41 lines
2.0 KiB
Markdown
Vendored
41 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). It has been stripped to the bare essentials necessary for doing inference, and converted to PyTorch. To run the torch model, 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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### Setup
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Run either `sh setup_torch.sh` or `sh setup_flax.sh` to install dependencies and download pretrained models. The torch models can be manually downloaded [here](https://huggingface.co/kuprel/min-dalle/tree/main).
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The flax models can be manually downloaded here:
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[VQGan](https://huggingface.co/dalle-mini/vqgan_imagenet_f16_16384),
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[DALL·E Mini](https://wandb.ai/dalle-mini/dalle-mini/artifacts/DalleBart_model/mini-1/v0/files),
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[DALL·E Mega](https://wandb.ai/dalle-mini/dalle-mini/artifacts/DalleBart_model/mega-1-fp16/v14/files)
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### Usage
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Use the python script `image_from_text.py` to generate images from the command line. Note: the command line script loads the models and parameters each time. To load a model once and generate multiple times, initialize either `MinDalleTorch` or `MinDalleFlax`, then call `generate_image` with some text and a seed. See the colab for an example.
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### Examples
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```
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python image_from_text.py --text='artificial intelligence' --seed=7
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```
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```
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python image_from_text.py --text='a comfy chair that looks like an avocado' --mega --seed=10
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```
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```
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python image_from_text.py --text='court sketch of godzilla on trial' --mega --seed=40
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```
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