
from diffusers import CVVAEModel, AutoencoderKL, UNet3DConditionModelAdapter, ControlNetModel3D, ControlNetModel
import torch
import time

device = torch.device("cuda:0")

vae = CVVAEModel.from_pretrained(
    "/data2/onkar/cvvae/CV-VAE",
    subfolder='vae3d',
    torch_dtype=torch.float16
)
vae.to(device)
print(vae)
vae.requires_grad_(False)

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