63 lines
2.3 KiB
Markdown
Vendored
63 lines
2.3 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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[](https://discord.gg/xBPBXfcFHd)
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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.
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It currently take **35 seconds** to generate a 3x3 grid with DALL·E Mega on a standard GPU runtime in Colab.
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The flax model and code for converting it to torch can be found [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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### Python
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Load the model parameters once and reuse the model to generate multiple images.
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```python
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from min_dalle import MinDalle
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model = MinDalle(is_mega=True, models_root='./pretrained')
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```
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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.
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```python
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text = 'a comfy chair that looks like an avocado'
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image = model.generate_image(text)
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display(image)
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```
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```python
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text = 'court sketch of godzilla on trial'
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image = model.generate_image(text, seed=6, grid_size=3)
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display(image)
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```
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### Command Line
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Use `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='trail cam footage of gollum eating watermelon' --mega --seed=1 --grid-size=3
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```
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