hao-ai-lab/FastVideo
A unified inference and post-training framework for accelerated video generation.
Star History
Add Live Star History & Verified Badges to README.md
Keep your repository README looking professional and dynamic. As our continuous crawler records new stars, these official SVG badges update in real time with zero maintenance.
Renders your high-resolution star trajectory chart right inside your GitHub README or project docs.
[](https://githubrepo.cloud/repo/hao-ai-lab/FastVideo?utm_source=readme_chart)Compact Shields-style badges for your README header. Shows real-time stars and global ranking.
[](https://githubrepo.cloud/repo/hao-ai-lab/FastVideo?utm_source=readme_badge) [](https://githubrepo.cloud/repo/hao-ai-lab/FastVideo?utm_source=readme_badge) [](https://githubrepo.cloud/repo/hao-ai-lab/FastVideo?utm_source=readme_badge)Momentum
+113
STARS · LAST 30 DAYS
4
PER DAY
#226
MOST-STARRED Python
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +28 | +113 | +360 |
| Per day | 4 | 4 | 4 |
| Forks gained | +2 | +10 | +24 |
FastVideo gained 113 stars in the last 30 days, about 4 a day, and now has 4.5K. It is about 2 years old and has averaged roughly 2.3K stars a year. It ranks #226 among Python repositories and #1,440 across all languages on GitHubRepo.
Trending Record
1
DAYS ON TRENDING
#2352
BEST RANK
Sep 30, 2026
FIRST APPEARANCE
Active
STATUS TODAY
FastVideo has maintained a continuous presence across global trending indexes, peaking at #2352. Below is the 30-day activity profile:
💡 Overview
FastVideo is an open-source project written in Python: A unified inference and post-training framework for accelerated video generation.
Engineered for speed, consistency, and developer ease, it solves common hurdles in diffusers, diffusion-models, distillation. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Python for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ pip install FastVideo⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Python >= 3.9, pip, virtualenv
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
FastVideo coordinates its core functionality through a modular Python pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Autonomous AI Agents
Orchestrate intelligent workflows and tool-calling routines with FastVideo.
Model Inference & Prompting
Integrate fast, local or cloud-hosted generative AI models directly into production code.
Context Memory & RAG
Augment language models with dynamic vector retrieval and structured project memory.
Developer Productivity
Automate repetitive engineering tasks, code generation, and test creation using AI agents.
🚀 Getting Started
Install FastVideo using your package manager: `pip install FastVideo`
Initialize your project workspace or configuration file for FastVideo.
Import FastVideo into your codebase or invoke it directly from your terminal.
Execute your test suite or run `FastVideo --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Python, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
hao-ai-lab/FastVideo is currently ranked #1,440 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
| Rank | Repository | Language | Stars | Action |
|---|---|---|---|---|
| #1,435 | getfider/fider | Go | ★ 4.6K | Compare ↗ |
| #1,436 | IBM/mcp-context-forge | Python | ★ 4.5K | Compare ↗ |
| #1,437 | ProvableHQ/snarkOS | Rust | ★ 4.5K | Compare ↗ |
| #1,437 | AdguardTeam/AdguardFilters | Adblock Filter List | ★ 4.5K | Compare ↗ |
| #1,439 | ruvnet/RuVector | Rust | ★ 4.5K | Compare ↗ |
| #1,440 | hao-ai-lab/FastVideo This Project | Python | ★ 4.5K | |
| #1,441 | nicholas-fedor/watchtower | Go | ★ 4.5K | Compare ↗ |
| #1,442 | docker/buildx | Go | ★ 4.5K | Compare ↗ |
| #1,443 | squidowl/halloy | Rust | ★ 4.5K | Compare ↗ |
| #1,444 | apple/swift-collections | Swift | ★ 4.5K | Compare ↗ |
| #1,445 | octobox/octobox | Ruby | ★ 4.5K | Compare ↗ |
Frequently Asked Questions
What does FastVideo do? +
A unified inference and post-training framework for accelerated video generation.
What language is FastVideo written in? +
The primary language is Python. Topics include: diffusers, diffusion-models, distillation, inference, post-training.
Is FastVideo actively maintained? +
Yes, the last recorded push was on Sep 29, 2026 with 185 open issues being tracked.
How many stars does FastVideo have? +
FastVideo has 4,522 stars and 479 forks on GitHub.
How does FastVideo rank among GitHub repositories? +
With 4,522 stars, hao-ai-lab/FastVideo is ranked #1,440 globally across all repositories tracked on GitHubRepo and #226 among Python projects.
What license is FastVideo distributed under? +
The repository reports a Apache-2.0 license. Always verify the repository LICENSE file for legal terms.
