From fff44d683e51e3ae0b6cd62d36cbfe025d7d69b4 Mon Sep 17 00:00:00 2001
From: Brett Kuprel <brkuprel@gmail.com>
Date: Fri, 1 Jul 2022 18:50:11 -0400
Subject: [PATCH] update readme, random seed if none specified

---
 README.md                    | 46 ++++++++++++++++--------------------
 min_dalle/min_dalle_torch.py |  2 ++
 2 files changed, 22 insertions(+), 26 deletions(-)

diff --git a/README.md b/README.md
index a7f7149..72121af 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,7 @@ The flax model, and the code for coverting it to torch, have been moved [here](h
 
 ### Install
 
-```bash
+```zsh
 $ pip install min-dalle
 ```  
 
@@ -19,6 +19,16 @@ $ pip install min-dalle
 
 Use the python script `image_from_text.py` to generate images from the command line.
 
+```zsh
+$ python image_from_text.py --text='artificial intelligence' --seed=7
+```
+![Artificial Intelligence](examples/artificial_intelligence.png)
+
+```zsh
+$ python image_from_text.py --text='court sketch of godzilla on trial' --mega
+```
+![Godzilla Trial](examples/godzilla_on_trial.png)
+
 To load a model once and generate multiple times, initialize `MinDalleTorch`, then call `generate_image` with some text and a seed.
 
 ```python
@@ -29,33 +39,17 @@ model = MinDalleTorch(
     is_reusable=True,
     models_root='./pretrained'
 )
-
-image = model.generate_image("court sketch of godzilla on trial", seed=40)
 ```
 
-Model parameters will be downloaded as needed to the directory specified.  The models can also be manually downloaded [here](https://huggingface.co/kuprel/min-dalle/tree/main).
-
-### Examples
-
+```python
+image = model.generate_image("a comfy chair that looks like an avocado")
+display(image)
 ```
-python image_from_text.py --text='artificial intelligence' --seed=7
-```
-![Alien](examples/artificial_intelligence.png)
 
+```python
+image = model.generate_image("trail cam footage of gollum eating watermelon", seed=1)
+display(image)
+```
+![Gollum Trailcam](examples/gollum_trailcam.png)
 
-```
-python image_from_text.py --text='a comfy chair that looks like an avocado' --mega --seed=10
-```
-![Avocado Armchair](examples/avocado_armchair.png)
-
-
-```
-python image_from_text.py --text='court sketch of godzilla on trial' --mega --seed=40
-```
-![Godzilla Trial](examples/godzilla_on_trial.png)
-
-
-```
-python image_from_text.py --text='trail cam footage of gollum eating watermelon' --mega --seed=1
-```
-![Gollum Trailcam](examples/gollum_trailcam.png)
\ No newline at end of file
+Model parameters will be downloaded as needed to the directory specified.  The models can also be manually downloaded [here](https://huggingface.co/kuprel/min-dalle/tree/main).
\ No newline at end of file
diff --git a/min_dalle/min_dalle_torch.py b/min_dalle/min_dalle_torch.py
index 2950f0a..212f461 100644
--- a/min_dalle/min_dalle_torch.py
+++ b/min_dalle/min_dalle_torch.py
@@ -6,6 +6,7 @@ from torch import LongTensor
 import torch
 import json
 import requests
+import random
 torch.set_grad_enabled(False)
 torch.set_num_threads(os.cpu_count())
 
@@ -167,6 +168,7 @@ class MinDalleTorch:
 
         if not self.is_reusable: self.init_decoder()
         print("sampling image tokens")
+        if seed < 0: seed = random.randint(0, 2 ** 31)
         torch.manual_seed(seed)
         image_tokens = self.decoder.forward(text_tokens, encoder_state)
         if not self.is_reusable: del self.decoder