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generation/2d_diffusion_autoencoder/2d_diffusion_autoencoder_tutorial.py

@@ -14,15 +14,15 @@
 # ---
 
 # %% [markdown]
-# Copyright (c) MONAI Consortium  
-# Licensed under the Apache License, Version 2.0 (the "License");  
-# you may not use this file except in compliance with the License.  
-# You may obtain a copy of the License at  
-#     http://www.apache.org/licenses/LICENSE-2.0  
-# Unless required by applicable law or agreed to in writing, software  
-# distributed under the License is distributed on an "AS IS" BASIS,  
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  
-# See the License for the specific language governing permissions and  
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
 # limitations under the License.
 
 # %% [markdown]
@@ -48,7 +48,7 @@
 #
 
 # %% [markdown]
-# ## Setup environment 
+# ## Setup environment
 
 # %%
 # !python -c "import monai" || pip install -q "monai-weekly[tqdm]"
@@ -194,6 +194,7 @@
 # The `embedding_dimension` parameter controls the dimension of the latent dimension learned by the semantic encoder.
 #
 
+
 # %% jupyter={"outputs_hidden": false}
 class Diffusion_AE(torch.nn.Module):
     def __init__(self, embedding_dimension=64):
@@ -407,7 +408,7 @@ def forward(self, xt, x_cond, t):
 )
 
 # %% [markdown]
-# Although not perfectly, the manipulated slices do not present a tumour (unlike the middle - "reconstructed" - ones), because we tweaked the latents to move away from the abnormality cluster: 
+# Although not perfectly, the manipulated slices do not present a tumour (unlike the middle - "reconstructed" - ones), because we tweaked the latents to move away from the abnormality cluster:
 
 # %%
 nb = 8
@@ -421,7 +422,7 @@ def forward(self, xt, x_cond, t):
 plt.figure(figsize=(15, 5))
 plt.imshow(grid.detach().cpu().numpy()[0], cmap="gray")
 plt.axis("off")
-plt.title(f"Original (top), Reconstruction (middle), Manipulated (bottom) s = {s}");
+plt.title(f"Original (top), Reconstruction (middle), Manipulated (bottom) s = {s}")
 
 # %%
 if directory is None: