Instructions to use hunyuanvideo-community/HunyuanVideo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hunyuanvideo-community/HunyuanVideo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hunyuanvideo-community/HunyuanVideo", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:Invalid JSON for config file config.json
Unofficial community fork for Diffusers-format weights on tencent/HunyuanVideo.
Using Diffusers
HunyuanVideo can be used directly from Diffusers. Install the latest version of Diffusers.
import torch
from diffusers import HunyuanVideoPipeline, HunyuanVideoTransformer3DModel
from diffusers.utils import export_to_video
model_id = "hunyuanvideo-community/HunyuanVideo"
transformer = HunyuanVideoTransformer3DModel.from_pretrained(
model_id, subfolder="transformer", torch_dtype=torch.bfloat16
)
pipe = HunyuanVideoPipeline.from_pretrained(model_id, transformer=transformer, torch_dtype=torch.float16)
# Enable memory savings
pipe.vae.enable_tiling()
pipe.enable_model_cpu_offload()
output = pipe(
prompt="A cat walks on the grass, realistic",
height=320,
width=512,
num_frames=61,
num_inference_steps=30,
).frames[0]
export_to_video(output, "output.mp4", fps=15)
Refer to the documentation for more information.
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