diff --git a/Train_Colab.ipynb b/Train_Colab.ipynb
index f3d0203..0f76d25 100644
--- a/Train_Colab.ipynb
+++ b/Train_Colab.ipynb
@@ -290,7 +290,7 @@
         "\n",
         "#@markdown * Batch size impacts VRAM use.  8 should work on SD1.x models and 5 for SD2.x models at 512 resolution.  Lower this if you get CUDA out of memory errors. You can check resources on your instance and watch the GPU RAM.\n",
         "\n",
-        "Batch_Size = 8 #@param{type: 'number'}\n",
+        "Batch_Size = 6 #@param{type: 'number'}\n",
         "\n",
         "#@markdown * Gradient accumulation is sort of like a virtual batch size increase use this to increase batch size with out increasing vram usage\n",
         "#@markdown Increasing from 1 to 2 will have a minor impact on vram use, but more beyond that will not.\n",
@@ -306,7 +306,7 @@
         "\n",
         "#@markdown * Max Epochs to train for, this defines how many total times all your training data is used. Default of 100 is a good start if you are training ~30-40 images of one subject. If you have 100 images, you can reduce this to 40-50 and so forth.\n",
         "\n",
-        "Max_Epochs = 200 #@param {type:\"slider\", min:0, max:200, step:5}\n",
+        "Max_Epochs = 100 #@param {type:\"slider\", min:0, max:200, step:5}\n",
         "\n",
         "#@markdown * How often to save checkpoints.\n",
         "Save_every_N_epoch = 20 #@param{type:\"integer\"}\n",