LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
On the map: Diffusion models → Video generation
Abstract (arXiv)
This work aims to learn a high-quality text-to-video (T2V) generative model by leveraging a pre-trained text-to-image (T2I) model as a basis. It is a highly desirable yet challenging task to simultaneously a) accomplish the synthesis of visually realistic and temporally coherent videos while b) preserving the strong creative generation nature of the pre-trained T2I model. To this end, we propose LaVie, an integrated video generation framework that operates on cascaded video latent diffusion models, comprising a base T2V model, a temporal interpolation model, and a video super-resolution model. Our key insights are two-fold: 1) We reveal that the incorporation of simple temporal self-attentions, coupled with rotary positional encoding, adequately captures the temporal correlations inherent in video data. 2) Additionally, we validate that the process of joint image-video fine-tuning plays a pivotal role in producing high-quality and creative outcomes. To enhance the performance of LaVie, we contribute a comprehensive and diverse video dataset named Vimeo25M, consisting of 25 million text-video pairs that prioritize quality, diversity, and aesthetic appeal. Extensive experiments demonstrate that LaVie achieves state-of-the-art performance both quantitatively and qualitatively. Furthermore, we showcase the versatility of pre-trained LaVie models in various long video generation and personalized video synthesis applications.
Key facts. Cited by 349 papers in the map (data through 7 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 64 of its 89 references that are in the map. First posted on arXiv 26 Sep 2023.
Builds on
64 of 89 references.
Closest ancestors · 8
Same direction, newest first
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- 2022MagicVideo: Efficient Video Generation With Latent Diffusion Models20 Nov
- 2022Imagen Video: High Definition Video Generation with Diffusion Models5 Oct
- 2022Video Diffusion Models7 Apr
Other references · 56
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
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- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
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- 2020Denoising Diffusion Implicit Modelscited by 9,655
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
- 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
- 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
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- 2022LAION-5B: An open large-scale dataset for training next generation image-text modelscited by 4,131
- 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
- 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
- 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
- 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
- 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
- 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
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- 2021Image Super-Resolution via Iterative Refinementcited by 1,506
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- 2022Null-text Inversion for Editing Real Images using Guided Diffusion Modelscited by 1,168
- 2022CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformerscited by 973
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- 2017MoCoGAN: Decomposing Motion and Content for Video Generationcited by 523
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- 2016Generating Videos with Scene Dynamicscited by 376
- 2021BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignmentcited by 340
- 2021StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2cited by 300
- 2021GODIVA: Generating Open-DomaIn Videos from nAtural Descriptionscited by 254
- 2021N"UWA: Visual Synthesis Pre-training for Neural visUal World creAtioncited by 253
- 2022Long Video Generation with Time-Agnostic VQGAN and Time-Sensitive Transformercited by 244
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- 2016Temporal Generative Adversarial Nets with Singular Value Clippingcited by 198
- 2022Generating Videos with Dynamics-aware Implicit Generative Adversarial Networkscited by 186
- 2021A Good Image Generator Is What You Need for High-Resolution Video Synthesiscited by 162
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- 2021Robust Reference-based Super-Resolution via C2-Matchingcited by 68
- 2023Text2Performer: Text-Driven Human Video Generationcited by 53
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