardag, P · 2021
Cited alongside, same era.
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Imagic: Text-based real image editing with diffusion models
Kawar, B., Zada, S., Lang, O., Tov, O., Chang, H., Dekel, T., Mosseri, I., and Irani, M · 2022
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Diffusion models already have a semantic latent space
Kwon, M., Jeong, J., and Uh, Y · 2022
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Magicmix: Semantic mixing with diffusion models
Liew, J. H., Yan, H., Zhou, D., and Feng, J · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 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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Generating high fidelity data from low-density regions using diffusion models
Sehwag, V., Hazirbas, C., Gordo, A., Ozgenel, F., and Canton, C · 2022
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Plug-and-play diffusion features for text-driven image-to-image translation
Tumanyan, N., Geyer, M., Bagon, S., and Dekel, T · 2022
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gddim: Generalized denoising diffusion implicit models
Zhang, Q., Tao, M., and Chen, Y · 2022
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