ibility; notice you don't place it on the GPU!
generator = torch.manual_seed(0)

# run pipeline for just two steps and return numpy tensor
image = ddim(num_inference_steps=2, output_type="np", generator=generator).images
print(np.abs(image).sum())
💡 If reproducibility is important, we recommend always passing a CPU generator.
The performance loss is often neglectable, and you’ll generate much more similar
values than if the pipeline had been ru