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Running
on
Zero
File size: 1,653 Bytes
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import os
import torch
from pipelines.wan_video import WanVideoPipeline, ModelConfig
from pipelines.wan_video_face_swap import WanVideoPipeline_FaceSwap
def load_wan_pipe(
base_path, torch_dtype=torch.bfloat16, face_swap=False, use_vace=False, device="cuda"
):
if not use_vace:
diffusion_model_files = [
f"diffusion_pytorch_model-0000{i}-of-00006.safetensors" for i in range(1, 7)
]
else:
diffusion_model_files = [
f"diffusion_pytorch_model-0000{i}-of-00007.safetensors" for i in range(1, 8)
]
diffusion_model_paths = [
os.path.join(base_path, fname) for fname in diffusion_model_files
]
pipe_cls = WanVideoPipeline_FaceSwap if face_swap else WanVideoPipeline
pipe = pipe_cls.from_pretrained(
torch_dtype=torch_dtype,
device=device,
model_configs=[
ModelConfig(
path=diffusion_model_paths,
offload_device="cpu",
skip_download=True,
),
ModelConfig(
path=os.path.join(base_path, "models_t5_umt5-xxl-enc-bf16.pth"),
offload_device="cpu",
skip_download=True,
),
ModelConfig(
path=os.path.join(base_path, "Wan2.1_VAE.pth"),
offload_device="cpu",
skip_download=True,
),
],
tokenizer_config=ModelConfig(
path=os.path.join(base_path, "google/umt5-xxl/"),
offload_device="cpu",
skip_download=True,
),
)
pipe.enable_vram_management()
return pipe
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