Open video models have tended to force a choice between quality and hardware you can actually own. HunyuanVideo-1.5 is Tencent’s attempt to narrow that gap: 8.3 billion parameters, weights and inference code on GitHub and Hugging Face, and a documented minimum of 14GB of GPU memory with offloading.
| Repository | Tencent-Hunyuan/HunyuanVideo-1.5 |
| Maintainer | Tencent Hunyuan |
| Primary language | Python |
| GitHub stars | 4,551 (as of 15 September 2026) |
| Last push | 10 April 2026 |
| License status | Tencent Hunyuan Community License |
What it does
HunyuanVideo-1.5 generates video from text prompts (T2V) or from a starting image (I2V). The repository’s release history since the November 2025 launch shows steady additions:
- Cache-based acceleration (DeepCache, TeaCache, TaylorCache), added in late November 2025.
- A 480p image-to-video step-distilled model (December 2025) that runs in 8 or 12 steps. The README reports end-to-end generation within 75 seconds on a single RTX 4090, a 75% reduction.
- Training code and a LoRA fine-tuning script, using the Muon optimizer the team open-sourced alongside it.
- FP8 GEMM inference (December 2025), plus official Hugging Face Diffusers support and a ComfyUI usage guide.
The README also lists community integrations, including LightX2V and Wan2GP, which it says can run the model with as little as 6GB of VRAM.
Who it’s for
- Creators and developers who want a self-hosted video model on a single high-end consumer GPU.
- Teams that plan to fine-tune a video model with LoRA on their own footage.
- ComfyUI and Diffusers users, who have official integration paths.
Getting started
The README’s system requirements: an NVIDIA GPU with CUDA, 14GB minimum GPU memory with model offloading enabled, Linux and Python 3.10+. Setup is a clone, pip install -r requirements.txt, and optional attention libraries (Flash Attention, Flex-Block-Attention for sparse attention, or SageAttention) for faster inference. Weights come from Hugging Face; the step-distilled model is enabled with --enable_step_distill when running generate.py.
License status
Checked October 17, 2026: The published license is the Tencent Hunyuan Community License Agreement. It excludes the EU, UK and South Korea from its territory and includes use, distribution and commercial-scale conditions. Public weights do not mean unrestricted use. Check this agreement and the terms of any additional components before deployment.
Limitations and open questions
- The open-source plan in the README still lists “Release all model weights (Sparse attention, distill model, and SR models)” as not done.
- The official setup is Linux-only.
- The 14GB figure assumes offloading, which the README notes is slower than running fully on the GPU.
- Speed figures are Tencent’s own; we have not benchmarked them.
Sources
Related: ComfyUI-LTXVideo · Video Generation.
This profile was drafted with AI assistance from the project’s public repository and reviewed by The Signal’s editors before publication. See our AI Disclosure.
