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import os
os.environ["HF_ENABLE_PARALLEL_LOADING"] = "yes"
import torch
from diffusers import AutoencoderKLWan
from diffusers.video_processor import VideoProcessor
from diffusers.utils import export_to_video
device = "cuda"
pretrained_model_name_or_path = "/mnt/bn/yufan-dev-my/ysh/Ckpts/Wan-AI/Wan2.1-I2V-14B-720P-Diffusers/"
vae = AutoencoderKLWan.from_pretrained(
pretrained_model_name_or_path,
subfolder="vae",
torch_dtype=torch.float32,
).to(device)
vae.eval()
vae.requires_grad_(False)
vae.enable_tiling()
vae_scale_factor_spatial = vae.spatial_compression_ratio
video_processor = VideoProcessor(vae_scale_factor=vae_scale_factor_spatial)
latents = torch.load('/mnt/bn/yufan-dev-my/ysh/Datasets/fp_offload_latents_wan/6ad434bc-df9b-40be-9632-c8f9508f1ccc_121_768_384.pt', map_location='cpu', weights_only=False)
latents_mean = torch.tensor(vae.config.latents_mean).view(1, vae.config.z_dim, 1, 1, 1)
latents_std = 1.0 / torch.tensor(vae.config.latents_std).view(1, vae.config.z_dim, 1, 1, 1)
vae_latents = latents['vae_latent'] / latents_std + latents_mean
vae_latents = vae_latents.to(device=device, dtype=vae.dtype)
video = vae.decode(vae_latents, return_dict=False)[0]
video = video_processor.postprocess_video(video, output_type="pil")
export_to_video(video[0], "output_wan.mp4", fps=30) |