Create diffusion.py

diffusion.py

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+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.