38 lines
1.8 KiB
Markdown
Vendored
38 lines
1.8 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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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 (and optionally wandb to download the models).
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DALL·E Mega inference with PyTorch takes 7.3 seconds in Colab to generate an avocado armchair
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### Setup
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Run `sh setup.sh` to install dependencies and download pretrained models. The models can also be downloaded manually 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' --torch
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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' --torch --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=100
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```
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