64 lines
2.0 KiB
Markdown
Vendored
64 lines
2.0 KiB
Markdown
Vendored
# min(DALL·E)
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[](https://colab.research.google.com/github/kuprel/min-dalle/blob/main/min_dalle.ipynb)
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[](https://replicate.com/kuprel/min-dalle)
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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.
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It currently takes **7.4 seconds** to generate an image with DALL·E Mega with PyTorch on a standard GPU runtime in Colab
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The flax model, and the code for coverting it to torch, have been moved [here](https://github.com/kuprel/min-dalle-flax).
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## Install
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```bash
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$ pip install min-dalle
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```
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## Usage
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### Command Line
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Use the python script `image_from_text.py` to generate images from the command line.
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```bash
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$ python image_from_text.py --text='artificial intelligence' --seed=7
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```
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```bash
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$ python image_from_text.py --text='court sketch of godzilla on trial' --mega
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```
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### Python
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To load a model once and generate multiple times, first initialize `MinDalleTorch`
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```python
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from min_dalle import MinDalleTorch
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model = MinDalleTorch(
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is_mega=True,
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is_reusable=True,
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models_root='./pretrained'
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)
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```
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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.
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```python
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image = model.generate_image("a comfy chair that looks like an avocado")
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display(image)
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```
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```python
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image = model.generate_image(
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"trail cam footage of gollum eating watermelon",
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seed=1
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)
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display(image)
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```
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