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Diffusion Models are popular generative modeling methods in various vision tasks, attracting significant attention.
N. Tumanyan, M. Geyer, S. Bagon, and T. Dekel, “Plug-and-play diffusion features for text-driven image-to-image translation,” in
1930
Earlier work this paper cites.
K. Itô, “Stochastic differential equations in a differentiable manifold,”
1950
Earlier work this paper cites.
B. D. O. Anderson, “Reverse-time diffusion equation models,”
1982
Earlier work this paper cites.
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, “Extracting and composing robust features with denoising autoencoders,” in
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in
2009
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,”
2010
Earlier work this paper cites.
P. Vincent, “A connection between score matching and denoising autoencoders,”
2011
Earlier work this paper cites.
Y. Bengio, L. Yao, G. Alain, and P. Vincent, “Generalized denoising auto-encoders as generative models,” in
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in
2014
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in
2014
Earlier work this paper cites.
M. Mirza and S. Osindero, “Conditional generative adversarial nets,”
2014
Earlier work this paper cites.
D. J. Rezende, S. Mohamed, and D. Wierstra, “Stochastic backpropagation and approximate inference in deep generative models,” in
2014
Earlier work this paper cites.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the Knowledge in a Neural Network,”
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in
2015
Earlier work this paper cites.
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio, “FitNets: Hints for Thin Deep Nets,”
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in
2015
Earlier work this paper cites.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in
2015
Earlier work this paper cites.
F. Yu, Y. Zhang, S. Song, A. Seff, and J. Xiao, “Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop,”
2015
Earlier work this paper cites.
J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer Normalization,”
2016
Earlier work this paper cites.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs,”
2016
Earlier work this paper cites.
D. Lin, J. Dai, J. Jia, K. He, and J. Sun, “Scribblesup: Scribble-supervised convolutional networks for semantic segmentation,” in
2016
Earlier work this paper cites.
——, “Wide residual networks,” in
2016
Earlier work this paper cites.
——, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,”
2017
Earlier work this paper cites.
B. Ham, M. Cho, C. Schmid, and J. Ponce, “Proposal flow: Semantic correspondences from object proposals,”
2017
Earlier work this paper cites.
T. Salimans, A. Karpathy, X. Chen, and D. P. Kingma, “Pixelcnn++: A pixelcnn implementation with discretized logistic mixture likelihood and other modifications,” in
2017
Earlier work this paper cites.
S. Zagoruyko and N. Komodakis, “Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer,” in
2017
Earlier work this paper cites.
J. Ahn and S. Kwak, “Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation,” in
2018
Earlier work this paper cites.
Y. M. Asano, C. Rupprecht, and A. Vedaldi, “Self-labelling via simultaneous clustering and representation learning,”
2019
Earlier work this paper cites.
A. Brock, J. Donahue, and K. Simonyan, “Large scale gan training for high fidelity natural image synthesis,” in
2019
Earlier work this paper cites.
J. Donahue and K. Simonyan, “Large scale adversarial representation learning,” in
2019
Earlier work this paper cites.
P. Helber, B. Bischke, A. Dengel, and D. Borth, “Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,”
2019
Earlier work this paper cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in
2019
Earlier work this paper cites.
——, “An Introduction to Variational Autoencoders,”
2019
Earlier work this paper cites.
A. Kirillov, R. Girshick, K. He, and P. Dollár, “Panoptic feature pyramid networks,” in
2019
Earlier work this paper cites.
A. Kirillov, K. He, R. Girshick, C. Rother, and P. Dollár, “Panoptic segmentation,” in
2019
Earlier work this paper cites.
J. Min, J. Lee, J. Ponce, and M. Cho, “SPair-71k: A Large-scale Benchmark for Semantic Correspondence,”
2019
Earlier work this paper cites.
Y. Song and S. Ermon, “Generative Modeling by Estimating Gradients of the Data Distribution,” in
2019
Earlier work this paper cites.
B. Zhou, H. Zhao, X. Puig, T. Xiao, S. Fidler, A. Barriuso, and A. Torralba, “Semantic understanding of scenes through the ade20k dataset,”
2019
Earlier work this paper cites.
I. Beltagy, M. E. Peters, and A. Cohan, “Longformer: The Long-Document Transformer,”
2020
Earlier work this paper cites.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in
2020
Earlier work this paper cites.
T. Chen, S. Kornblith, K. Swersky, M. Norouzi, and G. E. Hinton, “Big self-supervised models are strong semi-supervised learners,” in
2020
Earlier work this paper cites.
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar, B. Piot, k. kavukcuoglu, R. Munos, and M. Valko, “Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning,” in
2020
Earlier work this paper cites.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in
2020
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in
2020
Earlier work this paper cites.
S. Liu, T. Wang, D. Bau, J.-Y. Zhu, and A. Torralba, “Diverse Image Generation via Self-Conditioned GANs,” in
2020
Earlier work this paper cites.
A. Voynov and A. Babenko, “Unsupervised discovery of interpretable directions in the gan latent space,” in
2020
Earlier work this paper cites.
F. Yu, H. Chen, X. Wang, W. Xian, Y. Chen, F. Liu, V. Madhavan, and T. Darrell, “Bdd100k: A diverse driving dataset for heterogeneous multitask learning,” in
2020
Earlier work this paper cites.
J. Austin, D. D. Johnson, J. Ho, D. Tarlow, and R. van den Berg, “Structured Denoising Diffusion Models in Discrete State-Spaces,” in
2021
Earlier work this paper cites.
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in
2021
Earlier work this paper cites.
A. Casanova, M. Careil, J. Verbeek, M. Drozdzal, and A. Romero Soriano, “Instance-conditioned gan,” in
2021
Earlier work this paper cites.
N. Chen, Y. Zhang, H. Zen, R. J. Weiss, M. Norouzi, and W. Chan, “WaveGrad: Estimating Gradients for Waveform Generation,” in
2021
Earlier work this paper cites.
P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” in
2021
Earlier work this paper cites.
——, “Diffusion Models Beat GANs on Image Synthesis,” in
2021
Earlier work this paper cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly
2021
Earlier work this paper cites.
P. Esser, R. Rombach, and B. Ommer, “Taming transformers for high-resolution image synthesis,” in
2021
Earlier work this paper cites.
J. Ho and T. Salimans, “Classifier-free diffusion guidance,” in
2021
Earlier work this paper cites.
E. Hoogeboom, D. Nielsen, P. Jaini, P. Forré, and M. Welling, “Argmax flows and multinomial diffusion: Learning categorical distributions,” in
2021
Earlier work this paper cites.
C.-W. Huang, J. H. Lim, and A. Courville, “A variational perspective on diffusion-based generative models and score matching,” in
2021
Earlier work this paper cites.
D. Kingma, T. Salimans, B. Poole, and J. Ho, “Variational Diffusion Models,” in
2021
Earlier work this paper cites.
Z. Kong, W. Ping, J. Huang, K. Zhao, and B. Catanzaro, “DiffWave: A Versatile Diffusion Model for Audio Synthesis,”
2021
Earlier work this paper cites.
Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows,” in
2021
Earlier work this paper cites.
Z. Pan, P. Jiang, Y. Wang, C. Tu, and A. G. Cohn, “Scribble-supervised semantic segmentation by uncertainty reduction on neural representation and self-supervision on neural eigenspace,” in
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark
2021
Earlier work this paper cites.
J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” in
2021
Cited alongside, same era.
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole, “Score-based generative modeling through stochastic differential equations,” in
2021
Cited alongside, same era.
R. S. Zimmermann, L. Schott, Y. Song, B. A. Dunn, and D. A. Klindt, “Score-based generative classifiers,” in
2021
Cited alongside, same era.
N. Anand and T. Achim, “Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models,”
2022
Cited alongside, same era.
D. Baranchuk, A. Voynov, I. Rubachev, V. Khrulkov, and A. Babenko, “Label-efficient semantic segmentation with diffusion models,” in
2022
Cited alongside, same era.
S. J. Prince,
2023
Later among the works it cites.
S. Sheynin, O. Ashual, A. Polyak, U. Singer, O. Gafni, E. Nachmani, and Y. Taigman, “kNN-diffusion: Image generation via large-scale retrieval,” in
2023
Later among the works it cites.
J. Shipard, A. Wiliem, K. N. Thanh, W. Xiang, and C. Fookes, “Diversity is definitely needed: Improving model-agnostic zero-shot classification via stable diffusion,” in
2023
Later among the works it cites.
Y. Song, P. Dhariwal, M. Chen, and I. Sutskever, “Consistency models,”
2023
Later among the works it cites.
L. Tang, M. Jia, Q. Wang, C. P. Phoo, and B. Hariharan, “Emergent correspondence from image diffusion,” in
2023
Later among the works it cites.
B. Wallace, A. Gokul, S. Ermon, and N. Naik, “End-to-end diffusion latent optimization improves classifier guidance,” in
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Y. Benny and L. Wolf, “Dynamic dual-output diffusion models,” in
2022
Cited alongside, same era.
A. Blattmann, R. Rombach, K. Oktay, and B. Ommer, “Retrieval-augmented diffusion models,”
2022
Cited alongside, same era.
S. Borgeaud, A. Mensch, J. Hoffmann, T. Cai, E. Rutherford, K. Millican, G. B. Van Den Driessche, J.-B. Lespiau, B. Damoc, A. Clark
2022
Cited alongside, same era.
H. Chang, H. Zhang, L. Jiang, C. Liu, and W. T. Freeman, “Maskgit: Masked generative image transformer,” in
2022
Cited alongside, same era.
A. Graikos, N. Malkin, N. Jojic, and D. Samaras, “Diffusion models as plug-and-play priors,” in
2022
Cited alongside, same era.
K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in
2022
Cited alongside, same era.
A. Hertz, R. Mokady, J. Tenenbaum, K. Aberman, Y. Pritch, and D. Cohen-Or, “Prompt-to-Prompt Image Editing with Cross Attention Control,”
2022
Cited alongside, same era.
2023
Later among the works it cites.
Y. Wang, Y. Schiff, A. Gokaslan, W. Pan, F. Wang, C. De Sa, and V. Kuleshov, “Infodiffusion: Representation learning using information maximizing diffusion models,” in
2023
Later among the works it cites.
C. Wei, K. Mangalam, P.-Y. Huang, Y. Li, H. Fan, H. Xu, H. Wang, C. Xie, A. Yuille, and C. Feichtenhofer, “Diffusion models as masked autoencoders,” in
2023
Later among the works it cites.
W. Wu, Y. Zhao, M. Z. Shou, H. Zhou, and C. Shen, “Diffumask: Synthesizing images with pixel-level annotations for semantic segmentation using diffusion models,” in
2023
Later among the works it cites.
W. Xiang, H. Yang, D. Huang, and Y. Wang, “Denoising diffusion autoencoders are unified self-supervised learners,” in
2023
Later among the works it cites.
J. Xu, S. Liu, A. Vahdat, W. Byeon, X. Wang, and S. De Mello, “Open-vocabulary panoptic segmentation with text-to-image diffusion models,” in
2023
Later among the works it cites.
L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, W. Zhang, B. Cui, and M.-H. Yang, “Diffusion models: A comprehensive survey of methods and applications,”
2023
Later among the works it cites.
X. Yang and X. Wang, “Diffusion model as representation learner,” in
2023
Later among the works it cites.
Z. You, Y. Zhong, F. Bao, J. Sun, C. Li, and J. Zhu, “Diffusion models and semi-supervised learners benefit mutually with few labels,” in
2023
Later among the works it cites.
J. Yu, Y. Wang, C. Zhao, B. Ghanem, and J. Zhang, “Freedom: Training-free energy-guided conditional diffusion model,”
2023
Later among the works it cites.
J. Zhang, C. Herrmann, J. Hur, L. P. Cabrera, V. Jampani, D. Sun, and M.-H. Yang, “A tale of two features: Stable diffusion complements DINO for zero-shot semantic correspondence,” in
2023
Later among the works it cites.
L. Zhang, A. Rao, and M. Agrawala, “Adding conditional control to text-to-image diffusion models,” in
2023
Later among the works it cites.
W. Zhao, Y. Rao, Z. Liu, B. Liu, J. Zhou, and J. Lu, “Unleashing text-to-image diffusion models for visual perception,” in
2023
Later among the works it cites.
K. Zheng, C. Lu, J. Chen, and J. Zhu, “Improved techniques for maximum likelihood estimation for diffusion odes,” in
2023
Later among the works it cites.
N. Adaloglou, T. Kaiser, F. Michels, and M. Kollmann, “Rethinking cluster-conditioned diffusion models,”
2024
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D. Ahn, H. Cho, J. Min, W. Jang, J. Kim, S. Kim, H. H. Park, K. H. Jin, and S. Kim, “Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance,”
2024
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S. Ayromlou, A. Afkanpour, V. R. Khazaie, and F. Forghani, “Can Generative Models Improve Self-Supervised Representation Learning?”
2024
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H. Ben-Hamu, O. Puny, I. Gat, B. Karrer, U. Singer, and Y. Lipman, “D-Flow: Differentiating through Flows for Controlled Generation,”
2024
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H. Chefer, O. Lang, M. Geva, V. Polosukhin, A. Shocher, M. Irani, I. Mosseri, and L. Wolf, “The Hidden Language of Diffusion Models,” in
2024
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G. Chen, Y. Huang, J. Xu, B. Pei, Z. Chen, Z. Li, J. Wang, K. Li, T. Lu, and L. Wang, “Video Mamba Suite: State Space Model as a Versatile Alternative for Video Understanding,”
2024
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X. Chen, Z. Liu, S. Xie, and K. He, “Deconstructing Denoising Diffusion Models for Self-Supervised Learning,”
2024
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Z. Fei, M. Fan, C. Yu, and J. Huang, “Scalable Diffusion Models with State Space Backbone,”
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Z. Geng, B. Yang, T. Hang, C. Li, S. Gu, T. Zhang, J. Bao, Z. Zhang, H. Li, H. Hu
2024
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M. Geyer, O. Bar-Tal, S. Bagon, and T. Dekel, “Tokenflow: Consistent diffusion features for consistent video editing,” in
2024
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A. Gu and T. Dao, “Mamba: Linear-Time Sequence Modeling with Selective State Spaces,”
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M. Gui, J. Schusterbauer, U. Prestel, P. Ma, D. Kotovenko, O. Grebenkova, S. A. Baumann, V. T. Hu, and B. Ommer, “Depthfm: Fast monocular depth estimation with flow matching,”
2024
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J. Guo, X. Xu, Y. Pu, Z. Ni, C. Wang, M. Vasu, S. Song, G. Huang, and H. Shi, “Smooth diffusion: Crafting smooth latent spaces in diffusion models,” in
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J. Heek, E. Hoogeboom, and T. Salimans, “Multistep consistency models,”
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V. T. Hu, S. A. Baumann, M. Gui, O. Grebenkova, P. Ma, J. Schusterbauer, and B. Ommer, “ZigMa: A DiT-style Zigzag Mamba Diffusion Model,”
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V. T. Hu, D. Wu, Y. M. Asano, P. Mettes, B. Fernando, B. Ommer, and C. G. M. Snoek, “Flow matching for conditional text generation in a few sampling steps,” in
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V. T. Hu, W. Zhang, M. Tang, P. Mettes, D. Zhao, and C. Snoek, “Latent space editing in transformer-based flow matching,” in
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Y. Huang, J. Huang, Y. Liu, M. Yan, J. Lv, J. Liu, W. Xiong, H. Zhang, S. Chen, and L. Cao, “Diffusion Model-Based Image Editing: A Survey,”
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D. A. Hudson, D. Zoran, M. Malinowski, A. K. Lampinen, A. Jaegle, J. L. McClelland, L. Matthey, F. Hill, and A. Lerchner, “Soda: Bottleneck diffusion models for representation learning,” in
2024
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G. Kim, W. Jang, G. Lee, S. Hong, J. Seo, and S. Kim, “Depth-aware guidance with self-estimated depth representations of diffusion models,”
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S. Li, C. Chen, and H. Lu, “MoEController: Instruction-based Arbitrary Image Manipulation with Mixture-of-Expert Controllers,”
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X. Li, J. Lu, K. Han, and V. A. Prisacariu, “Sd4match: Learning to prompt stable diffusion model for semantic matching,” in
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S. Lin and X. Yang, “Diffusion Model with Perceptual Loss,”
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H. Liu, M. Zaharia, and P. Abbeel, “Ringattention with blockwise transformers for near-infinite context,” in
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G. Luo, T. Darrell, O. Wang, D. B. Goldman, and A. Holynski, “Readout Guidance: Learning Control from Diffusion Features,” in
2024
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W. Luo, T. Hu, S. Zhang, J. Sun, Z. Li, and Z. Zhang, “Diff-instruct: A universal approach for transferring knowledge from pre-trained diffusion models,”
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M. Mardani, J. Song, J. Kautz, and A. Vahdat, “A variational perspective on solving inverse problems with diffusion models,” in
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O. Mariotti, O. Mac Aodha, and H. Bilen, “Improving semantic correspondence with viewpoint-guided spherical maps,” in
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M. Oquab, T. Darcet, T. Moutakanni, H. V. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. HAZIZA, F. Massa, A. El-Nouby, M. Assran, N. Ballas, W. Galuba, R. Howes, P.-Y. Huang, S.-W. Li, I. Misra, M. Rabbat, V. Sharma, G. Synnaeve, H. Xu, H. Jegou, J. Mairal, P. Labatut, A. Joulin, and P. Bojanowski, “DINOv2: Learning robust visual features without supervision,”
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K. Pandey, P. Guerrero, M. Gadelha, Y. Hold-Geoffroy, K. Singh, and N. J. Mitra, “Diffusion handles enabling 3d edits for diffusion models by lifting activations to 3d,” in
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D. Samuel, R. Ben-Ari, S. Raviv, N. Darshan, and G. Chechik, “Generating images of rare concepts using pre-trained diffusion models,” in
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A. Sauer, F. Boesel, T. Dockhorn, A. Blattmann, P. Esser, and R. Rombach, “Fast high-resolution image synthesis with latent adversarial diffusion distillation,”
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J. Schnell, J. Wang, L. Qi, V. T. Hu, and M. Tang, “ScribbleGen: Generative Data Augmentation Improves Scribble-supervised Semantic Segmentation,”
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Y. Song, A. Keller, N. Sebe, and M. Welling, “Flow factorized representation learning,” in
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C. Tian, C. Tao, J. Dai, H. Li, Z. Li, L. Lu, X. Wang, H. Li, G. Huang, and X. Zhu, “ADDP: Learning general representations for image recognition and generation with alternating denoising diffusion process,” in
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K. Tian, Y. Jiang, Z. Yuan, B. Peng, and L. Wang, “Visual autoregressive modeling: Scalable image generation via next-scale prediction,”
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J. N. Yan, J. Gu, and A. M. Rush, “Diffusion models without attention,” in
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D. Yatim, R. Fridman, O. Bar-Tal, Y. Kasten, and T. Dekel, “Space-Time Diffusion Features for Zero-Shot Text-Driven Motion Transfer,” in
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Z. Yue, J. Wang, Q. Sun, L. Ji, E. I. Chang, and H. Zhang, “Exploring diffusion time-steps for unsupervised representation learning,” in
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J. Zhang, C. Herrmann, J. Hur, E. Chen, V. Jampani, D. Sun, and M.-H. Yang, “Telling left from right: Identifying geometry-aware semantic correspondence,” in
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