metadata
license: other
tags:
- text-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 (pip install kernels) to access prebuilt attention kernels.
You can check our documentation to learn more about all the different attention backends we support.
import torch
dtype = torch.bfloat16
device = "cuda:0"
from diffusers import HunyuanVideo15Pipeline, attention_backend
from diffusers.utils import export_to_video
pipe = HunyuanVideo15Pipeline.from_pretrained("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v", torch_dtype=dtype)
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()
generator = torch.Generator(device=device).manual_seed(seed)
with attention_backend("_flash_3_hub"): # or `"flash_hub"` if you are not on H100/H800
video = pipe(
prompt=prompt,
generator=generator,
num_frames=121,
num_inference_steps=50,
).frames[0]
export_to_video(video, "output.mp4", fps=24)
To run inference with default attention backend
import torch
dtype = torch.bfloat16
device = "cuda:0"
from diffusers import HunyuanVideo15Pipeline
from diffusers.utils import export_to_video
pipe = HunyuanVideo15Pipeline.from_pretrained("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v", torch_dtype=dtype)
pipe.enable_model_cpu_offload()
pipe.vae.enable_tiling()
generator = torch.Generator(device=device).manual_seed(seed)
video = pipe(
prompt=prompt,
generator=generator,
num_frames=121,
num_inference_steps=50,
).frames[0]
export_to_video(video, "output.mp4", fps=24)