3.4 KiB
Vendored
3.4 KiB
Vendored
min(DALL·E)
This is a fast, minimal implementation of Boris Dayma's DALL·E Mini. It has been stripped for inference and converted to PyTorch. The only third party dependencies are numpy, requests, pillow and torch.
To generate a 3x3 grid of DALL·E Mega images it takes
- 35 seconds with a P100 in Colab
- 15 seconds with an A100 on Replicate
- TBD with an H100 (@NVIDIA?)
The flax model and code for converting it to torch can be found here.
Install
$ pip install min-dalle
Usage
Python
Load the model parameters once and reuse the model to generate multiple images.
from min_dalle import MinDalle
model = MinDalle(is_mega=True, models_root='./pretrained')
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 and a seed as many times as you want.
text = 'a comfy chair that looks like an avocado'
image = model.generate_image(text)
display(image)
text = 'court sketch of godzilla on trial'
image = model.generate_image(text, seed=6, grid_size=3)
display(image)
text = 'Rusty Iron Man suit found abandoned in the woods being reclaimed by nature'
image = model.generate_image(text, seed=0, grid_size=3)
display(image)
text = 'a funeral at Whole Foods'
image = model.generate_image(text, seed=10, grid_size=3)
display(image)
text = 'Jesus turning water into wine on Americas Got Talent'
image = model.generate_image(text, seed=2, grid_size=3)
display(image)
text = 'cctv footage of Yoda robbing a liquor store'
image = model.generate_image(text, seed=0, grid_size=3)
display(image)
Command Line
Use image_from_text.py to generate images from the command line.
$ python image_from_text.py --text='artificial intelligence' --seed=7
$ python image_from_text.py --text='trail cam footage of gollum eating watermelon' --mega --seed=1 --grid-size=3
