# min(DALL·E)

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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, requests, pillow and torch.

To generate a 3x3 grid of DALL·E Mega images it takes
- **35 seconds** with the standard GPU runtime in Colab
- **15 seconds** with an A100 on replicate

The flax model and code for converting it to torch can be found [here](https://github.com/kuprel/min-dalle-flax).

## Install

```bash
$ pip install min-dalle
```  

## Usage

### Python

Load the model parameters once and reuse the model to generate multiple images.

```python
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.

```python
text = 'a comfy chair that looks like an avocado'
image = model.generate_image(text)
display(image)
```
![Avocado Armchair](https://github.com/kuprel/min-dalle/raw/main/examples/avocado_armchair.png)

```python
text = 'court sketch of godzilla on trial'
image = model.generate_image(text, seed=6, grid_size=3)
display(image)
```
![Godzilla Trial](https://github.com/kuprel/min-dalle/raw/main/examples/godzilla_trial.png)


### Command Line

Use `image_from_text.py` to generate images from the command line.

```bash
$ python image_from_text.py --text='artificial intelligence' --seed=7
```
![Artificial Intelligence](https://github.com/kuprel/min-dalle/raw/main/examples/artificial_intelligence.png)

```bash
$ python image_from_text.py --text='trail cam footage of gollum eating watermelon' --mega --seed=1 --grid-size=3
```
![Gollum Trailcam](https://github.com/kuprel/min-dalle/raw/main/examples/gollum_trailcam.png)