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# 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)
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