wan21-vae

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wangkanai

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Quick Summary

WAN2.1 VAE - 3D Causal Video Variational Autoencoder WAN2.1 VAE is a novel 3D causal Variational Autoencoder specifically designed for high-quality video gener...

Code Examples

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Resolution-Specific Requirementspythonpytorch

import torch
from diffusers import AutoencoderKL

# Load the WAN2.1 VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

print(f"VAE loaded: {vae.config}")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Encoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL
from PIL import Image
import numpy as np

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Prepare video frames (example with dummy data)
# Shape: [batch, channels, frames, height, width]
video_frames = torch.randn(1, 3, 16, 480, 720).half().to("cuda")

# Encode video to latent space
with torch.no_grad():
    latents = vae.encode(video_frames).latent_dist.sample()

print(f"Latent shape: {latents.shape}")
print(f"Compression ratio: {np.prod(video_frames.shape) / np.prod(latents.shape):.2f}x")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Video Decoding Examplepythonpytorch

import torch
from diffusers import AutoencoderKL

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
).to("cuda")

# Decode latents back to video frames
# Assuming you have latents from encoding step
with torch.no_grad():
    reconstructed_video = vae.decode(latents).sample

print(f"Reconstructed video shape: {reconstructed_video.shape}")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

Integration with WAN Modelspythonpytorch

import torch
from diffusers import DiffusionPipeline, AutoencoderKL

# Load custom VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/wan21-vae/vae/wan",
    torch_dtype=torch.float16
)

# Load WAN model with custom VAE
pipe = DiffusionPipeline.from_pretrained(
    "Wan-AI/Wan2.1-T2V-1.3B",
    vae=vae,
    torch_dtype=torch.float16
).to("cuda")

# Generate video
prompt = "A serene beach at sunset with waves crashing"
video = pipe(prompt, num_frames=16, height=480, width=720).frames

print(f"Generated video: {len(video)} frames")

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