#1616th of 24 in Image and videoFor image, video and design, video and animation
hunyuanvideo
Text-to-video diffusion model with PyTorch inference code and weights
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- Stars
- 12.6k
- License
- custom license
- Last commit
- Oct 2026
- Language
- Python
11,776 on 31 Mar 202612,643 on 20 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
Tencent's 13B-parameter text-to-video model, released with PyTorch model definitions, pretrained weights and inference code. It uses a dual-stream to single-stream transformer, an MLLM text encoder and a causal 3D VAE. The repo includes a Gradio demo, xDiT multi-GPU parallel inference, FP8 weights and a prompt-rewrite model.
Who it is for: Engineers with 60-80GB NVIDIA GPUs running or building on open video models
Strengths
- Open weights plus inference code, with Diffusers and ComfyUI integrations
- FP8 weights and xDiT sequence-parallel inference for multi-GPU speedup
- Includes Gradio web demo and prompt-rewrite model with Normal and Master modes
- Active ecosystem of community accelerators, GGUF quantizations and LoRAs
Weaknesses
- Needs 45GB GPU memory at 544x960, 60GB at 720x1280 for 129 frames
- NVIDIA CUDA only; tested on Linux only
- License is not a standard SPDX identifier (NOASSERTION); check terms before use
- Tencent has since released HunyuanVideo-1.5, a newer successor model
What it needs
- GPU required
- Needs PyTorch 2.7.1, CUDA 11.8 or 12.6, flash-attention 2.8.3, xfuser 0.4.0, conda
- Models: HunyuanVideo T2V (13B), FP8 weights, Diffusers, HunyuanVideo-PromptRewrite
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