Update diffusion.py

diffusion.py

@@ -49,9 +49,9 @@ class Diffusion_cond:
             for i in tqdm(reversed(range(1, self.noise_steps)), position=0): # reverse loop from T to 1
                 t = (torch.ones(n) * i).long().to(self.device) # create timesteps tensor of length n
                 predicted_noise = model(x, y, labels, t)
-                if cfg_scale > 0:
-                    uncond_predicted_noise = model(x, y, None, t)
-                    predicted_noise = torch.lerp(uncond_predicted_noise, predicted_noise, cfg_scale)
+                # if cfg_scale > 0:
+                #    uncond_predicted_noise = model(x, y, None, t)
+                #    predicted_noise = torch.lerp(uncond_predicted_noise, predicted_noise, cfg_scale)
                  
                 
                 alpha = self.alpha[t][:, None, None, None]