Instructions to use hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
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license: other
tags:
- image-to-video
---
Hunyuan1.5 use attention masks with variable-length sequences. For best performance, we recommend using an attention backend that handles padding efficiently.
We recommend installing [kernels](https://github.com/huggingface/kernels) (`pip install kernels`) to access prebuilt attention kernels.
You can check our [documentation](https://huggingface.co/docs/diffusers/main/en/optimization/attention_backends) to learn more about all the different attention backends we support.
```py
import torch
dtype = torch.bfloat16
device = "cuda:0"
from diffusers import HunyuanVideo15ImageToVideoPipeline, attention_backend
from diffusers.utils import export_to_video, load_image
pipe = HunyuanVideo15ImageToVideoPipeline.from_pretrained("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v", torch_dtype=dtype)
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()
generator = torch.Generator(device=device).manual_seed(1)
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG")
prompt="Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
with attention_backend("_flash_3_hub"): # or `"flash_hub"` if you are not using H100/H800
video = pipe(
prompt=prompt,
image=image,
generator=generator,
num_frames=121,
num_inference_steps=50,
).frames[0]
export_to_video(video, "output.mp4", fps=24)
```
to use default attention backend
```py
import torch
dtype = torch.bfloat16
device = "cuda:0"
from diffusers import HunyuanVideo15ImageToVideoPipeline
from diffusers.utils import export_to_video, load_image
pipe = HunyuanVideo15ImageToVideoPipeline.from_pretrained("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_i2v", torch_dtype=dtype)
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()
generator = torch.Generator(device=device).manual_seed(1)
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG")
prompt="Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
video = pipe(
prompt=prompt,
image=image,
generator=generator,
num_frames=121,
num_inference_steps=50,
).frames[0]
export_to_video(video, "output.mp4", fps=24)
``` |