diff --git a/test/test_multiscale_bijections.py b/test/test_multiscale_bijections.py
index fa1b181..08bb548 100644
--- a/test/test_multiscale_bijections.py
+++ b/test/test_multiscale_bijections.py
@@ -1,9 +1,19 @@
-from normalizing_flows.architectures import MultiscaleNICE, MultiscaleRQNSF, MultiscaleLRSNSF, MultiscaleRealNVP
+from normalizing_flows.architectures import (
+    MultiscaleNICE,
+    MultiscaleRQNSF,
+    MultiscaleLRSNSF,
+    MultiscaleRealNVP
+)
 import torch
 import pytest
 
 
-@pytest.mark.parametrize('architecture_class', [MultiscaleRQNSF, MultiscaleLRSNSF, MultiscaleNICE, MultiscaleRealNVP])
+@pytest.mark.parametrize('architecture_class', [
+    MultiscaleRQNSF,
+    MultiscaleLRSNSF,
+    MultiscaleNICE,
+    MultiscaleRealNVP
+])
 @pytest.mark.parametrize('image_shape', [(1, 28, 28), (3, 28, 28)])
 def test_non_factored(architecture_class, image_shape):
     torch.manual_seed(0)
@@ -15,7 +25,12 @@ def test_non_factored(architecture_class, image_shape):
     assert torch.allclose(ldf, -ldi, atol=1e-2)
 
 
-@pytest.mark.parametrize('architecture_class', [MultiscaleRQNSF, MultiscaleLRSNSF, MultiscaleNICE, MultiscaleRealNVP])
+@pytest.mark.parametrize('architecture_class', [
+    MultiscaleRQNSF,
+    MultiscaleLRSNSF,
+    MultiscaleNICE,
+    MultiscaleRealNVP
+])
 @pytest.mark.parametrize('image_shape', [(1, 28, 28), (3, 28, 28)])
 def test_non_factored_too_small_image(architecture_class, image_shape):
     torch.manual_seed(0)
@@ -23,7 +38,12 @@ def test_non_factored_too_small_image(architecture_class, image_shape):
         bijection = architecture_class(image_shape, n_layers=3, factored=False)
 
 
-@pytest.mark.parametrize('architecture_class', [MultiscaleRQNSF, MultiscaleLRSNSF, MultiscaleNICE, MultiscaleRealNVP])
+@pytest.mark.parametrize('architecture_class', [
+    MultiscaleRQNSF,
+    MultiscaleLRSNSF,
+    MultiscaleNICE,
+    MultiscaleRealNVP
+])
 @pytest.mark.parametrize('image_shape', [(1, 32, 32), (3, 32, 32)])
 def test_factored(architecture_class, image_shape):
     torch.manual_seed(0)
@@ -35,7 +55,12 @@ def test_factored(architecture_class, image_shape):
     assert torch.allclose(ldf, -ldi, atol=1e-2)
 
 
-@pytest.mark.parametrize('architecture_class', [MultiscaleRQNSF, MultiscaleLRSNSF, MultiscaleNICE, MultiscaleRealNVP])
+@pytest.mark.parametrize('architecture_class', [
+    MultiscaleRQNSF,
+    MultiscaleLRSNSF,
+    MultiscaleNICE,
+    MultiscaleRealNVP
+])
 @pytest.mark.parametrize('image_shape', [(1, 15, 32), (3, 15, 32)])
 def test_factored_wrong_shape(architecture_class, image_shape):
     torch.manual_seed(0)
@@ -44,10 +69,41 @@ def test_factored_wrong_shape(architecture_class, image_shape):
         bijection = architecture_class(image_shape, n_layers=2, factored=True)
 
 
-@pytest.mark.parametrize('architecture_class', [MultiscaleRQNSF, MultiscaleLRSNSF, MultiscaleNICE, MultiscaleRealNVP])
+@pytest.mark.parametrize('architecture_class', [
+    MultiscaleRQNSF,
+    MultiscaleLRSNSF,
+    MultiscaleNICE,
+    MultiscaleRealNVP
+])
 @pytest.mark.parametrize('image_shape', [(1, 8, 8), (3, 8, 8)])
 def test_factored_too_small_image(architecture_class, image_shape):
     torch.manual_seed(0)
     x = torch.randn(size=(5, *image_shape))
     with pytest.raises(ValueError):
         bijection = architecture_class(image_shape, n_layers=8, factored=True)
+
+
+@pytest.mark.parametrize('architecture_class', [
+    MultiscaleRQNSF,
+    MultiscaleLRSNSF,
+    MultiscaleNICE,
+    MultiscaleRealNVP
+])
+@pytest.mark.parametrize('image_shape', [(1, 4, 4), (3, 4, 4)])
+def test_non_factored_automatic_n_layers(architecture_class, image_shape):
+    torch.manual_seed(0)
+    x = torch.randn(size=(5, *image_shape))
+    bijection = architecture_class(image_shape, factored=False)
+
+
+@pytest.mark.parametrize('architecture_class', [
+    MultiscaleRQNSF,
+    MultiscaleLRSNSF,
+    MultiscaleNICE,
+    MultiscaleRealNVP
+])
+@pytest.mark.parametrize('image_shape', [(1, 4, 8), (3, 4, 4)])
+def test_factored_automatic_n_layers(architecture_class, image_shape):
+    torch.manual_seed(0)
+    x = torch.randn(size=(5, *image_shape))
+    bijection = architecture_class(image_shape, factored=True)