support bfloat16

README.md

@@ -12,7 +12,6 @@ To generate a 4x4 grid of DALL·E Mega images it takes:
 - 89 sec with a T4 in Colab
 - 48 sec with a P100 in Colab
 - 14 sec with an A100 on Replicate
-- TBD with an H100 (@NVIDIA?)
 
 The flax model and code for converting it to torch can be found [here](https://github.com/kuprel/min-dalle-flax).
 
@@ -30,13 +29,14 @@ Load the model parameters once and reuse the model to generate multiple images.
 from min_dalle import MinDalle
 
 model = MinDalle(
+    models_root='./pretrained',
+    dtype=torch.float32,
     is_mega=True, 
-    is_reusable=True,
-    models_root='./pretrained'
+    is_reusable=True
 )
 ```
 
-The required models will be downloaded to `models_root` if they are not already there.  Once everything has finished initializing, call `generate_image` with some text as many times as you want.  Use a positive `seed` for reproducible results.  Higher values for `log2_supercondition_factor` result in better agreement with the text but a narrower variety of generated images.  Every image token is sampled from the top $k$ most probable tokens.
+The required models will be downloaded to `models_root` if they are not already there.  If you have an Ampere architecture GPU you can set the `dtype=torch.bfloat16` and save GPU memory.  There is still an issue with `dtype=torch.float16` that needs to be sorted out.  Once everything has finished initializing, call `generate_image` with some text as many times as you want.  Use a positive `seed` for reproducible results.  Higher values for `log2_supercondition_factor` result in better agreement with the text but a narrower variety of generated images.  Every image token is sampled from the top-$k$ most probable tokens.
 
 ```python
 image = model.generate_image(