| examples | ||
| min_dalle | ||
| .gitattributes | ||
| .gitignore | ||
| cog.yaml | ||
| image_from_text.py | ||
| LICENSE | ||
| min_dalle.ipynb | ||
| predict.py | ||
| README.md | ||
| setup_flax.sh | ||
| setup_torch.sh | ||
min(DALL·E)
This is a minimal implementation of Boris Dayma's DALL·E 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.
It currently takes 7.4 seconds to generate an image with DALL·E Mega with PyTorch on a standard GPU runtime in Colab
Setup
Run either sh setup_torch.sh or sh setup_flax.sh to install dependencies and download pretrained models. The torch models can be manually downloaded here.
The flax models can be manually downloaded here:
VQGan,
DALL·E Mini,
DALL·E Mega
Usage
Use the python script image_from_text.py to generate images from the command line. Note: the command line script loads the models and parameters each time. To load a model once and generate multiple times, initialize either MinDalleTorch or MinDalleFlax, then call generate_image with some text and a seed. See the colab for an example.
Examples
python image_from_text.py --text='artificial intelligence' --seed=7
python image_from_text.py --text='a comfy chair that looks like an avocado' --mega --seed=10
python image_from_text.py --text='court sketch of godzilla on trial' --mega --seed=40
