Fetching the paper…
Reading the bibliography…
The primary axes of interest in image-generating diffusion models are image quality, the amount of variation in the results, and how well the results align with a given condition, e.g., a class label or a text prompt.
Neural networks for pattern recognition
C. M. Bishop · 1995
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
A. Hyvärinen · 2005
Earlier work this paper cites.
ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Interpretation and generalization of score matching
S. Lyu · 2009
Earlier work this paper cites.
A connection between score matching and denoising autoencoders
P. Vincent · 2011
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
Earlier work this paper cites.
Gaussian error linear units (GELUs)
D. Hendrycks and K. Gimpel · 2016
Earlier work this paper cites.
Rethinking the Inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
Earlier work this paper cites.
GANs trained by a two time-scale update rule converge to a local Nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
Earlier work this paper cites.
Megapixel size image creation using generative adversarial networks
M. Marchesi · 2017
Earlier work this paper cites.
Large scale GAN training for high fidelity natural image synthesis
A. Brock, J. Donahue, and K. Simonyan · 2019
Earlier work this paper cites.
Improved precision and recall metric for assessing generative models
T. Kynkäänniemi, T. Karras, S. Laine, J. Lehtinen, and T. Aila · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Y. Song and S. Ermon · 2019
Earlier work this paper cites.
Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
Earlier work this paper cites.
Diffusion models beat GANs on image synthesis
P. Dhariwal and A. Nichol · 2021
Earlier work this paper cites.
Classifier-free diffusion guidance
J. Ho and T. Salimans · 2021
Earlier work this paper cites.
Gotta go fast when generating data with score-based models
A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas · 2021
Earlier work this paper cites.
Improved denoising diffusion probabilistic models
A. Nichol and P. Dhariwal · 2021
Earlier work this paper cites.
The intrinsic dimension of images and its impact on learning
P. Pope, C. Zhu, A. Abdelkader, M. Goldblum, and T. Goldstein · 2021
Earlier work this paper cites.
Denoising diffusion implicit models
J. Song, C. Meng, and S. Ermon · 2021
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2021
Cited alongside, same era.
Guidance: A cheat code for diffusion models
S. Dieleman · 2022
Cited alongside, same era.
Imagen Video: High definition video generation with diffusion models
J. Ho, W. Chan, C. Saharia, J. Whang, R. Gao, A. Gritsenko, D. P. Kingma, B. Poole, M. Norouzi, D. J. Fleet, and T. Salimans · 2022
Cited alongside, same era.
Elucidating the design space of diffusion-based generative models
T. Karras, M. Aittala, T. Aila, and S. Laine · 2022
Cited alongside, same era.
Pseudo numerical methods for diffusion models on manifolds
Pick-a-Pic: An open dataset of user preferences for text-to-image generation
Y. Kirstain, A. Polyak, U. Singer, S. Matiana, J. Penna, and O. Levy · 2023
Later among the works it cites.
Contrastive decoding: Open-ended text generation as optimization
X. L. Li, A. Holtzman, D. Fried, P. Liang, J. Eisner, T. Hashimoto, L. Zettlemoyer, and M. Lewis · 2023
Later among the works it cites.
Scalable diffusion models with transformers
W. Peebles and S. Xie · 2023
Later among the works it cites.
DreamFusion: Text-to-3D using 2D diffusion
B. Poole, A. Jain, J. T. Barron, and B. Mildenhall · 2023
Later among the works it cites.
DeepFloyd IF
Stability AI · 2023
Later among the works it cites.
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
G. Stein, J. C. Cresswell, R. Hosseinzadeh, Y. Sui, B. L. Ross, V. Villecroze, Z. Liu, A. L. Caterini, J. E. T. Taylor, and G. Loaiza-Ganem · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
L. Liu, Y. Ren, Z. Lin, and Z. Zhao · 2022
Cited alongside, same era.
Compositional visual generation with composable diffusion models
N. Liu, S. Li, Y. Du, A. Torralba, and J. B. Tenenbaum · 2022
Cited alongside, same era.
GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models
A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, and M. Chen · 2022
Cited alongside, same era.
Hierarchical text-conditional image generation with CLIP latents
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer · 2022
Cited alongside, same era.
Photorealistic text-to-image diffusion models with deep language understanding
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. Denton, S. K. S. Ghasemipour, B. K. Ayan, S. S. Mahdavi, R. G. Lopes, T. Salimans, J. Ho, D. J. Fleet, and M. Norouzi · 2022
Cited alongside, same era.
All are worth words: A ViT backbone for diffusion models
F. Bao, S. Nie, K. Xue, Y. Cao, C. Li, H. Su, and J. Zhu · 2023
Cited alongside, same era.
Later among the works it cites.
X. Wu, Y. Hao, K. Sun, Y. Chen, F. Zhu, R. Zhao, and H. Li · 2023
Later among the works it cites.
Adding conditional control to text-to-image diffusion models
L. Zhang, A. Rao, and M. Agrawala · 2023
Later among the works it cites.
Fast sampling of diffusion models with exponential integrator
Q. Zhang and Y. Chen · 2023
Later among the works it cites.
Self-rectifying diffusion sampling with perturbed-attention guidance
D. Ahn, H. Cho, J. Min, W. Jang, J. Kim, S. Kim, H. H. Park, K. H. Jin, and S. Kim · 2024
Closest in time.
Scalable high-resolution pixel-space image synthesis with hourglass diffusion transformers
K. Crowson, S. A. Baumann, A. Birch, T. M. Abraham, D. Z. Kaplan, and E. Shippole · 2024
Closest in time.
Smoothed energy guidance: Guiding diffusion models with reduced energy curvature of attention
S. Hong · 2024
Closest in time.
Analyzing and improving the training dynamics of diffusion models
T. Karras, M. Aittala, J. Lehtinen, J. Hellsten, T. Aila, and S. Laine · 2024
Closest in time.
Applying guidance in a limited interval improves sample and distribution quality in diffusion models
T. Kynkäänniemi, M. Aittala, T. Karras, S. Laine, T. Aila, and J. Lehtinen · 2024
Closest in time.
DINOv2: Learning robust visual features without supervision
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 · 2024
Closest in time.
Align your steps: Optimizing sampling schedules in diffusion models
A. Sabour, S. Fidler, and K. Kreis · 2024
Closest in time.
CADS: Unleashing the diversity of diffusion models through condition-annealed sampling
S. Sadat, J. Buhmann, D. Bradley, O. Hilliges, and R. M. Weber · 2024
Closest in time.
Analysis of classifier-free guidance weight schedulers
X. Wang, N. Dufour, N. Andreou, M.-P. Cani, V. F. Abrevaya, D. Picard, and V. Kalogeiton · 2024
Closest in time.
Characteristic guidance: Non-linear correction for diffusion model at large guidance scale
C. Zheng and Y. Lan · 2024
Closest in time.