Latent Video Diffusion Models

Updated 12 November 2025

Latent video diffusion models are generative techniques that operate in low-dimensional latent spaces derived from autoencoders, reducing computational demands drastically.
They incorporate temporal modules and motion-based alignment, such as optical flow, to maintain frame coherence and handle high-resolution, long-sequence video synthesis.
These models are applied in text-to-video generation, video editing, and frame interpolation, achieving state-of-the-art results in efficiency and visual quality.

Latent video diffusion models are a class of generative models that synthesize videos by learning diffusion processes in compact, lower-dimensional latent spaces rather than pixel space. This approach emerged as a solution to the formidable computational and memory bottlenecks associated with high-resolution and long-range video generation using conventional diffusion models. Latent video diffusion exploits a compressed representation—typically produced by pretrained or tailored autoencoders—to make tractable the learning of distributions and dynamics otherwise infeasible at video-scale pixel granularity. This article reviews the principles, architectural strategies, temporal modeling approaches, application domains, and current trade-offs in contemporary latent video diffusion research.

1. Fundamentals of Latent Video Diffusion

The core idea in latent video diffusion is to operate the entire diffusion process—forward noising and reverse denoising—not in RGB pixel space, but in a low-dimensional latent space. This latent space is typically induced by an autoencoder, such as a VQ-GAN, VQ-VAE, or variational autoencoder trained on video or images. For a video of $T$ frames (each of spatial size $H\times W$ ), the encoder $\mathcal{E}$ maps each frame (or spatiotemporal block) $x_t$ to a latent $z_t\in\mathbb{R}^{h\times w\times c}$ , where $h\ll H$ , $w\ll W$ , and $c$ is a small channel count. The entire video is represented as $z_{1:T} \in \mathbb{R}^{T\times h\times w\times c}$ or as a factorized/tubular projection (e.g., triplane or tensorized factorization in PVDM (Yu et al., 2023)).

The forward process is a fixed-length Markov chain of additive Gaussian perturbations in latent space: $q(z_t|z_{t-1}) = \mathcal{N}(z_t; \sqrt{\alpha_t}z_{t-1}, (1-\alpha_t)I)$ with $\{\alpha_t\}$ as a linear or cosine noise schedule. The denoising model $\epsilon_\theta$ (usually a U-Net or transformer) is trained using a score-matching or noise-prediction loss

$\mathcal{L} = \mathbb{E}_{z_0, t, \epsilon} \| \epsilon - \epsilon_\theta(z_t, t, c) \|^2$

where $c$ denotes conditioning (text prompt, action class, reference image, etc).

By diffusing in latent rather than pixel space, the model's memory and computational demands are reduced by 1–2 orders of magnitude, enabling high-resolution ( $512\times1024$ or above) and long-sequence ( $>1000$ frames) video generation (Blattmann et al., 2023, He et al., 2022, HaCohen et al., 30 Dec 2024).

2. Temporal and Spatiotemporal Coordination in Latent Space

Temporal coherence—i.e., avoidance of frame-to-frame flicker and physically consistent motion—is a principal challenge. Two broad strategies exist in contemporary literature:

Inserting Temporal Modules into Diffusion Architectures:

Video-LDM, Stable Video Diffusion, and MagicVideo extend an image-pretrained U-Net by interleaving temporal convolutions, temporal residual or attention blocks, or cross-frame attention with the existing spatial modules (Blattmann et al., 2023, Blattmann et al., 2023, Zhou et al., 2022). For each layer, the outputs from spatial and temporal computations are often blended by a learnable gating parameter:

$x_\text{out} = \alpha^i_\phi\cdot x_\theta + (1-\alpha^i_\phi)\cdot x'_{\phi}$

where $x_\theta$ is spatial output (from frozen image weights), $x'_\phi$ is the temporal output, and $\alpha^i_\phi$ is learned.

Flow- and Motion-based Latent Alignment:

MoVideo and LatentWarp leverage explicit optical flow and depth supervision. MoVideo diffuses not only over latent video tensors, but also jointly over per-frame depth and optical flows, using the latter to warp latent codes and as direct model input (Liang et al., 2023). LatentWarp, designed for zero-shot video-to-video translation, computes optical flow between input frames, warps previous latent features accordingly, and injects these warped features into the denoising stream to constrain attention queries, thus eliminating cross-frame drift and reducing temporal warp error by a factor of 2–3 over prior approaches (Bao et al., 2023).

Additional approaches (e.g., CMD (Yu et al., 21 Mar 2024)) compress a video as a content frame plus a compact motion code, enabling the reuse of 2D image diffusion for content and a lightweight transformer for motion.

3. Model Architectures: Autoencoders, Diffusers, and Transformers

Autoencoder Design

Autoencoders serve as the interface between pixel and latent space, determining the expressivity and compressibility of the model:

Frame-wise VAE: Processes each video frame independently, as in Video-LDM and MagicVideo, enabling the reuse of powerful image models.
3D/Spatiotemporal Autoencoders: Apply 3D convolutions or transformers for joint space-time encoding (e.g., LVDM (He et al., 2022), LTX-Video (HaCohen et al., 30 Dec 2024), CMD (Yu et al., 21 Mar 2024)).
Factorized Projections: Project high-dimensional video tensors into structured planes or tubes (e.g., triplane schemes in PVDM (Yu et al., 2023)).

Diffusion Denoisers

Three primary types of denoising networks are used:

3D U-Net: Standard for joint spatiotemporal processing; spatial and temporal blocks are inserted hierarchically (Blattmann et al., 2023, Blattmann et al., 2023).
Factorized/Decoupled U-Nets: Space and time blocks alternate or are factorized; some methods employ interleaved residual learning or separate spatial/temporal branches (e.g., Latte's four-factorization variants (Ma et al., 5 Jan 2024)).
Transformer-based Denoisers: Transformer layers operate on sequences of patchified latent tokens (e.g. Latte, LTX-Video), enabling full spatiotemporal self-attention (Ma et al., 5 Jan 2024, HaCohen et al., 30 Dec 2024). Efficient factorization—along the spatial or temporal dimensions—is critical for tractability.

Recent innovations (e.g., LTX-Video (HaCohen et al., 30 Dec 2024)) perform patchification inside the VAE, drastically reducing the number of tokens while enabling global spatiotemporal receptive fields at the transformer level.

4. Applications, Conditioning, and Extensions

Latent video diffusion models are applied in both unconditional and conditional (text-to-video, image-to-video, video editing) settings. Examples include:

Driving Simulation: Video-LDM achieves FVD of ≈356–389 on real driving data at $512\times 1024$ resolution (Table 1, (Blattmann et al., 2023)).
Text-to-Video: Leveraging pretrained text-to-image LDMs (e.g., Stable Diffusion), models insert temporal layers and train on large web video/caption corpora (WebVid-10M, LVD, InternVid) (Blattmann et al., 2023, Blattmann et al., 2023, Yu et al., 21 Mar 2024).
Image-to-Video and Personalization: Adaptations include replacing text with CLIP image features and finetuning only select temporal or spatial subnetworks (e.g. DreamBooth-style personalization (Blattmann et al., 2023)).
Video Editing: Fusion of T2I and T2V denoisers (FLDM (Lu et al., 2023)) via per-step linear blend achieves both temporal consistency and fine per-frame fidelity.
Video Frame Interpolation: Diffusion-based VFI in latent space (LDMVFI, MADiff (Danier et al., 2023, Huang et al., 21 Apr 2024)) outperform GANs and flow/kernels-based interpolators, especially under complex dynamic textures.

Conditioning mechanisms include text prompts (CLIP or BERT embeddings), image frames, explicit motion fields (optical flow, depth), and pseudo-videos synthesized from image-text corpora by applying artificial pans and zooms for data augmentation (VidRD (Gu et al., 2023)).

5. Training, Sampling, and Scalability

Determinants of training and sampling efficiency include latent dimensionality, network width, variance schedule, and parallelization:

Sampling Steps: High-fidelity results require 50–250 denoising steps for DDIM/ODE samplers; frame interpolation is possible with as few as 10–20 steps (Danier et al., 2023, Huang et al., 21 Apr 2024).
Memory and Compute: Latent models run at ~1/64th to ~1/192nd the cost of pixel-space video diffusion (Zhou et al., 2022, HaCohen et al., 30 Dec 2024). CMD samples a $16$-frame $512 \times 1024$ video in 3.1s on a single A100 GPU, $\sim 7.7\times$ faster than prior works (Yu et al., 21 Mar 2024). LTX-Video achieves real-time $768\times512$ at 24 fps in 2s on an H100 (HaCohen et al., 30 Dec 2024).
Batch Size: Exploiting high compression, SVD and LTX-Video achieve batch sizes up to 1536 on high-memory GPUs (Blattmann et al., 2023).
Guidance and Conditioning: Classifier-free or staged guidance is widely used; scaling the guidance parameter mediates sample sharpness vs. diversity (Blattmann et al., 2023, Blattmann et al., 2023, Gu et al., 2023).
Data Curation: Practical high-quality text-to-video synthesis requires massive, carefully filtered video-text datasets (motion, CLIP sim, OCR, aesthetic), as shown by SVD's LVD-F curation pipeline (Blattmann et al., 2023).

6. Performance Characteristics and Empirical Outcomes

Latent video diffusion models lead the field on established video benchmarks:

UCF-101 zero-shot text-to-video, FVD (↓): SVD $242$ (Blattmann et al., 2023); CMD $107$ (Yu et al., 21 Mar 2024); Video-LDM $550.6$ (Blattmann et al., 2023); Latte $333.6$ (Ma et al., 5 Jan 2024).
High-Res/Long Video: LVDM generates videos over 1,000 frames with FVD $_{1024}$ growing more slowly than all previous autoregressive or GAN baselines; hierarchical infilling reduces error accumulation (He et al., 2022).
Qualitative Realism: Human studies confirm that temporally-aligned upsamplers, flow-guided denoising, and in-iteration latent deflickering all reduce visual jitter and preserve fine structure (Blattmann et al., 2023, Duan et al., 2023).
Efficiency and Scaling: Moving patchification into the VAE (LTX-Video) leads to a 4 $\times$ increase in pixels-per-token and enables global full-attention in transformer denoisers (HaCohen et al., 30 Dec 2024). CMD and LTX-Video achieve 10 $\times$ –20 $\times$ reduction in TFLOPs and memory over previous SoTA (Yu et al., 21 Mar 2024, HaCohen et al., 30 Dec 2024).

7. Limitations, Trade-Offs, and Research Directions

Trade-offs are inherent to latent video diffusion:

Compression versus Detail: High-compression VAEs, as in LTX-Video (1:192), may under-represent fine spatiotemporal details; the burden shifts to the decoder to inpaint and denoise residual artifacts (HaCohen et al., 30 Dec 2024).
Explicit Motion Modeling: While motion-aware and optical-flow–conditioned models (MoVideo, MADiff) achieve better temporal consistency and prompt alignment, they require additional computation for per-frame depth/flow prediction and complex warping pipelines (Liang et al., 2023, Huang et al., 21 Apr 2024).
Autoregressive versus Hierarchical Generation: Long video synthesis via stepwise autoregression can accumulate errors; hierarchical (sparse–dense) methods mitigate error propagation but add system complexity and pipeline latency (He et al., 2022).
Generalization: Temporal layers trained on one backbone (e.g., SD 1.4) can generalize to others or to personalized variants (DreamBooth), supporting flexible subject-driven T2V pipelines (Blattmann et al., 2023).
Plug-and-Play Editing: Model fusion at inference enables training-free, flexible video editing, but requires latent-space compatibility, and manual tuning of fusion parameters (FLDM (Lu et al., 2023)).

Ongoing research is directed towards larger and cleaner datasets, scalable spatiotemporal transformers, more powerful and flexible conditioning mechanisms, accelerated sampling via distillation or ODE/SDE solvers, and domain extensions such as multi-view/3D prior video synthesis (Blattmann et al., 2023, Ma et al., 5 Jan 2024, HaCohen et al., 30 Dec 2024).

Latent video diffusion models constitute the current frontier for scalable, temporally coherent, high-fidelity video generation, editing, and understanding. They unify advances in generative modeling, video understanding, and representation learning, providing a modular and extensible framework that is expected to underpin broad industrial and scientific applications in the years ahead.