update readme, cleanup

README.md

@@ -3,7 +3,7 @@
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/kuprel/min-dalle/blob/main/min_dalle.ipynb)  
 [![Replicate](https://replicate.com/kuprel/min-dalle/badge)](https://replicate.com/kuprel/min-dalle)
 
-This is a minimal implementation of [DALL·E Mini](https://github.com/borisdayma/dalle-mini).  It has been stripped to the bare essentials necessary for doing inference, and converted to PyTorch.  The only third party dependencies are numpy, torch, and flax (and optionally wandb to download the models).  
+This is a minimal implementation of Boris Dayma's [DALL·E Mini](https://github.com/borisdayma/dalle-mini).  It has been stripped to the bare essentials necessary for doing inference, and converted to PyTorch.  To run the torch model, the only third party dependencies are numpy and torch.  Flax is used to convert the weights (which can be saved with `torch.save` once the model is loaded), and wandb is only used to download the models.
 
 It currently takes **7.3 seconds** to generate an avocado armchair with DALL·E Mega in PyTorch on Colab
 

min_dalle/min_dalle_torch.py

@@ -1,5 +1,3 @@
-from random import sample
-import numpy
 import os
 from PIL import Image
 from typing import Dict

min_dalle/models/dalle_bart_decoder_torch.py

@@ -36,7 +36,8 @@ class DecoderSelfAttentionTorch(AttentionTorch):
             attention_state
         )
         batch_count = decoder_state.shape[0]
-        keys, values = attention_state[:batch_count], attention_state[batch_count:]
+        keys = attention_state[:batch_count]
+        values = attention_state[batch_count:]
         decoder_state = super().forward(keys, values, queries, attention_mask)
         return decoder_state, attention_state