Model reference · open weights

HunyuanVideo

Available as managed deployment Licence fee Video tencent Text→video 1 variants 805 dl/mo

HunyuanVideo is an open-weight video model from tencent. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Makertencent
TypeVideo models
TaskText→video
Released2024-12-01
Popularity805 downloads / month
LicenceCommercial licence needed

About

What HunyuanVideo is


This repo contains PyTorch model definitions, pre-trained weights and inference/sampling code for our paper exploring HunyuanVideo. You can find more visualizations on our project page.

HunyuanVideo: A Systematic Framework For Large Video Generation Model Training

News!!

  • Jan 13, 2025: 📈 We release the Penguin Video Benchmark.
  • Dec 18, 2024: 🏃‍♂️ We release the FP8 model weights of HunyuanVideo to save more GPU memory.
  • Dec 17, 2024: 🤗 HunyuanVideo has been integrated into Diffusers.
  • Dec 7, 2024: 🚀 We release the parallel inference code for HunyuanVideo powered by xDiT.
  • Dec 3, 2024: 👋 We release the inference code and model weights of HunyuanVideo. Download.

Open-source Plan

  • HunyuanVideo (Text-to-Video Model)
    • [x] Inference
    • [x] Checkpoints
    • [x] Multi-gpus Sequence Parallel inference (Faster inference speed on more gpus)
    • [x] Web Demo (Gradio)
    • [x] Diffusers
    • [x] FP8 Quantified weight
    • [x] Penguin Video Benchmark
    • [x] ComfyUI
  • HunyuanVideo (Image-to-Video Model)
    • [x] Inference
    • [x] Checkpoints

Contents


Abstract

We present HunyuanVideo, a novel open-source video foundation model that exhibits performance in video generation that is comparable to, if not superior to, leading closed-source models. In order to train HunyuanVideo model, we adopt several key technologies for model learning, including data curation, image-video joint model training, and an efficient infrastructure designed to facilitate large-scale model training and inference. Additionally, through an effective strategy for scaling model architecture and dataset, we successfully trained a video generative model with over 13 billion parameters, making it the largest among all open-source models.

We conducted extensive experiments and implemented a series of targeted designs to ensure high visual quality, motion diversity, text-video alignment, and generation stability. According to professional human evaluation results, HunyuanVideo outperforms previous state-of-the-art models, including Runway Gen-3, Luma 1.6, and 3 top-performing Chinese video generative models. By releasing the code and weights of the foundation model and its applications, we aim to bridge the gap between closed-source and open-source video foundation models. This initiative will empower everyone in the community to experiment with their ideas, fostering a more dynamic and vibrant video generation ecosystem.

HunyuanVideo Overall Architecture

HunyuanVideo is trained on a spatial-temporally compressed latent space, which is compressed through a Causal 3D VAE. Text prompts are encoded using a large language model, and used as the conditions. Taking Gaussian noise and the conditions as input, our generative model produces an output latent, which is then decoded to images or videos through the 3D VAE decoder.

HunyuanVideo Key Features

Unified Image and Video Generative Architecture

HunyuanVideo introduces the Transformer design and employs a Full Attention mechanism for unified image and video generation. Specifically, we use a "Dual-stream to Single-stream" hybrid model design for video generation. In the dual-stream phase, video and text tokens are processed independently through multiple Transformer blocks, enabling each modality to learn its own appropriate modulation mechanisms without interference. In the single-stream phase, we concatenate the video and text tokens and feed them into subsequent Transformer blocks for effective multimodal information fusion. This design captures complex interactions between visual and semantic information, enhancing overall model performance.

MLLM Text Encoder

Some previous text-to-video models typically use pre-trained CLIP and T5-XXL as text encoders where CLIP uses Transformer Encoder and T5 uses an Encoder-Decoder structure. In contrast, we utilize a pre-trained Multimodal Large Language Model (MLLM) with a Decoder-Only structure as our text encoder, which has the following advantages: (i) Compared with T5, MLLM after visual

From the published model card. Full card on the HuggingFace links in the sidebar.

How it works

How video models work

Prompt / imagestart pointTemporal diffusionframes over timeVideoMP4 clipA video model generates a sequence of coherent frames from your prompt or a starting image.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys tencent-hunyuanvideo for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (tencent-hunyuanvideo below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/videos/generations \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"tencent-hunyuanvideo","prompt":"a drone shot over a forest"}'

Create an account — your API key is available in the console. 5M tokens/month currently included with every new account at launch.

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