Update app.py

app.py

@@ -55,9 +55,10 @@ def predict(Seed,noise_mode,truncation_psi,trans_x,trans_y,angle):
     # generator expects this matrix as an inverse to avoid potentially failing numerical
     # operations in the network.
     
-    m = make_transform((trans_x,trans_y), angle)
-    m = np.linalg.inv(m)
-    G.synthesis.input.transform.copy_(torch.from_numpy(m))
+    if hasattr(G.synthesis, 'input'):
+        m = make_transform((trans_x,trans_y), angle)
+        m = np.linalg.inv(m)
+        G.synthesis.input.transform.copy_(torch.from_numpy(m))
 
     img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode)
     img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)