nope no perp

backend/disentangle_concepts.py

@@ -34,7 +34,7 @@ def regenerate_images(model, z, decision_boundary, min_epsilon=-3, max_epsilon=3
     device = torch.device('cpu')
     G = model.to(device) # type: ignore
 
-    if True:
+    if False:
         decision_boundary = z - (np.dot(z, decision_boundary.T) / np.dot(decision_boundary, decision_boundary.T)) * decision_boundary
     # Labels.
     label = torch.zeros([1, G.c_dim], device=device)