dtype dropdown in colab

README.md

@@ -6,7 +6,7 @@
  
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-This is a fast, minimal port of Boris Dayma's [DALL·E Mega](https://github.com/borisdayma/dalle-mini).  It has been stripped down for inference and converted to PyTorch.  The only third party dependencies are numpy, requests, pillow and torch.
+This is a fast, minimal port of [DALL·E Mega](https://github.com/borisdayma/dalle-mini).  It has been stripped down for inference and converted to PyTorch.  The only third party dependencies are numpy, requests, pillow and torch.
 
 To generate a 4x4 grid of DALL·E Mega images it takes:
 - 89 sec with a T4 in Colab

min_dalle.ipynb

@@ -102,7 +102,7 @@
       },
       "source": [
         "### Load Model\n",
-        "Float32 is faster but uses more GPU memory.  Change the `grid_size` to 3 or less if using float32."
+        "`float32` is faster than `float16` but uses more GPU memory.  Change the `grid_size` to 3 or less if using `float32`."
       ]
     },
     {
@@ -128,13 +128,14 @@
         }
       ],
       "source": [
+        "dtype = \"float32\" #@param [\"float32\", \"float16\", \"bfloat16\"]\n",
         "from IPython.display import display, update_display\n",
         "from math import log2\n",
         "import torch\n",
         "from min_dalle import MinDalle\n",
         "\n",
         "model = MinDalle(\n",
-        "    dtype=torch.float16,\n",
+        "    dtype=getattr(torch, dtype),\n",
         "    is_mega=True, \n",
         "    is_reusable=True\n",
         ")"