train_ldm.py

import torch
import yaml
import os
from tqdm import tqdm
from torch.utils.data import DataLoader
from dataset.celeba import create_dataloader
from models import vqvae
from models.unet_cond import UNet
from models.vqvae import VQVAE
from scheduler import LinearNoiseScheduler
from torch.optim import Adam

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


def train(args):
    with open(args.config_path, "r") as f:
        try:
            config = yaml.safe_load(f)
        except yaml.YAMLError as exc:
            print(exc)

    print(config)

    diffusion_config = config["diffusion_params"]
    dataset_config = config["dataset_params"]
    diffusion_model_config = config["ldm_params"]
    autoencoder_config = config["autoencoder_params"]
    train_config = config["train_config"]

    # Create noise scheduler
    scheduler = LinearNoiseScheduler(
        num_timesteps=diffusion_config["num_timesteps"],
        beta_start=diffusion_config["beta_start"],
        beta_end=diffusion_config["beta_end"],
    )

    dataloader = create_dataloader(dataset_config["im_path"])

    ldm_ckpt_path = os.path.join(train_config["task_name"],train_config["ldm_ckpt_name"])
    vqvae_ckpt_path = os.path.join(
        train_config["task_name"], train_config["vqvae_autoencoder_ckpt_name"]
    )

    if os.path.exists