A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN. Technique was originally created by https://twitter.com/advadnoun
I added the ability to train on multiple prompts. Simply pass a \ delimiter ("\\" in python strings) to separate each phrase.
You can also send in phrases to penalize in the output. This can be surprisingly effective (or a total failure).
Train on Multiple Phrases
In this example we train on three phrases:
an armchair in the form of pikachu
an armchair imitating pikachu
abstract
from big_sleep import Imagine
dream = Imagine(
text = "an armchair in the form of pikachu\\an armchair imitating pikachu\\abstract",
lr = 5e-2,
save_every = 25,
save_progress = True
)
dream()
Penalize certain prompts as well!
In this example we train on the three phrases from before,
andpenalize the phrases:
blur
zoom
from big_sleep import Imagine
dream = Imagine(
text = "an armchair in the form of pikachu\an armchair imitating pikachu\abstract",
text_min = "blur\zoom",
)
dream()
### Basic experiment showing it works to a degree
This image is generated from the cell above.
![pika_2](https://user-images.githubusercontent.com/3994972/109432745-528d0a00-79d2-11eb-967d-03f0bfcaa775.png)
What happens if we minimize the word "yellow"?
![armchair_pika_0](https://user-images.githubusercontent.com/3994972/109433102-38ecc200-79d4-11eb-98c7-fc1050def0ce.png)
I added the ability to train on multiple prompts. Simply pass a
\
delimiter ("\\"
in python strings) to separate each phrase.You can also send in phrases to penalize in the output. This can be surprisingly effective (or a total failure).
Train on Multiple Phrases
In this example we train on three phrases:
an armchair in the form of pikachu
an armchair imitating pikachu
abstract
Penalize certain prompts as well!
In this example we train on the three phrases from before,
and penalize the phrases:
blur
zoom
dream = Imagine( text = "an armchair in the form of pikachu\an armchair imitating pikachu\abstract", text_min = "blur\zoom", ) dream()