diffusion.py

import torch
import torch.nn as nn

# Define the Stable Diffusion model architecture
class DiffusionModel(nn.Module):
    def __init__(self):
        super(DiffusionModel, self).__init__()
        # Define the layers of the model

    def forward(self, x):
        # Define the forward pass of the model
        return x

# Additional functions for training and sampling with the model

def train_model(model, data_loader, optimizer, criterion, num_epochs):
    # Training loop to train the model

def sample(model, z, clip_denoised=False):
    # Function to sample images from the model

# Additional code for hyperparameters, utility functions, etc.