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import torch
from diffusers import HunyuanVideoFramepackPipeline, HunyuanVideoFramepackTransformer3DModel
from diffusers.hooks import apply_group_offloading
from diffusers.utils import export_to_video, load_image
from transformers import SiglipImageProcessor, SiglipVisionModel
transformer = HunyuanVideoFramepackTransformer3DModel.from_pretrained(
"/mnt/workspace/checkpoints/lllyasviel/FramePack_F1_I2V_HY_20250503", torch_dtype=torch.bfloat16
)
feature_extractor = SiglipImageProcessor.from_pretrained(
"/mnt/workspace/checkpoints/lllyasviel/flux_redux_bfl", subfolder="feature_extractor"
)
image_encoder = SiglipVisionModel.from_pretrained(
"/mnt/workspace/checkpoints/lllyasviel/flux_redux_bfl", subfolder="image_encoder", torch_dtype=torch.float16
)
pipe = HunyuanVideoFramepackPipeline.from_pretrained(
"/mnt/workspace/checkpoints/hunyuanvideo-community/HunyuanVideo",
transformer=transformer,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
torch_dtype=torch.float16,
)
onload_device = torch.device("cuda")
offload_device = torch.device("cpu")
list(map(
lambda x: apply_group_offloading(x, onload_device, offload_device, offload_type="leaf_level", use_stream=True, low_cpu_mem_usage=True),
[pipe.text_encoder, pipe.text_encoder_2, pipe.transformer]
))
pipe.image_encoder.to(onload_device)
pipe.vae.to(onload_device)
pipe.vae.enable_tiling()
image = load_image("penguin.png")
output = pipe(
image=image,
prompt="A penguin dancing in the snow",
height=832,
width=480,
num_frames=91,
num_inference_steps=30,
guidance_scale=9.0,
generator=torch.Generator().manual_seed(0),
sampling_type="vanilla",
).frames[0]
print(f"Max memory: {torch.cuda.max_memory_allocated() / 1024**3:.3f} GB")
export_to_video(output, "output.mp4", fps=30) |