44 lines
1.9 KiB
Markdown
Vendored
44 lines
1.9 KiB
Markdown
Vendored
# min(DALL·E)
|
|
|
|
[](https://colab.research.google.com/github/kuprel/min-dalle/blob/main/min_dalle.ipynb)
|
|
[](https://replicate.com/kuprel/min-dalle)
|
|
|
|
This is a minimal implementation of Boris Dayma's [DALL·E Mini](https://github.com/borisdayma/dalle-mini) in PyTorch. It has been stripped to the bare essentials necessary for doing inference. 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
|
|
|
|
The flax model, and the code for coverting it to torch, has been moved [here](https://github.com/kuprel/min-dalle-flax).
|
|
|
|
### Setup
|
|
|
|
Run `sh setup.sh` to install dependencies and download pretrained models. The torch models can be manually downloaded [here](https://huggingface.co/kuprel/min-dalle/tree/main).
|
|
|
|
### 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 `MinDalleTorch`, 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
|
|
```
|
|
|
|
|
|
|
|
|
|
```
|
|
python image_from_text.py --text='trail cam footage of gollum eating watermelon' --mega --seed=1
|
|
```
|
|
 |