🐛 fix cosine noise scheduler

[slavaheroes]

Fixes: Update cosine noise scheduling

Description

In the current DDPMScheduler implementation, using the cosine noise schedule results in a division-by-zero issue during sampling. Specifically, scheduler.alphas_cumprod[0] == 1.0, which causes NaN values in the output image.

You can reproduce the issue with the following snippet:

from monai.inferers import DiffusionInferer
from monai.networks.nets import DiffusionModelUNet
from monai.networks.schedulers import DDPMScheduler

import torch 
N = 250

device = 'cuda'

model = DiffusionModelUNet(
    spatial_dims=3,
    in_channels=1,
    out_channels=1,
    channels=[64, 64, 128],
    attention_levels=[False, False, True],
    num_head_channels=[0, 0, 128],
    num_res_blocks=2,
).to(device)

scheduler = DDPMScheduler(num_train_timesteps=N, schedule="cosine").to(device)
print(scheduler.alphas_cumprod[0])

inferer = DiffusionInferer(scheduler)

scheduler.set_timesteps(num_inference_steps=N)

noise = torch.randn((1, 1, 32, 40, 32))
noise = noise.to(device)
image = inferer.sample(input_noise=noise, diffusion_model=model, scheduler=scheduler)

assert torch.isfinite(image).any(), "Image has NaN values"

Fix

This PR applies clipping to the beta values first and re-computes alphas_cumprod accordingly before returning in monai/networks/schedulers/scheduler.py: 112. This ensures numerical stability during sampling and prevents NaNs in the output.

Types of changes

• Non-breaking change (fix or new feature that would not break existing functionality).
• Breaking change (fix or new feature that would cause existing functionality to change).
• New tests added to cover the changes.
• Integration tests passed locally by running ./runtests.sh -f -u --net --coverage.
• Quick tests passed locally by running ./runtests.sh --quick --unittests --disttests.
• In-line docstrings updated.
• Documentation updated, tested make html command in the docs/ folder.

[ericspod]

@virginiafdez could you please check this? Thanks!

[virginiafdez]

The new implementation is more inline with the paper (https://arxiv.org/abs/2102.09672). I've tried with several number of time steps, all seems to be in order.

[ericspod]

Thanks @slavaheroes @virginiafdez we'll put these changes through then. @KumoLiu please trigger blossom, we should rerun the windows test since it shouldn't have failed given these changes.

[KumoLiu]

/build

[KumoLiu]

/build