Plot twist: the best open-source video model in 2026 came from Tencent. HunyuanVideo dropped with full weights, training code, and a permissive license — 13B parameters of text-to-video you can run on your own metal. The Sora moat just got smaller.
What Is HunyuanVideo?
HunyuanVideo is Tencent's open-weight text-to-video model: a 13B diffusion transformer (DiT) trained jointly on image and video data. Joint training is a big part of why it behaves — the model learns composition and lighting from stills as well as motion from footage, instead of trying to learn everything from video alone.
Open-weight here means the real deal: downloadable weights, public training code, and a license permissive enough to actually use the outputs. This isn't a demo behind a waitlist — it's a model you pull down and run.
The results are the part that surprised everyone. Motion is smooth, prompt adherence is wild, and the clips don't have that melting-faces fever-dream quality that made early open video models a meme.
How HunyuanVideo Works
It's a diffusion transformer extended across time: you describe a shot, the model denoises a latent video toward that description, and a decoder turns the result into frames. Same broad recipe as modern image generation, with temporal consistency as the hard part it actually gets right.
In practice you think in film terms, not ML terms — shot type, subject, movement, lighting, lens. “Drone shot over Brisbane river at dawn, cinematic” beats a paragraph of adjectives. Brief it like a camera operator, not a novelist.
Clip math worth knowing before you start: the standard recipe is 121 frames at 24fps at 1280×720 with around 30 sampling steps — call it five seconds of usable footage per run.
HunyuanVideo vs Sora and the Open-Weight Field
The obvious comparison is Sora, and the real difference isn't quality — it's control. Sora is credits, rate limits, and a terms-of-service ceiling. HunyuanVideo is your GPU, your queue, your pipeline, and nobody meters your renders.
For anyone building automated video workflows, that's the whole ballgame. A closed API can change pricing or policy and delete your pipeline overnight; open weights can't be taken back.
Within the open-weight field, this is the one to beat right now. Mochi kicked the open-video door open; HunyuanVideo walked through it with smoother motion and a proper local-tooling story across both ComfyUI and diffusers.
How to Run It: ComfyUI or Diffusers
The ComfyUI route
Hunyuan slots straight into ComfyUI, which is the fastest way to get frames on screen and iterate on prompts. Load the model, encode a prompt, sample, combine to video — four nodes and you're rendering.
{`{
"nodes": {
"1": { "class_type": "HunyuanVideoLoader", "inputs": { "model": "hunyuan-video-13b" } },
"2": { "class_type": "CLIPTextEncode", "inputs": { "text": "drone shot over Brisbane river at dawn, cinematic" } },
"3": { "class_type": "HunyuanVideoSampler", "inputs": {
"model": ["1", 0], "positive": ["2", 0],
"frames": 121, "width": 1280, "height": 720, "steps": 30
}},
"4": { "class_type": "VideoCombine", "inputs": { "frames": ["3", 0], "fps": 24 } }
}
}`}ComfyUI is the right tool while you're exploring: tweak the prompt, re-queue, compare outputs side by side. Once you know what you want, graduate to code.
The diffusers route
There's an official diffusers pipeline if you'd rather script it, and scripting is where this model earns its keep. Dependencies are the usual suspects:
{`pip install diffusers transformers accelerate`}{`import torch
from diffusers import HunyuanVideoPipeline
from diffusers.utils import export_to_video
pipe = HunyuanVideoPipeline.from_pretrained(
"tencent/HunyuanVideo",
torch_dtype=torch.bfloat16,
).to("cuda")
frames = pipe(
prompt="slow zoom on a brutalist concrete facade, golden hour, 35mm",
num_frames=121,
height=720, width=1280,
num_inference_steps=30,
).frames[0]
export_to_video(frames, "out.mp4", fps=24)`}The diffusers route is what I'm using for FFmpeg-driven batch jobs — generate, pipe straight into ffmpeg for format conversion, drop into After Effects for the final colour pass. No Sora credits, no rate limits.
Batchability is the underrated feature. A prompt list plus a for-loop becomes a b-roll library generator that runs overnight, and every clip lands in a folder already named and encoded the way your edit wants it.
The Catch
5-second clips max at the good quality tier. Fine for loops and inserts, painful for anything narrative — you'll be stitching, and stitched clips don't share any memory of what the previous one looked like.
Then there's the hardware bill: 80GB of VRAM at full bf16, which is H100 territory. The fp8 quants squeeze it onto a 4090, but motion gets jittery on complex scenes — usable, with an asterisk.
Render times are also not a joke: 5 minutes for 5 seconds on an H100. This is not a real-time toy; it's a render-farm workload you plan around, which is exactly why the batch-scripting route matters more than the demo GIFs.
The Verdict
For the first time, open-source video gen is actually usable for short loops, intros, and motion plates. If you're doing After Effects work, this is your new b-roll generator — the 5-second limit is annoying but stitchable.
The honest read: if you need one clip and own no GPU, a hosted tool is still less hassle. If you need a hundred clips a week inside a pipeline you control, HunyuanVideo is the first open model that genuinely does the job. Try it before the inevitable licensing rug-pull discourse starts.