62 lines
2.0 KiB
Markdown
Vendored
62 lines
2.0 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)
|
|
|
|
[](https://discord.gg/xBPBXfcFHd)
|
|
|
|
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 on a standard GPU runtime in Colab.
|
|
|
|
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
|
|
|
|
To load a model once and generate multiple times, first initialize `MinDalle`.
|
|
|
|
```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. After the model has loaded, 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)
|
|
```
|
|
|
|
|
|
```python
|
|
text = "trail cam footage of gollum eating watermelon"
|
|
image = model.generate_image(text, seed=1)
|
|
display(image)
|
|
```
|
|
|
|
|
|
|
|
### 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
|
|
```
|
|
|
|
|
|
```bash
|
|
$ python image_from_text.py --text='court sketch of godzilla on trial' --mega
|
|
```
|
|
 |