Refactor TopoDiff recipe to use diffusion framework

[CharlelieLrt]

PhysicsNeMo Pull Request

Description

Checklist

• I am familiar with the Contributing Guidelines.
• New or existing tests cover these changes.
• The documentation is up to date with these changes.
• The CHANGELOG.md is up to date with these changes.
• An issue is linked to this pull request.
• If I am implementing a new model or modifying any existing model, I have followed the Models Implementation Coding Standards.

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[greptile-apps]

Greptile Summary

This PR refactors the TopoDiff example scripts to use the physicsnemo.diffusion framework, replacing the custom Diffusion class with DDPMLinearNoiseScheduler, MSEDSMLoss, DPSScorePredictor, and the framework sample() loop. New adapters (DDPMSolver, ClassifierGuidance) and a component test file are added in utils.py and test_components.py.

There are two P1 bugs affecting training correctness:

• Double normalization in train_classifier.py and train_regressor.py: both scripts normalize pixel values to [-1, 1] before the training loop and then apply the same * 2 - 1 transform again inside the loop, producing a [-3, 1] input range throughout training.

Important Files Changed

Filename	Overview
examples/generative/topodiff/inference.py	Replaces hand-rolled DDPM loop with DPSScorePredictor + DDPMSolver + framework sample(); gradient flow preserved via torch.inference_mode(False); plotting improved to handle dynamic batch sizes.
examples/generative/topodiff/utils.py	Adds DDPMLinearNoiseScheduler, DDPMSolver, and ClassifierGuidance adapters wrapping the new diffusion framework; DDPMSolver stochastic noise check uses .sum() which is fragile, and timesteps() has a div-by-zero edge case when num_steps=1.
examples/generative/topodiff/train.py	Replaces old Diffusion class with MSEDSMLoss + DDPMLinearNoiseScheduler; license header has a stray "cd .." appended.
examples/generative/topodiff/train_classifier.py	Replaces manual noise injection with noise_scheduler.add_noise(); contains a double-normalization bug: images are mapped [0,1]→[-1,1] at load time, then the same transform is applied again inside the training loop, producing a [-3,1] range.
examples/generative/topodiff/train_regressor.py	Same double-normalization as train_classifier.py: topologies are scaled [0,1]→[-1,1] before the loop, then scaled again inside the loop, yielding an incorrect [-3,1] range for training.
examples/generative/topodiff/test_components.py	New test file that validates scheduler, noise, epsilon round-trips, DDPMSolver, MSEDSMLoss, and sample() against the original Diffusion class; well-structured and covers the key refactored components.

Comments Outside Diff (3)

1. examples/generative/topodiff/train_classifier.py, line 79-81 (link)

   Same double normalization in validation branch

   The validation batch has the same issue — valid_img was already mapped to [-1, 1] on line 40, so the * 2 - 1 here produces [-3, 1]:

2. examples/generative/topodiff/train_regressor.py, line 77-79 (link)

   Double normalization — topologies scaled to [-3, 1] instead of [-1, 1]

   Line 40 already normalizes topologies to [-1, 1] with topologies = topologies * 2 - 1. Applying * 2 - 1 again in the training loop maps the range to [-3, 1], feeding the regressor incorrectly scaled inputs throughout training:

3. examples/generative/topodiff/train.py, line 15 (link)

   Stray shell command in license header

   cd .. was accidentally appended to the closing license comment line:

_{Reviews (1): Last reviewed commit: "Refactor TopoDiff recipe to use diffusio..." | Re-trigger Greptile}

[coreyjadams]

approved for pyproject.toml

[mnabian]

I think the changes look good overall. I could not spot any major issues. It would be good to track the known bugs in the code in an issue. Hopefully at some point in the future we can revisit this example and extend it to 3D cases.

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[CharlelieLrt]

/blossom-ci

[CharlelieLrt]

/blossom-ci