Update sampling.py

sampling.py

@@ -40,16 +40,7 @@ def sample_weights(unet, proj, mean, std, v, device, factor = 1.0):
     for i in range(1000):
         sample[0, i] = torch.normal(m[i], factor*standev[i], (1,1))
 
-    # load weights into network
-    network = LoRAw2w( sample, mean, std, v, 
-                    unet,
-                    rank=1,
-                    multiplier=1.0,
-                    alpha=27.0,
-                    train_method="xattn-strict"
-                ).to(device, torch.bfloat16)
-        
-    return network
+    return sample