Create reproducible pipelines            Reproducibility is important for testing, replicating results, and can even be used to improve image quality. However, the randomness in diffusion models is a desired property because it allows the pipeline to generate different images every time it is run. While you can’t expect to get the exact same results across platforms, you can expect results to be reproducible across releases and platforms within a certain tolerance range. Even then, tolerance varies depending on the diffusion pipeline and checkpoint. This is why it’s important to understand how to control sources of randomness in diffusion models or use deterministic algorithms. 💡 We strongly recommend reading PyTorch’s statement about reproducibility: Completely reproducible results are not guaranteed across PyTorch releases, individual commits, or different platforms. Furthermore, results may not be reproducible between CPU and GPU executions, even when using identical seeds.  Control randomness During inference, pipelines rely heavily on random sampling operations which include creating the