38 lines
1.7 KiB
Markdown
38 lines
1.7 KiB
Markdown
# min(DALL·E)
|
|
|
|
[](https://colab.research.google.com/github/kuprel/min-dalle/blob/main/min_dalle.ipynb)
|
|
|
|
This is a minimal implementation of [DALL·E Mini](https://github.com/borisdayma/dalle-mini). It has been stripped to the bare essentials necessary for doing inference, and converted to PyTorch. The only third party dependencies are `numpy`, `torch`, and `flax`. PyTorch inference with DALL·E Mega takes about 10 seconds in colab.
|
|
|
|
### Setup
|
|
|
|
Run `sh setup.sh` to install dependencies and download pretrained models. The `wandb` python package is installed to download DALL·E mini and DALL·E mega. Alternatively, the models can be downloaded manually here:
|
|
[VQGan](https://huggingface.co/dalle-mini/vqgan_imagenet_f16_16384),
|
|
[DALL·E Mini](https://wandb.ai/dalle-mini/dalle-mini/artifacts/DalleBart_model/mini-1/v0/files),
|
|
[DALL·E Mega](https://wandb.ai/dalle-mini/dalle-mini/artifacts/DalleBart_model/mega-1-fp16/v14/files)
|
|
|
|
### Usage
|
|
|
|
The simplest way to get started is the command line python script `image_from_text.py` provided. Here are some examples runs:
|
|
|
|
```
|
|
python3 image_from_text.py --text='artificial intelligence' --torch
|
|
```
|
|
|
|
|
|
|
|
```
|
|
python image_from_text.py --text='a comfy chair that looks like an avocado' --torch --mega --seed=10
|
|
```
|
|
|
|
|
|
|
|
```
|
|
python image_from_text.py --text='court sketch of godzilla on trial' --mega --seed=100
|
|
```
|
|
|
|
|
|
|
|
### Load once run multiple times
|
|
|
|
The command line script loads the models and parameters each time. The colab notebook demonstrates how to load the models once and run multiple times. |