FastVideo is a unified post-training and real-time inference framework for accelerated video generation.
Recent news
2026/11/10: Day-0 support for PAN 2, a world model from IFM. See the cookbook.2026/10/21: Release FastH3 Omni (Ref2VA): reference-conditioned video-and-audio generation from a prompt plus ordered image, video and audio references, in eight fused forwards. It runs on consumer Blackwell (RTX 5090, RTX PRO 6000, DGX Spark — NVFP4 QAD), on RTX 4090 (FP8) and on Apple Silicon (MLX). See the cookbook and the inference guide.2026/10/06: FastVideo now supports Kandinsky 6 from Kandinsky Lab: text- and image-to-video with synchronized audio (base and 10-step distilled pi-Flow checkpoints) plus video super-resolution up to 4x. See the Kandinsky 6 recipes.2026/10/06: FastH3 V2 now runs on a single consumer machine: NVIDIA RTX 5090, RTX 4090 and RTX PRO 6000 GPUs, DGX Spark and Apple Silicon. We also release FastH3 Trim, an experimental pruned model that is 4.2× smaller than base H3 and runs in as little as 8 GB of GPU memory. Get the models and read the Blog.2026/09/15: Release FastH3 8-Step V2, an eight-forward data-free DMD2 checkpoint distilled from MiniMax-H3 with 80% Video Sparse Attention. Run it with examples/inference/basic/basic_fasth3_8step.py or the FastH3 8-Step V2 recipe.2026/09/01: FastH3 now runs locally on Apple Silicon through MLX and on NVIDIA DGX Spark through CUDA 13, including two-Spark inference. Follow the FastH3 recipes and read the Blog.2026/08/27: FastH3 Preview v1 is an open-weight 4-step sparse-distilled MiniMax-H3 model for synchronized video-and-audio generation, developed in collaboration with Nuva Lab and the NVIDIA FastGen team. Download the recommended VSA / Data-Free weights, or see the FastH3 V1 collection.2026/08/19: FastVideo now supports MLX on Apple Silicon with FastMetal-QAD, a family of 1.3B, 5B, and 14B models optimized for Mac. Follow the MLX install guide and read the Blog.
More News
Key Features
FastVideo has the following features:
- End-to-end post-training support for bidirectional and autoregressive models:
- Support full finetuning and LoRA finetuning for state-of-the-art open video DiTs
- Data preprocessing pipeline for video, image, and text data
- Distribution Matching Distillation (DMD2) stepwise distillation.
- Sparse attention with Video Sparse Attention
- Sparse distillation to achieve >50x denoising speedup
- Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing.
- Causal distillation through Self-Forcing
- See this page for the supported training workflows, and the support matrix for supported models.
- State-of-the-art performance optimizations for inference
- Sequence Parallelism for distributed inference
- Multiple state-of-the-art attention backends
- User-friendly CLI and Python API
- See this page for full list of supported optimizations.
- Diverse hardware and OS support
- Support H100, A100, 4090
- Support Linux, Windows, MacOS
- See this page for full list of supported models, hardware assumptions, and optimization compatibility.
- Realtime video generation & editing
- Dreamverse: stream and "vibe direct" video in realtime (live demo), deployable on local GPU, a self-hosted B200 server, Docker, or serverless Modal
Getting Started
Install FastVideo with uv:
uv venv --python 3.12 --seed
source .venv/bin/activate
# NVIDIA CUDA 12; use cu130 on CUDA 13
UV_TORCH_BACKEND=cu126 uv pip install fastvideo
Platform guides: NVIDIA GPUs, DGX Spark / GB10, Apple Silicon MLX, AMD / ROCm — or the full installation guide.
To have a coding agent do the install, give it the agent install prompt.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
