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Brett Kuprel 2022-07-01 19:08:42 -04:00
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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 and torch.
It currently takes **7.4 seconds** to generate an image with DALL·E Mega on a standard GPU runtime in Colab
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 the code for coverting it to torch can be found [here](https://github.com/kuprel/min-dalle-flax).
The flax model and the code for coverting it to torch can be found [here](https://github.com/kuprel/min-dalle-flax).
## Install
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```
![Godzilla Trial](examples/godzilla_on_trial.png)
### Python
To load a model once and generate multiple times, first initialize `MinDalleTorch`
To load a model once and generate multiple times, first initialize `MinDalleTorch`.
```python
from min_dalle import MinDalleTorch
@ -46,19 +47,19 @@ model = MinDalleTorch(
)
```
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
image = model.generate_image("a comfy chair that looks like an avocado")
text = "a comfy chair that looks like an avocado"
image = model.generate_image(text)
display(image)
```
![Avocado Armchair](examples/avocado_armchair.png)
```python
image = model.generate_image(
"trail cam footage of gollum eating watermelon",
seed=1
)
text = "trail cam footage of gollum eating watermelon"
image = model.generate_image(text, seed=1)
display(image)
```
![Gollum Trailcam](examples/gollum_trailcam.png)