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+import torch
+
+class GaussianDiffusion(nn.Module):
+    def __init__(self, model, timesteps=1000, beta_start=1e-4, beta_end=0.02):
+        super(GaussianDiffusion, self).__init__()
+        self.model = model
+        self.timesteps = timesteps
+        
+        # Create a schedule of betas (noise variance at each timestep)
+        self.betas = torch.linspace(beta_start, beta_end, timesteps)
+        self.alphas = 1 - self.betas
+        self.alpha_cumprod = torch.cumprod(self.alphas, dim=0)
+
+    def add