nPipeline downloads and caches all modeling, tokenization, and scheduling components.
Because the model consists of roughly 1.4 billion parameters, we strongly recommend running it on a GPU.
You can move the generator object to a GPU, just like you would in PyTorch:


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>>> generator.to("cuda")
Now you can use the generator to generate an image:


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>>> image = generator().images[0]
The output is by default wrapped into a PIL.Image 