Scalable Diffusion Models with Transformers
Open accessAbstract
We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass complexity as measured by Gflops. We find that DiTs with higher Gflops—through increased transformer depth/width or increased number of input tokens—consistently have lower FID. In addition to possessing good scalability properties, our largest DiT-XL/2 models outperform all prior diffusion models on the class-conditional ImageNet 512×512 and 256×256 benchmarks, achieving a state-of-the-art FID of 2.27 on the latter.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
MoCha is built on a diffusion transformer and effectively captures both semantics and temporal dynamics by incorporating text and speech conditions sequentially via cross-attention.
“Unlike prior works that employ text-to-image (T2I) U-Net [10, 33, 35, 40] for talking head generation, MoCha is built on a diffusion transformer (DiT) [26]. By incorporating text and speech conditions sequentially via cross-attention, it effectively captures both semantics and temporal dynamics.”
From MoCha: Towards Movie-Grade Talking Character Synthesis (2025) · cites this paper as [26]
Supervised learning uses labeled datasets to guide models towards accurate predictions and has been integral to applications including image recognition and NLP.
“Supervised learning, a foundational approach in AI, uses labeled datasets to guide models towards accurate predictions, and it has been integral to various applications, including image recognition and NLP [163], [164], [165].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [164]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930 · see citing works
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01042 · Cited by 15,880 · see citing works
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) · arXiv · doi:10.48550/arxiv.2103.00020 · Cited by 15,293 · see citing works
Decoupled Weight Decay Regularization
Loshchilov, I., & Hutter, F. (2017) · arXiv · doi:10.48550/arxiv.1711.05101 · Cited by 12,522 · see citing works
A Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T., Laine, S., & Aila, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00453 · Cited by 11,714 · see citing works
Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11239 · Cited by 8,712 · see citing works
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017) · arXiv · doi:10.48550/arxiv.1706.08500 · Cited by 8,365 · see citing works
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965 · see citing works
Large Scale GAN Training for High Fidelity Natural Image Synthesis
Brock, A., Donahue, J., & Simonyan, K. (2018) · arXiv · doi:10.48550/arxiv.1809.11096 · Cited by 3,717 · see citing works
Gaussian Error Linear Units (GELUs)
Hendrycks, D., & Gimpel, K. (2016) · arXiv · doi:10.48550/arxiv.1606.08415 · Cited by 3,537 · see citing works
Diffusion Models Beat GANs on Image Synthesis
Dhariwal, P., & Nichol, A. (2021) · arXiv · doi:10.48550/arxiv.2105.05233 · Cited by 3,362 · see citing works
Conditional Image Generation with PixelCNN DecodersPreprint
van den Oord, A., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., & Kavukcuoglu, K. (2016) · Neural Information Processing Systems · doi:10.48550/arxiv.1606.05328 · Cited by 3,066 · see citing works
Neural Discrete Representation Learning
Oord, A. V. D., Vinyals, O., & Kavukcuoglu, K. (2017) · arXiv · doi:10.48550/arxiv.1711.00937 · Cited by 2,729 · see citing works
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., & Ganguli, S. (2015) · arXiv · doi:10.48550/arxiv.1503.03585 · Cited by 2,637 · see citing works
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., et al. (2022) · arXiv · doi:10.48550/arxiv.2205.11487 · Cited by 2,528 · see citing works
Taming Transformers for High-Resolution Image Synthesis
Esser, P., Rombach, R., & Ommer, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01268 · Cited by 2,514 · see citing works
Hierarchical Text-Conditional Image Generation with CLIP Latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022) · arXiv · doi:10.48550/arxiv.2204.06125 · Cited by 2,511 · see citing works
Denoising Diffusion Implicit Models
Song, J., Meng, C., & Ermon, S. (2020) · arXiv · doi:10.48550/arxiv.2010.02502 · Cited by 2,507 · see citing works
Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., De Vries, H., Dumoulin, V., & Courville, A. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11671 · Cited by 2,490 · see citing works
Zero-shot text-to-image generation
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., & Sutskever, I. (2021) · arXiv · doi:10.48550/arxiv.2102.12092 · Cited by 2,417 · see citing works
Designing Network Design Spaces
Radosavovic, I., Kosaraju, R. P., Girshick, R., He, K., & Dollar, P. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01044 · Cited by 2,084 · see citing works
Scaling Laws for Neural Language Models
Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., & Amodei, D. (2020) · arXiv · Cited by 1,749 · cited 1× · see citing works
“They have shown remarkable scaling properties under increasing model size, training compute and data in the language domain [26], as generic autoregressive models [17] and as ViTs [63].”
BackgroundConditional Image Generation with PixelCNN DecodersPreprint
van den Oord, A., Kalchbrenner, N., Vinyals, O., et al. (2016) · arXiv (Cornell University) · Cited by 1,586 · see citing works
Improved Denoising Diffusion Probabilistic Models
Nichol, A., & Dhariwal, P. (2021) · arXiv · doi:10.48550/arxiv.2102.09672 · Cited by 1,550 · see citing works
Generative Modeling by Estimating Gradients of the Data Distribution
Song, Y., & Ermon, S. (2019) · arXiv · doi:10.48550/arxiv.1907.05600 · Cited by 1,453 · see citing works
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., & Chen, M. (2021) · arXiv · doi:10.48550/arxiv.2112.10741 · Cited by 1,357 · see citing works
Classifier-Free Diffusion Guidance
Ho, J., & Salimans, T. (2022) · arXiv · doi:10.48550/arxiv.2207.12598 · Cited by 1,126 · see citing works
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, Ł., Shazeer, N., Ku, A., & Tran, D. (2018) · arXiv · doi:10.48550/arxiv.1802.05751 · Cited by 1,085 · see citing works
Generating Long Sequences with Sparse Transformers
Child, R., Gray, S., Radford, A., & Sutskever, I. (2019) · arXiv · doi:10.48550/arxiv.1904.10509 · Cited by 1,063 · see citing works
(2020) · International Conference on Machine Learning · Cited by 1,012 · see citing works
Offline Reinforcement Learning as One Big Sequence Modeling ProblemPreprint
Janner, M., Li, Q.-Y., & Levine, S. (2021) · Neural Information Processing Systems · doi:10.48550/arxiv.2106.02039 · Cited by 968 · see citing works
Zhai, X., Kolesnikov, A., Houlsby, N., & Beyer, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01179 · Cited by 891 · see citing works
Estimation of Non-Normalized Statistical Models by Score Matching
Hyvärinen, A. (2005) · doi:10.5555/1046920.1088696 · Cited by 756 · see citing works
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2021) · arXiv · doi:10.48550/arxiv.2112.10752 · Cited by 737 · see citing works
Vector Quantized Diffusion Model for Text-to-Image Synthesis
Gu, S., Chen, D., Bao, J., Wen, F., Zhang, B., Chen, D., Yuan, L., & Guo, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01043 · Cited by 720 · see citing works
Elucidating the Design Space of Diffusion-Based Generative Models
Karras, T., Aittala, M., Aila, T., & Laine, S. (2022) · neural information processing systems · doi:10.52202/068431-1926 · Cited by 702 · see citing works
Decision Transformer: Reinforcement Learning via Sequence ModelingPreprint
Chen, L.-L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Abbeel, P., Srinivas, A., & Mordatch, I. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.01345 · Cited by 677 · see citing works
Salimans, T., Karpathy, A., Chen, X., & Kingma, D. P. (2017) · arXiv · doi:10.48550/arxiv.1701.05517 · Cited by 584 · see citing works
Early Convolutions Help Transformers See BetterPreprint
Xiao, T., Singh, M., Mintun, E., Darrell, T., Dollár, P., & Girshick, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.14881 · Cited by 565 · see citing works
Cascaded Diffusion Models for High Fidelity Image Generation
Ho, J., Saharia, C., Chan, W., Fleet, D. J., Norouzi, M., & Salimans, T. (2021) · arXiv · doi:10.48550/arxiv.2106.15282 · Cited by 514 · see citing works
MaskGIT: Masked Generative Image Transformer
Chang, H., Zhang, H., Jiang, L., Liu, C., & Freeman, W. T. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01103 · Cited by 446 · see citing works
Scaling Autoregressive Models for Content-Rich Text-to-Image Generation
Yu, J., Xu, Y., Koh, J. Y., Luong, T., Baid, G., Wang, Z., Vasudevan, V., Ku, A., Yang, Y., Ayan, B. K., et al. (2022) · arXiv · doi:10.48550/arxiv.2206.10789 · Cited by 371 · see citing works
StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets
Sauer, A., Schwarz, K., & Geiger, A. (2022) · Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings · doi:10.1145/3528233.3530738 · Cited by 353 · see citing works
Improved Precision and Recall Metric for Assessing Generative ModelsPreprint
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., & Aila, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.06991 · Cited by 329 · see citing works
On Aliased Resizing and Surprising Subtleties in GAN Evaluation
Parmar, G., Zhang, R., & Zhu, J.-Y. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01112 · Cited by 307 · see citing works
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
Steiner, A., Kolesnikov, A., Zhai, X., Wightman, R., Uszkoreit, J., & Beyer, L. (2021) · arXiv · doi:10.48550/arxiv.2106.10270 · Cited by 292 · see citing works
Scaling Laws for Autoregressive Generative Modeling
Henighan, T., Kaplan, J., Katz, M., Chen, M., Hesse, C., Jackson, J., Jun, H., Brown, T. B., Dhariwal, P., Gray, S., et al. (2020) · arXiv · doi:10.48550/arxiv.2010.14701 · Cited by 150 · see citing works
On Network Design Spaces for Visual Recognition
Radosavovic, I., Johnson, J., Xie, S., Lo, W.-Y., & Dollar, P. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00197 · Cited by 127 · see citing works
Self-citationGenerating Images with Sparse RepresentationsPreprint
Nash, C., Menick, J., Dieleman, S., & Battaglia, P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.03841 · Cited by 86 · see citing works
Scalable Adaptive Computation for Iterative GenerationPreprint
Jabri, A., Fleet, D. J., & Chen, T. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2212.11972 · Cited by 20 · see citing works
Learning to Learn with Generative Models of Neural Network CheckpointsPreprint
Peebles, W., Radosavovic, I., Brooks, T., Efros, A. A., & Malik, J. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2209.12892 · Cited by 8 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Diffusion models in medical imaging: A comprehensive survey
Kazerouni, A., Aghdam, E. K., Heidari, M., Azad, R., Fayyaz, M., Hacihaliloglu, I., & Merhof, D. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102846 · Cited by 850 · see citing works
Structured 3D Latents for Scalable and Versatile 3D Generation
Xiang, J., Lv, Z., Xu, S., Yu, D., Wang, R., Zhang, B., Chen, D., Tong, X., & Yang, J. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02000 · Cited by 318 · see citing works
Humans in 4D: Reconstructing and Tracking Humans with Transformers
Goel, S., Pavlakos, G., Rajasegaran, J., Kanazawa, A., & Malik, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01358 · Cited by 283 · see citing works
Adversarial Diffusion Distillation
Sauer, A., Lorenz, D., Blattmann, A., & Rombach, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73016-0_6 · Cited by 306 · see citing works
The Stable Signature: Rooting Watermarks in Latent Diffusion Models
Fernandez, P., Couairon, G., Jégou, H., Douze, M., & Furon, T. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02053 · Cited by 251 · see citing works
Foundation Models for Time Series Analysis: A Tutorial and Survey
Liang, Y., Wen, H., Nie, Y., Jiang, Y., Jin, M., Song, D., Pan, S., & Wen, Q. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671451 · Cited by 273 · see citing works
SVDiff: Compact Parameter Space for Diffusion Fine-Tuning
Han, L., Li, Y., Zhang, H., Milanfar, P., Metaxas, D., & Yang, F. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00673 · Cited by 224 · see citing works
Text-to-3D using Gaussian Splatting
Chen, Z., Wang, F., Wang, Y., & Liu, H. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02022 · Cited by 202 · see citing works
Transformers without Normalization
Zhu, J., Chen, X., He, K., LeCun, Y., & Liu, Z. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01388 · Cited by 185 · see citing works
Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models
Luo, Z., Gustafsson, F., Zhao, Z., Sjölund, J., & Schön, T. B. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00169 · Cited by 168 · see citing works
LaVie: High-Quality Video Generation with Cascaded Latent Diffusion Models
Wang, Y., Chen, X., Ma, X., Zhou, S., Huang, Z., Yang, C., Yang, C., He, Y., Yu, J., Yang, P., et al. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02295-1 · Cited by 218 · see citing works
Your Diffusion Model is Secretly a Zero-Shot Classifier
Li, A. C., Prabhudesai, M., Duggal, S., Brown, E. V., & Pathak, D. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00210 · Cited by 159 · see citing works
Q-Diffusion: Quantizing Diffusion Models
Li, X., Liu, Y., Lian, L., Yang, H., Dong, Z., Kang, D. D., Zhang, S., & Keutzer, K. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01608 · Cited by 156 · see citing works
PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding
Li, Z., Cao, M., Wang, X., Qi, Z., Cheng, M., & Shan, Y. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00825 · Cited by 161 · see citing works
Zou, Z., Yu, Z., Guo, Y., Li, Y., Ding, L., Cao, Y., & Zhang, S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00983 · Cited by 155 · see citing works
Intriguing properties of synthetic images: from generative adversarial networks to diffusion models
Corvi, R., Cozzolino, D., Poggi, G., Nagano, K., & Verdoliva, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00104 · Cited by 142 · see citing works
Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., Boesel, F., et al. (2024) · arXiv · doi:10.48550/arxiv.2403.03206 · Cited by 140 · see citing works
ResDiff: Combining CNN and Diffusion Model for Image Super-resolution
Shang, S., Shan, Z., Liu, G., Wang, L., Wang, X., Zhang, Z., & Zhang, J. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28746 · Cited by 151 · see citing works
A Survey on Video Diffusion Models
Xing, Z., Feng, Q., Chen, H., Dai, Q., Hu, H., Xu, H., Wu, Z., & Jiang, Y. (2024) · ACM Computing Surveys · doi:10.1145/3696415 · Cited by 159 · see citing works
Raising the Bar of AI-generated Image Detection with CLIP
Cozzolino, D., Poggi, G., Corvi, R., Nießner, M., & Verdoliva, L. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00439 · Cited by 147 · see citing works
Protenix - Advancing Structure Prediction Through a Comprehensive AlphaFold3 ReproductionPreprint
ByteDance AML AI4Science Team, Chen, X., Zhang, Y., Lu, C., Ma, W., Guan, J., Gong, C., Yang, J., Zhang, H., Zhang, K., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.08.631967 · Cited by 150 · see citing works
Sapiens: Foundation for Human Vision Models
Khirodkar, R., Bagautdinov, T., Martinez, J., Zhaoen, S., James, A., Selednik, P., Anderson, S., & Saito, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73235-5_12 · Cited by 139 · see citing works
State of the Art on Diffusion Models for Visual Computing
Po, R., Yifan, W., Golyanik, V., Aberman, K., Barron, J. T., Bermano, A. H., Chan, E. R., Dekel, T., Holynski, A., Kanazawa, A., et al. (2024) · Computer Graphics Forum · doi:10.1111/cgf.15063 · Cited by 156 · see citing works
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Grauman, K. L., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V. B., Bansal, S., Boote, B., et al. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01834 · Cited by 159 · see citing works
Atom-level enzyme active site scaffolding using RFdiffusion2
Ahern, W., Yim, J., Tischer, D., Salike, S., Woodbury, S. M., Kim, D., Kalvet, I., Kipnis, Y., Coventry, B., Altae-Tran, H., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02975-x · Cited by 131 · see citing works
Efficient Diffusion Training via Min-SNR Weighting Strategy
Hang, T., Gu, S., Li, C., Bao, J., Chen, D., Hu, H., Geng, X., & Guo, B. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00684 · Cited by 123 · see citing works
PIXART-$$\Sigma $$: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation
Chen, J., Ge, C., Xie, E., Wu, Y., Yao, L., Ren, X., Wang, Z., Luo, P., Lu, H., & Li, Z. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73411-3_5 · Cited by 123 · see citing works
SiT: Exploring Flow and Diffusion-Based Generative Models with Scalable Interpolant Transformers
Ma, N., Goldstein, M., Albergo, M. S., Boffi, N. M., Vanden-Eijnden, E., & Xie, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72980-5_2 · Cited by 123 · see citing works
Self-citationQian, Q., Luo, J. T., & Qin, Y. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2024.3408058 · Cited by 108 · see citing works
From Large Language Models to Large Multimodal Models: A Literature Review
Huang, D., Yan, C., Li, Q., & Peng, X. (2024) · Applied Sciences · doi:10.3390/app14125068 · Cited by 110 · see citing works
McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
“Generative AI model architectures have seen significant developments, with four key domains standing out: • Transformer Models: Transformer models have signifi-cantly revolutionized the field of AI, especially in NLP, due to their higher efficiency and scalability [139], [140], [141].”
BackgroundFederated Recommender System Based on Diffusion Augmentation and Guided Denoising
Di, Y., Shi, H., Wang, X., Ma, R., & Liu, Y. (2024) · ACM Transactions on Information Systems · doi:10.1145/3688570 · Cited by 102 · see citing works
ZigMa: A DiT-style Zigzag Mamba Diffusion Model
Hu, V. T., Baumann, S. A., Gui, M., Grebenkova, O., Ma, P., Fischer, J., & Ommer, B. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72664-4_9 · Cited by 105 · see citing works
TrafficFormer: An Efficient Pre-trained Model for Traffic Data
Zhou, G., Guo, X., Liu, Z., Li, T., Li, Q., & Xu, K. (2025) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp61157.2025.00102 · Cited by 98 · see citing works
On the design fundamentals of diffusion models: A survey
Chang, Z., Koulieris, G. A., Chang, H. J., & Shum, H. P. H. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111934 · Cited by 117 · see citing works
Tian, L., Wang, Q. J., Zhang, B., & Bo, L. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73010-8_15 · Cited by 112 · see citing works
Comprehensive exploration of diffusion models in image generation: a survey
Chen, H., Xiang, Q., Hu, J., Ye, M., Yu, C., Cheng, H., & Zhang, L. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11110-3 · Cited by 104 · see citing works
Masked Diffusion Transformer is a Strong Image Synthesizer
Gao, S., Zhou, P., Cheng, M.-M., & Yan, S. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02117 · Cited by 91 · see citing works
CRM: Single Image to 3D Textured Mesh with Convolutional Reconstruction Model
Wang, Z., Wang, Y., Chen, Y., Xiang, C., Chen, S., Yu, D., Li, C., Su, H., & Zhu, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72751-1_4 · Cited by 102 · see citing works
Residential floor plans: Multi-conditional automatic generation using diffusion models
Zeng, P., Gao, W., Yin, J., Xu, P., & Lu, S. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105374 · Cited by 86 · see citing works
Photorealistic Video Generation with Diffusion Models
Gupta, A., Yu, L., Sohn, K., Gu, X., Hahn, M., Fei-Fei, L., Essa, I. A., Jiang, L., & Lezama, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72986-7_23 · Cited by 112 · see citing works
Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications
Kawaharazuka, K., Oh, J., Yamada, J., Posner, I., & Zhu, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3609980 · Cited by 95 · see citing works
Analyzing and Improving the Training Dynamics of Diffusion Models
Karras, T., Aittala, M., Lehtinen, J., Hellsten, J., Aila, T., & Laine, S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02282 · Cited by 90 · see citing works
Improving Sample Quality of Diffusion Models Using Self-Attention Guidance
Hong, S., Lee, G., Jang, W., & Kim, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00686 · Cited by 83 · see citing works
Online Detection of AI-Generated Images
Epstein, D. C., Jain, I., Wang, O., & Zhang, R. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00045 · Cited by 83 · see citing works
OmniGen: Unified Image Generation
Xiao, S., Wang, Y., Zhou, J., Yuan, H., Xing, X., Yan, R., Li, C., Wang, S., Huang, T., & Liu, Z. (2025) · 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52734.2025.01241 · Cited by 75 · see citing works
Gong, H., Kang, L., Wang, Y., Wang, Y., Wan, X., Wu, X., & Li, H. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10980694 · Cited by 78 · see citing works
Evans, Z., Parker, J. D., Carr, C., Zukowski, Z., Taylor, J., & Pons, J. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888461 · Cited by 73 · see citing works
Alimisis, P., Mademlis, I., Grammatikis, P. I. R., Sarigiannidis, P. G., & Papadopoulos, G. T. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11116-x · Cited by 77 · see citing works
EchoMimic: Lifelike Audio-Driven Portrait Animations through Editable Landmark Conditions
Chen, Z., Cao, J., Chen, Z., Li, Y., & Ma, C. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i3.32241 · Cited by 78 · see citing works
He, Z., Wang, Y.-H., & Zhang, J. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102654 · Cited by 78 · see citing works
When Does Sora Show: The Beginning of TAO to Imaginative Intelligence and Scenarios Engineering
Wang, F., Miao, Q., Li, L., Ni, Q., Li, X., Li, J., Fan, L., Tian, Y., & Han, Q. (2024) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2024.124383 · Cited by 64 · see citing works
Changen2: Multi-Temporal Remote Sensing Generative Change Foundation Model
Zheng, Z., Ermon, S., Kim, D., Zhang, L., & Zhong, Y. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3475824 · Cited by 67 · see citing works
Harnessing large vision and language models in agriculture: a review
Zhu, H., Qin, S., Su, M., Lin, C., Li, A., & Gao, J. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1579355 · Cited by 83 · see citing works
Denoising Diffusion Autoencoders are Unified Self-supervised Learners
Xiang, W., Hongyu, Y., Huang, D., & Wang, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01448 · Cited by 63 · see citing works
Transferable deep generative modeling of intrinsically disordered protein conformations
Janson, G., & Feig, M. (2024) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1012144 · Cited by 62 · see citing works
Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives
Liu, Y., Yue, J., Xia, S., Ghamisi, P., Xie, W., & Fang, L. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3464685 · Cited by 64 · see citing works
MWFormer: Multi-Weather Image Restoration Using Degradation-Aware Transformers
Zhu, R., Tu, Z., Liu, J., Bovik, A. C., & Fan, Y. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3501855 · Cited by 60 · see citing works
Foundation models and intelligent decision-making: Progress, challenges, and perspectives
Huang, J., Xu, Y., Wang, Q., Wang, Q., Liang, X., Wang, F., Zhang, Z., Wei, W., Zhang, B., Huang, L., et al. (2025) · The Innovation · doi:10.1016/j.xinn.2025.100948 · Cited by 76 · see citing works
Zheng, K. P., Sun, Z., Song, Y. L., Zhang, C., Zhang, C., Chang, F., Yang, D., & Fu, X. (2025) · Energies · doi:10.3390/en18030503 · Cited by 61 · see citing works
Xie, E., Yao, L., Shi, H., Liu, Z., Zhou, D., Liu, Z., Li, J., & Li, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00390 · Cited by 62 · see citing works
Towards Large-Scale 3D Representation Learning with Multi-Dataset Point Prompt Training
Wu, X., Tian, Z., Wen, X., Peng, B., Liu, X., Yu, K., & Zhao, H. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01849 · Cited by 59 · see citing works
DiffiT: Diffusion Vision Transformers for Image Generation
Hatamizadeh, A., Song, J., Liu, G., Kautz, J., & Vahdat, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73242-3_3 · Cited by 63 · see citing works
The role of large language models in agriculture: harvesting the future with LLM intelligence
Shaikh, T. A., Rasool, T., Veningston, K., & Yaseen, S. M. (2024) · Progress in Artificial Intelligence · doi:10.1007/s13748-024-00359-4 · Cited by 61 · see citing works
Kandinsky: An Improved Text-to-Image Synthesis with Image Prior and Latent Diffusion
Razzhigaev, A., Shakhmatov, A., Maltseva, A., Arkhipkin, V., Pavlov, I., Ryabov, I., Kuts, A. L., Panchenko, A., Кузнецов, А. В., & Dimitrov, D. (2023) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2023.emnlp-demo.25 · Cited by 53 · see citing works
Chen, K., Han, T., Ling, F., Gong, J., Bai, L., Wang, X., Luo, J., Fei, B., Zhang, W., Chen, X., et al. (2025) · Communications Earth & Environment · doi:10.1038/s43247-025-02502-y · Cited by 52 · see citing works
Toward Real-World Blind Face Restoration With Generative Diffusion Prior
Chen, X., Tan, J., Wang, T., Zhang, K., Luo, W., & Cao, X. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3383659 · Cited by 48 · see citing works
Track2Act: Predicting Point Tracks from Internet Videos Enables Generalizable Robot Manipulation
Bharadhwaj, H., Mottaghi, R., Gupta, A., & Tulsiani, S. (2024) · European Conference on Computer Vision · doi:10.1007/978-3-031-73116-7_18 · Cited by 200 · see citing works
StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal
Ye, C., Qiu, L., Gu, X., Zuo, Q., Wu, Y., Dong, Z., Bo, L., Xiu, Y., & Han, X. (2024) · ACM Transactions on Graphics · doi:10.1145/3687971 · Cited by 45 · see citing works
Automated residential layout generation and editing using natural language and images
Zeng, P., Gao, W., Li, J., Yin, J., Chen, J., & Lu, S. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106133 · Cited by 45 · see citing works
GenTron: Diffusion Transformers for Image and Video Generation
Chen, S., Xu, M., Ren, J., Cong, Y., He, S., Xie, Y., Sinha, A., Luo, P., Xiang, T., & Pérez-Rúa, J.-M. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00616 · Cited by 48 · see citing works
MobileDiffusion: Instant Text-to-Image Generation on Mobile Devices
Zhao, Y., Xu, Y., Xiao, Z., Jia, H., & Hou, T. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73033-7_13 · Cited by 51 · see citing works
Resource-efficient Algorithms and Systems of Foundation Models: A Survey
Xu, M., Cai, D., Yin, W., Wang, S., Jin, X., & Liu, X. (2024) · ACM Computing Surveys · doi:10.1145/3706418 · Cited by 152 · see citing works
Diffusion Model is Secretly a Training-Free Open Vocabulary Semantic Segmenter
Wang, J., Li, X., Zhang, J., Xu, Q., Zhou, Q., Yu, Q., Sheng, L., & Xu, D. (2025) · IEEE Transactions on Image Processing · doi:10.1109/tip.2025.3551648 · Cited by 55 · see citing works
A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
Yang, Y., Jin, M., Wen, H., Zhang, C., Liang, Y., Ma, L., Wang, Y., Liu, C., Yang, B., Xu, Z., et al. (2025) · ACM Computing Surveys · doi:10.1145/3783986 · Cited by 107 · see citing works
Generative AI in depth: A survey of recent advances, model variants, and real-world applications
Yazdani, S., Singh, A., Saxena, N., Wang, Z., Palikhe, A., Pan, D., Pal, U., Yang, J., & Zhang, W. (2025) · Journal of Big Data · doi:10.1186/s40537-025-01247-x · Cited by 40 · see citing works
The Crack Diffusion Model: An Innovative Diffusion-Based Method for Pavement Crack Detection
Zhang, H., Chen, N., Li, M., & Mao, S. (2024) · Remote Sensing · doi:10.3390/rs16060986 · Cited by 40 · see citing works
A Comprehensive Survey of Image Generation Models Based on Deep Learning
Li, J., Zhang, C., Zhu, W., & Ren, Y. (2024) · Annals of Data Science · doi:10.1007/s40745-024-00544-1 · Cited by 42 · see citing works
A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling
Qu, W., Shao, Y., Meng, L., Huang, X., & Xiao, L. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01964 · Cited by 44 · see citing works
Efficient Diffusion Models: A Comprehensive Survey From Principles to Practices
Ma, Z., Zhang, Y., Jia, G., Zhao, L., Ma, Y., Ma, M., Liu, G., Zhang, K., Ding, N., Li, J., et al. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3569700 · Cited by 46 · see citing works
Shu, Q., Zhu, X., Xu, S., Wang, Y., & Liu, D. (2025) · Remote Sensing of Environment · doi:10.1016/j.rse.2025.114872 · Cited by 40 · see citing works
stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics
Li, K., Li, J., Tao, Y., & Wang, F. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae171 · Cited by 37 · see citing works
Generative AI Enables EEG Super-Resolution via Spatio-Temporal Adaptive Diffusion Learning
Wang, S., Zhou, T., Shen, Y., Li, Y., Huang, G., & Hu, Y. S. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3528438 · Cited by 39 · see citing works
Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality
Lorenz, P., Durall, R., & Keuper, J. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00051 · Cited by 38 · see citing works
Retracted: Sora for Scenarios Engineering of Intelligent Vehicles: V&V, C&C, and Beyonds
Li, X., Miao, Q., Li, L., Ni, Q., Fan, L., Wang, Y., Tian, Y., & Wang, F. (2024) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2024.3379989 · Cited by 38 · see citing works
RetractedVideo and Audio Deepfake Datasets and Open Issues in Deepfake Technology: Being Ahead of the Curve
Akhtar, Z., Pendyala, T. L., & Athmakuri, V. S. (2024) · Forensic Sciences · doi:10.3390/forensicsci4030021 · Cited by 39 · see citing works
Generating realistic neurophysiological time series with denoising diffusion probabilistic models
Vetter, J., Macke, J. H., & Gao, R. (2024) · Patterns · doi:10.1016/j.patter.2024.101047 · Cited by 40 · see citing works
Diffusion Models for Medical Image Computing: A Survey
Shi, Y., Abulizi, A., Wang, H., Feng, K., Abudukelimu, N., Su, Y., & Abudukelimu, H. (2024) · Tsinghua Science & Technology · doi:10.26599/tst.2024.9010047 · Cited by 39 · see citing works
PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generation
Liu, S., Ren, Z., Gupta, S., & Wang, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73007-8_21 · Cited by 37 · see citing works
SpaDiT: diffusion transformer for spatial gene expression prediction using scRNA-seq
Li, X., Zhu, F., & Min, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae571 · Cited by 38 · see citing works
Machine learning for modelling unstructured grid data in computational physics: A review
Cheng, S. L., Bocquet, M., Ding, W., Finn, T. S., Fu, R., Fu, J., Guo, Y., Johnson, E., Li, S., Liu, C., et al. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103255 · Cited by 40 · see citing works
Wang, H., Liu, Z. Q., Sun, K., Wang, X., Shen, D. G., & Cui, Z. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3585372 · Cited by 40 · see citing works
Precipitation Nowcasting Using Diffusion Transformer With Causal Attention
Li, C., Ling, X., Xue, Y., Luo, W., Zhu, L., Qin, F., Zhou, Y., & Huang, Y. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3510693 · Cited by 35 · see citing works
Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures
Sordo, Z., Chagnon, E., Hu, Z., Donatelli, J. J., Andeer, P. F., Nico, P., Northen, T. R., & Ushizima, D. M. (2025) · Journal of Imaging · doi:10.3390/jimaging11080252 · Cited by 41 · see citing works
Language Modeling Materializes a World Model of Protein BiologyPreprint
Candido, S., Hayes, T., Derry, A., Rao, R., Lin, Z., Verkuil, R., Wu, B. Z., Lee, J. S., Bruguera, E. S., Keval, J. A., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.03.729735 · Cited by 65 · see citing works
Diffusion Models Without Attention
Yan, J. N., Gu, J., & Rush, A. M. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00787 · Cited by 37 · see citing works
Stable Diffusion Segmentation for Biomedical Images with Single-Step Reverse Process
Lin, T., Chen, Z., Yan, Z., Yu, W., & Zheng, F. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72111-3_62 · Cited by 34 · see citing works
Dong, S., Wang, S., Liu, S., Cai, L., Fan, Q., Kannala, J., & Yang, Y. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01560 · Cited by 37 · see citing works
Zhao, L., Lyu, R., Lei, H., Lin, Q., Zhou, A., Ma, H., Wang, J., Meng, X., Shao, C., Tang, Y., et al. (2024) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3678550 · Cited by 33 · see citing works
Adversarial attacks and defenses on text-to-image diffusion models: A survey
Zhang, C., Hu, M., Li, W., & Wang, L. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102701 · Cited by 33 · see citing works
- Authors
- Peebles, W., & Xie, S.
- Affiliations
- University of California, Berkeley (US)
- New York University (US)
- ORCID
- Xie, Saining
- Venue
- 2023 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2023-10-01
- Pages
- 4172-4182
- OpenAlex
- W4390872297
- Semantic Scholar
- 736973165f98105fec3729b7db414ae4d80fcbeb
- Cited by
- 2,284
- References
- 59
- Citations by year
- 2026: 1007 · 2025: 925 · 2024: 315 · 2023: 37
- FWCI
- 244.26
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Keywords
- diffusion models, latent diffusion models, diffusion transformer, scalability, image generation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
