Latent Video Diffusion Models

Updated 30 June 2025

Latent video diffusion models are generative frameworks that compress videos into latent spaces using autoencoders for efficient synthesis.
They combine VAE-based encoding with denoising diffusion probabilistic models to achieve scalable, high-fidelity, and temporally consistent video generation.
Applications include text-to-video synthesis, video editing, and efficient streaming, significantly reducing computational and memory requirements.

Latent video diffusion models are a class of generative video models that synthesize or modify videos by learning a diffusion process in a compressed latent space, rather than directly in the pixel domain. These models combine a powerful autoencoder—often a variational autoencoder (VAE) or similar structure—for dimensionality reduction, with a denoising diffusion probabilistic model (DDPM) that learns to stochastically transform noise into structured representations. This approach enables efficient, scalable, and high-fidelity video generation, supporting tasks such as text-to-video, image-to-video, video editing, and more.

1. Latent Diffusion in Video: Principle and Motivation

Traditional video generation with pixel-space diffusion models is computationally expensive, as the number of variables scales cubically with video length and resolution. Latent video diffusion models address this by first compressing video clips into a significantly lower-dimensional latent space using a VAE (or similar autoencoding scheme) and then training a diffusion model to learn and generate sequences in this space (2211.11018, 2211.13221, 2302.07685).

Given a video $\mathbf{X}=[x^1,...,x^F]$ , the VAE encoder $\mathcal{E}$ produces latent codes $\mathbf{Z} = [\mathcal{E}(x^1), ..., \mathcal{E}(x^F)]$ . The diffusion process operates on these latents using the standard forward and reverse steps: $z_t = \sqrt{\bar{\alpha}_t}z_0 + \sqrt{1-\bar{\alpha}_t}\epsilon, \;\; \epsilon \sim \mathcal{N}(0, I)$ The DDPM's denoiser $\epsilon_\theta$ is trained to predict and remove the noise, solving

$\mathbb{E}_{t}\sum_{i=1}^F \| z^i_0 - \epsilon_\theta(z^i_t, t, c) \|_2^2$

where $c$ denotes conditioning information such as text embedding, prior frames, or other modalities.

This paradigm reduces memory, computation, and data requirements, enabling the training of high-resolution, long-duration video models on affordable hardware.

2. Architectures and Latent Space Designs

Latent video diffusion models differ in their architecture for both encoding (VAE) and the diffusion process:

Spatial-only VAEs compress each frame independently (e.g., 2D VAEs), while
Spatiotemporal VAEs employ 3D convolutions or transformer-based solutions to compress space and time jointly, thus removing redundancy across frames (2211.13221, 2409.01199, 2411.06449).

Advanced architectures include:

Triplane/Factorized representations, which decompose a video’s latent space into multiple 2D planes, drastically reducing dimensionality and computational load (2302.07685).
Frame-wise adaptors and temporal attention, such as causal temporal self-attention layers and lightweight adaptors, are applied to 2D image-based U-Net backbones to capture temporal dependencies without redundant 3D convolutions (2211.11018).
Transformer backbones for joint spatial-temporal modeling in latent space, offering efficient, scalable long-range context modeling (2401.03048).

Decoder improvements address spatial and temporal inconsistencies by adding temporal attention in the decoder (“VideoVAE”) (2211.11018), group causal convolutions for balanced temporal-spatial encoding (2411.06449), or wavelet-driven energy flow for efficient multi-frequency representation (2411.17459).

3. Efficiency, Scalability, and Practical Benchmarking

Operating in latent space provides:

Resource efficiency: Orders-of-magnitude reductions in FLOPs and memory usage compared to pixel-space diffusion (e.g., $\sim$ 64 $\times$ less computation than VDM (2211.11018), $6$– $20\times$ faster sampling (2211.13221)).
High-resolution and long-sequence capability: Models can train and sample at $256\times256$ (or higher) and for thousands of frames on commodity GPUs.
Streaming and bandwidth applications: Compressing and transmitting only latent keyframes significantly reduces storage and transmission load, enabling efficient streaming with semantic-aware error recovery using LDM denoising and VFI at the receiver (2502.05695).
Real-time generation: Transformer-based LDMs with extreme compression (e.g., $1:192$ ratio) achieve sub-real-time video synthesis at megapixel resolutions (2501.00103).

A summary comparison is shown below:

Model/Backbone	Compression	Notable Features/Results
MagicVideo	$8\times$	Efficient 3D U-Net, directed temporal attention, $64\times$ faster than VDM (2211.11018)
PVDM	Triplane	2D U-Net in projected plane, linear scaling, SOTA FVD (639.7) (2302.07685)
OD-VAE	$4\times8\times8$	Temporal-spatial omnidimensional compression (2409.01199)
WF-VAE	wavelet	Multi-level wavelet, $2\times$ throughput, $4\times$ lower memory (2411.17459)
LTX-Video	$32\times32\times8$	Holistic VAE-transformer, patchify on VAE input, real-time at $768\times512$ (2501.00103)

4. Temporal Consistency and Long-Video Generation

Ensuring temporal coherence is central in video synthesis. Innovations include:

Directed/casual temporal attention: Each frame attends only to itself and prior frames, preventing flow of information from future to past and reducing visible time leaks (2211.11018).
Hierarchical latent prediction and interpolation: For long videos, generate sparse keyframes and fill intermediate frames by latent interpolation, which improves consistency and reduces accumulation of errors (2211.13221).
Motion-aware or explicit motion representation: Separating content and motion via latent decompositions enables more controllable and stable long-term synthesis (2304.11603, 2404.13534).
Temporal alignment modules: Fine-tuning only temporal layers on top of pre-trained spatial LDMs enables frame-wise coherence without retraining entire networks (2304.08818).
Causal cache and lossless chunking: Address boundary artifacts in block-wise inference by aligning convolutional windows and caching previous contexts, eliminating flicker at chunk joins (2411.17459).

These techniques are essential for producing physically and visually plausible video, both in unconditional and conditioned (e.g., text-driven) settings.

5. Applications and Extensions

Latent video diffusion models are used in:

Video synthesis/creation: Text-to-video, image-to-video, and multi-modal generation (e.g., integrating text and reference images) at high resolution (2211.11018, 2311.15127, 2501.00103).
Editing and style transfer: Training-free frameworks fuse T2I (text-to-image) and T2V (text-to-video) models in a plug-and-play manner for high-quality video editing with robust temporal consistency (2310.16400).
Personalized and multi-view video: Demonstrated via personalized text-to-video (by combining temporal modules with personalized Stable Diffusion backbones (2304.08818)) and 3D multi-view generation (using video diffusion priors for object consistency (2311.15127)).
Efficient video streaming: Semantic-aware latent compression and conditional LDM/VFI enable bandwidth-efficient and error-resilient video streaming in wireless environments, particularly for 5G and beyond (2502.05695).
Frame interpolation and enhancement: Motion-aware latent diffusion frameworks explicitly incorporate motion hints for robust, high-quality video frame interpolation, super-resolution, and restoration (2404.13534).
Physically informed generation: Integrating masked autoencoder-extracted physical embeddings and aligning with CLIP vision-language space allows generation of scientifically plausible/physically accurate phenomena (e.g., fluid or weather simulations) (2411.11343).

6. Limitations, Challenges, and Future Directions

Despite rapid advances, current latent video diffusion models exhibit several limitations:

Temporal drift and mode coverage: Long video generation may suffer from drifting motion or “mode collapse,” where only a subset of training data distribution is covered, limiting downstream utility and diversity (2411.04956). A reported upper bound is $\sim$ 31% coverage of original dataset modes in healthcare echocardiography applications, even with large synthetic datasets.
Temporal inconsistency in block-wise inference: Tiling/blocked processing without careful cache management introduces boundary artifacts, though causal cache techniques mitigate this (2411.17459).
Physical realism: Without explicit or learned physical priors, videos may be visually plausible but physically implausible. Embedding MAE-derived latent physical knowledge or leveraging pseudo-language prompts aligned with CLIP are emerging solutions that improve performance on scientific phenomena (2411.11343).
Compression/quality trade-off: Higher spatial and temporal compression can degrade fine details, though final-pixel denoising in the decoder or wavelet-guided energy flows can recover much of the lost detail (2501.00103, 2411.17459).
Efficiency versus extensibility: Efficient transformer or U-Net backbones, optimal pretraining/finetuning schedules, and smart plug-and-play architectures are active areas for increasing speed, quality, and adaptability (2304.08818, 2311.15127).

Potential areas for further research include richer, physically grounded or disentangled latent spaces, improved data curation and filtering, enhanced temporal modules, scaling to longer and higher-resolution sequences, and extending multi-modal control, personalization, and explicit physical law embedding.

7. Summary Table: Core Advances in Latent Video Diffusion Models

Aspect	Model Innovation / Impact	Example Papers
Latent compression	VAE, omni-dimensional/3D, wavelet, keyframe hybrids	(2211.11018, 2411.17459)
Diffusion backbone	3D U-Net, Transformer, Triplane, S-AdaLN	(2302.07685, 2401.03048)
Temporal consistency	Directed attention, hierarchical, causal cache	(2211.11018, 2411.17459)
Efficiency	$>60\times$ faster, blockwise, real-time ViT	(2501.00103, 2409.01199)
Editing/control	Inference-time plugin, time-aware point clouds	(2310.16400, 2412.06029)
Physically-informed	MAE + CLIP-quaternion, pseudo-language prompts	(2411.11343)
Video streaming	Latent I-frame LDM, VFI, robust to wireless noise	(2502.05695)
Applications	Synthesis, editing, streaming, personalized video	(2211.11018, 2311.15127)

Latent video diffusion models represent the state of the art in scalable, efficient, and flexible video generation. Their foundations—powerful learned compression and plug-and-play generative backbones—enable broad practical deployments and ongoing research in generative video, video editing, content creation, streaming, and scientific simulation.