
TVPaint Animation
The digital solution for your professional 2D animation projects.

State-of-the-art high-resolution video synthesis using cascaded latent diffusion models.

NVIDIA VideoLDM (Video Latent Diffusion Model) represents a breakthrough in high-resolution video synthesis by leveraging a cascaded latent space architecture. Unlike traditional video models that suffer from massive compute requirements, VideoLDM utilizes a two-stage approach: training on image datasets for high-quality spatial features and then introducing temporal layers through fine-tuning on video data. This allows for the generation of temporally consistent, 1280x720 resolution videos. In the 2026 landscape, VideoLDM is a foundational pillar for NVIDIA's AI Foundation models and NVIDIA Picasso. It is designed to run efficiently on H100/H200 and Blackwell architectures, providing developers with the weights and architectural flexibility to create personalized video content using techniques like DreamBooth. The model's ability to handle diverse aspect ratios and its integration into the NVIDIA NIM (NVIDIA Inference Microservices) ecosystem makes it a preferred choice for enterprise-grade generative video pipelines requiring localized data control and extreme performance scaling.
NVIDIA VideoLDM (Video Latent Diffusion Model) represents a breakthrough in high-resolution video synthesis by leveraging a cascaded latent space architecture.
Explore all tools that specialize in latent diffusion. This domain focus ensures NVIDIA VideoLDM delivers optimized results for this specific requirement.
Separates spatial content learning from temporal motion patterns, allowing high-resolution training on image-only datasets.
Incorporation of temporal layers into pre-trained 2D latent diffusion models.
Native support for generating 1280x720 content by stacking super-resolution diffusion models.
Enables personalization of video models using a small set of target images.
Supports non-square resolutions through flexible latent patch encoding.
Hardware-level optimization for NVIDIA Blackwell and Hopper architectures.
Advanced prompt conditioning using CLIP-based text encoders with attention re-weighting.
Provision NVIDIA GPU environment (A100 80GB or higher recommended).
Install NVIDIA Container Toolkit and Docker.
Clone the official VideoLDM research repository or access via NVIDIA NGC.
Download pre-trained latent diffusion weights (LDM-4/8).
Configure the Python environment using the provided environment.yaml.
Initialize temporal fine-tuning scripts for specific video datasets if required.
Define inference parameters including resolution, frame count, and CFG scale.
Execute the sampling script (e.g., sample_videoldm.py).
Optimize output using NVIDIA TensorRT for real-time inference.
Integrate into production via NVIDIA NIM microservices.
All Set
Ready to go
Verified feedback from other users.
"Highly praised for temporal consistency and technical flexibility, though compute requirements remain high."
Post questions, share tips, and help other users.

The digital solution for your professional 2D animation projects.

Empowering independent artists with digital music distribution, publishing administration, and promotional tools.

Convert creative micro-blogs into high-performance web presences using generative AI and Automattic's core infrastructure.

Fashion design technology software and machinery for apparel product development.

Instantly turns any text to natural sounding speech for listening online or generating downloadable audio.

Professional studio-quality AI headshot generator for individuals and teams.