# Inference script for the trained diffusion model
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
from tqdm import tqdm
import math

# [Copy all the model architecture classes here - TimeEmbedding, ResidualBlock, etc.]

def load_model(checkpoint_path, device='cuda'):
    """Load the trained diffusion model"""
    checkpoint = torch.load(checkpoint_path, map_location=device)

    # Initialize model with saved config
    model = SimpleUNet(**checkpoint['model_config'])
    model.load_state_dict(checkpoint['model_state_dict'])
    model.to(device)
    model.eval()

    # Initialize scheduler
    scheduler = DDPMScheduler(**checkpoint['diffusion_config'], device=device)

    return model, scheduler, checkpoint['model_info']

# Usage example:
# model, scheduler, info = load_model('complete_diffusion_model.pth')
# generated_images = generate_images(model, scheduler, num_images=4)