Latte: Latent Diffusion Transformer for Video Generation
On the map: Diffusion models → Video generation
Abstract (arXiv)
We propose Latte, a novel Latent Diffusion Transformer for video generation. Latte first extracts spatio-temporal tokens from input videos and then adopts a series of Transformer blocks to model video distribution in the latent space. In order to model a substantial number of tokens extracted from videos, four efficient variants are introduced from the perspective of decomposing the spatial and temporal dimensions of input videos. To improve the quality of generated videos, we determine the best practices of Latte through rigorous experimental analysis, including video clip patch embedding, model variants, timestep-class information injection, temporal positional embedding, and learning strategies. Our comprehensive evaluation demonstrates that Latte achieves state-of-the-art performance across four standard video generation datasets, i.e., FaceForensics, SkyTimelapse, UCF101, and Taichi-HD. In addition, we extend Latte to the text-to-video generation (T2V) task, where Latte achieves results that are competitive with recent T2V models. We strongly believe that Latte provides valuable insights for future research on incorporating Transformers into diffusion models for video generation.
Key facts. Cited by 536 papers in the map (data through 7 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 72 of its 95 references that are in the map. First posted on arXiv 5 Jan 2024.
Builds on
72 of 95 references.
Closest ancestors · 12
Same direction, newest first
- 2023Photorealistic Video Generation with Diffusion Models11 Dec
- 2023Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation7 Dec
- 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets25 Nov
- 2023SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction31 Oct
- 2023VDT: General-purpose Video Diffusion Transformers via Mask Modeling22 May
- 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models18 Apr
- 2023VideoFusion: Decomposed Diffusion Models for High-Quality Video Generation15 Mar
- 2023Video Probabilistic Diffusion Models in Projected Latent Space15 Feb
- 2022VIDM: Video Implicit Diffusion Models1 Dec
- 2022Latent Video Diffusion Models for High-Fidelity Long Video Generation23 Nov
- 2022Flexible Diffusion Modeling of Long Videos23 May
- 2022Video Diffusion Models7 Apr
Other references · 60
- 2017Attention Is All You Needcited by 59,932
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2020Denoising Diffusion Implicit Modelscited by 9,655
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
- 2020Scaling Laws for Neural Language Modelscited by 6,411
38 more
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
- 2022Scalable Diffusion Models with Transformerscited by 5,931
- 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
- 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,581
- 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
- 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
- 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,286
- 2021SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equationscited by 1,903
- 2023VBench: Comprehensive Benchmark Suite for Video Generative Modelscited by 1,755
- 2022Make-A-Video: Text-to-Video Generation without Text-Video Datacited by 1,725
- 2021ViViT: A Video Vision Transformercited by 1,610
- 2021Is Space-Time Attention All You Need for Video Understanding?cited by 1,609
- 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
- 2018Towards Accurate Generative Models of Video: A New Metric & Challengescited by 1,204
- 2021Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrievalcited by 1,175
- 2014Two-Stream Convolutional Networks for Action Recognition in Videoscited by 1,067
- 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 973
- 2023PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesiscited by 854
- 2018Image Transformercited by 770
- 2020First Order Motion Model for Image Animationcited by 698
- 2023Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creationcited by 684
- 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 651
- 2022Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generationcited by 597
- 2017MoCoGAN: Decomposing Motion and Content for Video Generationcited by 523
- 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
- 2023Zero-shot Image-to-Image Translationcited by 503
- 2022All are Worth Words: A ViT Backbone for Diffusion Modelscited by 498
- 2023VideoPoet: A Large Language Model for Zero-Shot Video Generationcited by 479
- 2016Generating Videos with Scene Dynamicscited by 376
- 2021StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2cited by 300
- 20223D Neural Field Generation using Triplane Diffusioncited by 274
- 2021Video Transformer Networkcited by 271
- 2022Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformercited by 244
- 2016Temporal Generative Adversarial Nets with Singular Value Clippingcited by 198
- 2019Scaling Autoregressive Video Modelscited by 189
- 2022Generating Videos with Dynamics-aware Implicit Generative Adversarial Networkscited by 186
- 2022EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equationscited by 172
- 2021A Good Image Generator Is What You Need for High-Resolution Video Synthesiscited by 162
50 most cited of 60 All 60 (JSON)
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