diff --git a/README.ipynb b/README.ipynb
index c1ec1d289646b695e700f1c3bbbd8e99aff5a2c3..ba1081d3cc64cf9a6fa182fca4e3c047c5015efa 100644
--- a/README.ipynb
+++ b/README.ipynb
@@ -94,21 +94,21 @@
        "[[SYNOP3] - Time series with RNN - 12h predictions](SYNOP/03-12h-predictions.ipynb)  \n",
        "      Episode 3: Attempt to predict in the longer term   \n",
        "[[VAE1] - Variational AutoEncoder (VAE) with MNIST](VAE/01-VAE-with-MNIST.ipynb)  \n",
-       "      First generative network experience with the MNIST dataset  \n",
+       "      Episode 1 : Model construction and Training  \n",
        "[[VAE2] - Variational AutoEncoder (VAE) with MNIST - Analysis](VAE/02-VAE-with-MNIST-post.ipynb)  \n",
-       "      Use of the previously trained model, analysis of the results  \n",
+       "      Episode 2 : Exploring our latent space  \n",
        "[[VAE3] - About the CelebA dataset](VAE/03-About-CelebA.ipynb)  \n",
-       "      New VAE experience, but with a larger and more fun dataset  \n",
+       "      Episode 3 : About the CelebA dataset, a more fun dataset !  \n",
        "[[VAE4] - Preparation of the CelebA dataset](VAE/04-Prepare-CelebA-batch.ipynb)  \n",
-       "      Preparation of a clustered dataset, batchable  \n",
+       "      Episode 4 : Preparation of a clustered dataset, batchable  \n",
        "[[VAE5] - Checking the clustered CelebA dataset](VAE/05-Check-CelebA.ipynb)  \n",
-       "      Verification of prepared data from CelebA dataset  \n",
+       "      Episode 5 :\\tChecking the clustered dataset  \n",
        "[[VAE6] - Variational AutoEncoder (VAE) with CelebA (small)](VAE/06-VAE-with-CelebA-s.ipynb)  \n",
-       "      VAE with a more fun and realistic dataset - small resolution and batchable  \n",
+       "      Episode 6 : Variational AutoEncoder (VAE) with CelebA (small res.)  \n",
        "[[VAE7] - Variational AutoEncoder (VAE) with CelebA (medium)](VAE/07-VAE-with-CelebA-m.ipynb)  \n",
-       "      VAE with a more fun and realistic dataset - medium resolution and batchable  \n",
-       "[[VAE12] - Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/12-VAE-withCelebA-post.ipynb)  \n",
-       "      Use of the previously trained model with CelebA, analysis of the results  \n",
+       "      Episode 7 : Variational AutoEncoder (VAE) with CelebA (medium res.)  \n",
+       "[[VAE8] - Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/08-VAE-withCelebA-post.ipynb)  \n",
+       "      Episode 8 : Exploring latent space of our trained models  \n",
        "[[BASH1] - OAR batch script](VAE/batch-oar.sh)  \n",
        "      Bash script for OAR batch submission of a notebook  \n",
        "[[BASH2] - SLURM batch script](VAE/batch-slurm.sh)  \n",
@@ -142,13 +142,6 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The history saving thread hit an unexpected error (DatabaseError('database disk image is malformed')).History will not be written to the database.\n"
-     ]
     }
    ],
    "source": [
diff --git a/README.md b/README.md
index 9b97450162534925baf754cd8fefadb533455c2a..b87ec08fdf5a719ab3638a7f78950fb3c6e5d546 100644
--- a/README.md
+++ b/README.md
@@ -93,7 +93,7 @@ Useful information is also available in the [wiki](https://gricad-gitlab.univ-gr
       Episode 6 : Variational AutoEncoder (VAE) with CelebA (small res.)  
 [[VAE7] - Variational AutoEncoder (VAE) with CelebA (medium)](VAE/07-VAE-with-CelebA-m.ipynb)  
       Episode 7 : Variational AutoEncoder (VAE) with CelebA (medium res.)  
-[[VAE12] - Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/08-VAE-withCelebA-post.ipynb)  
+[[VAE8] - Variational AutoEncoder (VAE) with CelebA - Analysis](VAE/08-VAE-withCelebA-post.ipynb)  
       Episode 8 : Exploring latent space of our trained models  
 [[BASH1] - OAR batch script](VAE/batch-oar.sh)  
       Bash script for OAR batch submission of a notebook  
diff --git a/VAE/07-VAE-with-CelebA-m.ipynb b/VAE/07-VAE-with-CelebA-m.ipynb
index 394355680c142600e2d2d7c2d942f4f49b570185..bfe64fd69b0e8a73da56f639effaf240c38517f6 100644
--- a/VAE/07-VAE-with-CelebA-m.ipynb
+++ b/VAE/07-VAE-with-CelebA-m.ipynb
@@ -179,7 +179,8 @@
     "## Step 5 - Train\n",
     "For 10 epochs, adam optimizer :  \n",
     "- Run time at IDRIS : 1299.77 sec. - 0:21:39\n",
-    "- Run time at GRICAD : 2092.77 sec. - 0:34:52"
+    "- Run time at GRICAD : 2092.77 sec. - 0:34:52\n",
+    "- Run time at IDRIS with medium resolution : Train duration : 6638.61 sec. - 1:50:38"
    ]
   },
   {
diff --git a/VAE/07-VAE-with-CelebA-m.nbconvert.ipynb b/VAE/07-VAE-with-CelebA-m.nbconvert-done.ipynb
similarity index 98%
rename from VAE/07-VAE-with-CelebA-m.nbconvert.ipynb
rename to VAE/07-VAE-with-CelebA-m.nbconvert-done.ipynb
index 13c392d4706e62967a83a3bfca894d816d21a256..46f4b4e41c372deff9b605b37b11c758ca21bcb6 100644
--- a/VAE/07-VAE-with-CelebA-m.nbconvert.ipynb
+++ b/VAE/07-VAE-with-CelebA-m.nbconvert-done.ipynb
@@ -262,7 +262,8 @@
     "## Step 5 - Train\n",
     "For 10 epochs, adam optimizer :  \n",
     "- Run time at IDRIS : 1299.77 sec. - 0:21:39\n",
-    "- Run time at GRICAD : 2092.77 sec. - 0:34:52"
+    "- Run time at GRICAD : 2092.77 sec. - 0:34:52\n",
+    "- At IDRIS with medium resolution : Train duration : 6638.61 sec. - 1:50:38"
    ]
   },
   {
diff --git a/VAE/08-VAE-withCelebA-post.ipynb b/VAE/08-VAE-withCelebA-post.ipynb
index 3ed0684598d722a851281f53240542e242437011..0f9c5a6850db8b3f51679c0327ab80716e2f5703 100644
--- a/VAE/08-VAE-withCelebA-post.ipynb
+++ b/VAE/08-VAE-withCelebA-post.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
     "\n",
-    "# <!-- TITLE --> [VAE12] - Variational AutoEncoder (VAE) with CelebA - Analysis\n",
+    "# <!-- TITLE --> [VAE8] - Variational AutoEncoder (VAE) with CelebA - Analysis\n",
     "<!-- DESC --> Episode 8 : Exploring latent space of our trained models\n",
     "<!-- AUTHOR : Jean-Luc Parouty (CNRS/SIMaP) -->\n",
     "\n",