Implements Predictor specialization for multi-diffusion

[CharlelieLrt]

PhysicsNeMo Pull Request

Description

Adds MultiDiffusionPredictor, the sampling-time counterpart of MultiDiffusionMSEDSMLoss. It wraps a trained MultiDiffusionModel2D and satisfies the Predictor protocol, so it plugs straight into sample() / get_denoiser() / standard solvers with no other changes. Condition and positional embedding are pre-patched once at construction; per-step calls only patch x and t. New unit, end-to-end, and compile tests live under test/diffusion/test_multi_diffusion_{predictor,sampling}.py. Drive-by fixes: .contiguous() on t_cur / t_next in EulerSolver and EDMStochasticEulerSolver (parity with HeunSolver), .contiguous() on the broadcast pos_embd before patching, and F.pad in place of nn.ReflectionPad2d(...)(input) in image_batching.

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.

Dependencies

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[greptile-apps]

Greptile Summary

This PR introduces MultiDiffusionPredictor, a Predictor-protocol-compatible wrapper for test-time patch-based diffusion sampling using MultiDiffusionModel2D. It pre-patches the condition and positional embedding once at construction time to avoid redundant work per step, refactors the duplicate PE injection logic into a shared _inject_patched_pos_embd helper, and generalises the DDP/compile-unwrapping helper into a reusable _unwrap_module utility. A thorough test suite covering constructor validation, non-regression, gradient flow, torch.compile, and checkpoint round-trips is included.

• P1 (predictor.py line 406): _skip_positional_embedding_injection = True is permanently written to the underlying MultiDiffusionModel2D and never restored. If the same model object is used for loss computation after the predictor is created (e.g., continued fine-tuning), PE injection is silently skipped, producing wrong training outputs without any error.

Important Files Changed

Filename	Overview
physicsnemo/diffusion/multi_diffusion/predictor.py	New file implementing MultiDiffusionPredictor — a Predictor-protocol wrapper for patch-based sampling. Correctly pre-patches condition and PE at construction, but permanently mutates the underlying model's _skip_positional_embedding_injection flag with no restore mechanism (P1), and the dead is_compiling() guard in init is misleading.
physicsnemo/diffusion/multi_diffusion/models.py	Adds _skip_positional_embedding_injection flag and refactors PE injection into a shared _inject_patched_pos_embd helper. The refactoring is clean and the logic is preserved correctly.
physicsnemo/diffusion/utils/utils.py	Extracts the DDP/torch.compile unwrapping logic into a reusable generic _unwrap_module function. Clean generalization with correct TypeVar usage.
physicsnemo/diffusion/multi_diffusion/losses.py	Replaces the local _unwrap_multi_diffusion helper with the new generic _unwrap_module utility. Equivalent behavior, no logic changes.
physicsnemo/diffusion/guidance/dps_guidance.py	One-line change: DPSScorePredictor now explicitly inherits from Predictor, correctly aligning it with the protocol.
physicsnemo/diffusion/multi_diffusion/init.py	Adds MultiDiffusionPredictor to the public module exports.
test/diffusion/test_multi_diffusion_predictor.py	Comprehensive test suite for MultiDiffusionPredictor covering constructor validation, non-regression outputs, gradient flow, torch.compile compatibility, and checkpoint round-trips.
test/diffusion/test_multi_diffusion_sampling.py	End-to-end sampling tests for MultiDiffusionPredictor integrated with all three noise schedulers (EDM, VE, VP), two solvers, and compiled predictor paths.

Comments Outside Diff (1)

1. physicsnemo/diffusion/multi_diffusion/predictor.py, line 405-406 (link)

   Persistent flag side-effect on underlying model

   _skip_positional_embedding_injection = True is written to _md_model and never restored. If the same MultiDiffusionModel2D is used for loss computation after the predictor is created — for example, after sampling a checkpoint to validate a mid-training result or during continued fine-tuning — the model's forward will silently skip PE injection in both the "no patching" and "with patching" paths, producing wrong outputs from the loss without any error or warning.

   A minimal safeguard would be to save and restore the original flag, or at minimum include a runtime warning that the model is permanently modified:

   # Save original value so callers can restore it
   self._prev_skip_pe = self._md_model._skip_positional_embedding_injection
   self._md_model._skip_positional_embedding_injection = True

   Or add a close() / __del__ to restore the flag, or document that the model must not be used for training after predictor construction.

_{Reviews (1): Last reviewed commit: "Implements Predictor specialization for ..." | Re-trigger Greptile}

[CharlelieLrt]

/blossom-ci

[pzharrington]

Approving with minor comments. I'll note the test coverage is good, but this is adding another fairly large set of data files to the repo. We probably eventually need a better solution then keeping everything here, but that's a problem for another day.

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[CharlelieLrt]

/ok to test 0874c90

[CharlelieLrt]

/blossom-ci