#
Reproducibility

Diffusion is a random process that generates a different output every time. For certain situations like testing and replicating results, you want to generate the same result each time, across releases and platforms within a certain tolerance range.

This guide will show you how to control sources of randomness and enable deterministic algorithms.

##
Generator

Pipelines rely on torch.randn, which uses a different random seed each time, to create the initial noisy tensors. To generate the same output on a CPU or GPU, use a Generator to manage how random values are generated.

> If reproducibility is important to your use case, we recommend always using a CPU `Generator`. The performance loss is often negligible and you’ll generate more similar values.

GPU
CPU

The GPU uses a different random number generator than the CPU. Diffusers solves this issue with the randn_tensor() function to create the random tensor on a CPU and then moving it to the GPU. This function is used everywhere inside the pipeline and you don’t need to explicitly call it.

Use manual_seed as shown below to set a seed.

    import torch
    import numpy as np
    from diffusers import DDIMPipeline

    ddim = DDIMPipeline.from_pretrained("google/ddpm-cifar10-32", device_map="cuda")
    generator = torch.manual_seed(0)
    image = ddim(num_inference_steps=2, output_type="np", generator=generator).images
    print(np.abs(image).sum())

The `Generator` object should be passed to the pipeline instead of an integer seed. `Generator` maintains a random state that is consumed and modified when used. Once consumed, the same `Generator` object produces different results in subsequent calls, even across different pipelines, because it’s state has changed.

    generator = torch.manual_seed(0)

    for _ in range(5):
    -    image = pipeline(prompt, generator=generator)
    +    image = pipeline(prompt, generator=torch.manual_seed(0))

##
Deterministic algorithms

PyTorch supports deterministic algorithms - where available - for certain operations so they produce the same results. Deterministic algorithms may be slower and decrease performance.

Use Diffusers’ enable_full_determinism function to enable deterministic algorithms.

    import torch
    from diffusers_utils import enable_full_determinism

    enable_full_determinism()

Under the hood, `enable_full_determinism` works by:

- Setting the environment variable CUBLAS_WORKSPACE_CONFIG to `:16:8` to only use one buffer size during rntime. Non-deterministic behavior occurs when operations are used in more than one CUDA stream.
- Disabling benchmarking to find the fastest convolution operation by setting `torch.backends.cudnn.benchmark=False`. Non-deterministic behavior occurs because the benchmark may select different algorithms each time depending on hardware or benchmarking noise.
- Disabling TensorFloat32 (TF32) operations in favor of more precise and consistent full-precision operations.

##
Resources

We strongly recommend reading PyTorch’s developer notes about Reproducibility. You can try to limit randomness, but it is not guaranteed even with an identical seed.

Update on GitHub