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Fix hardcoded input dim in DiffusionModelEncoder #8514

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@IamTingTing IamTingTing commented Jul 18, 2025

Fixes #8496

Description

A few sentences describing the changes proposed in this pull request.

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.

Summary by CodeRabbit

  • New Features
    • Improved model flexibility by dynamically adapting the output layer to the input feature size during the first use, allowing for broader compatibility with different input shapes.

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coderabbitai bot commented Jul 18, 2025

Walkthrough

The initialization of the output layer in the DiffusionModelEncoder class was changed from a fixed input size to a lazy, runtime-determined configuration. Now, the output module is constructed during the first forward pass, adapting its input dimension to the actual flattened feature size, enhancing flexibility for different encoder architectures.

Changes

File(s) Change Summary
monai/networks/nets/diffusion_model_unet.py Refactored DiffusionModelEncoder: self.out is now lazily initialized in forward() based on the actual feature shape, replacing the hardcoded 4096 input size.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant DiffusionModelEncoder
    participant nn.Sequential

    User->>DiffusionModelEncoder: forward(input)
    DiffusionModelEncoder->>DiffusionModelEncoder: Flatten features (h)
    alt self.out not initialized
        DiffusionModelEncoder->>nn.Sequential: Create with input size = h.shape[1]
        DiffusionModelEncoder->>DiffusionModelEncoder: Assign to self.out
    end
    DiffusionModelEncoder->>nn.Sequential: Pass h through self.out
    nn.Sequential-->>DiffusionModelEncoder: Output tensor
    DiffusionModelEncoder-->>User: Return output
Loading

Poem

A hop and a skip, the encoder’s set free,
No more hardcoded shapes for me!
With lazy layers built on the fly,
Flexibility hops ever so high.
Now every feature can fit just right—
The model’s future is fluffy and bright!
🐇✨


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Actionable comments posted: 0

🧹 Nitpick comments (1)
monai/networks/nets/diffusion_model_unet.py (1)

2053-2056: Consider device placement and thread safety for lazy initialization.

The lazy initialization logic is correct, but consider these improvements:

  1. Device placement: The dynamically created module should be moved to the same device as the input tensor.
  2. Thread safety: In multi-threaded environments, this could create race conditions.

Apply this diff to ensure proper device placement:

-        if self.out is None:
-            self.out = nn.Sequential(
-                nn.Linear(h.shape[1], 512), nn.ReLU(), nn.Dropout(0.1), nn.Linear(512, self.out_channels)
-            )
+        if self.out is None:
+            self.out = nn.Sequential(
+                nn.Linear(h.shape[1], 512), nn.ReLU(), nn.Dropout(0.1), nn.Linear(512, self.out_channels)
+            ).to(h.device)

For thread safety, consider using a lock or moving the initialization to a separate method called during model setup.

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🔇 Additional comments (1)
monai/networks/nets/diffusion_model_unet.py (1)

2008-2008: LGTM: Proper lazy initialization setup.

Setting self.out = None correctly defers the output layer creation until runtime, enabling dynamic input dimension adaptation.

@KumoLiu KumoLiu requested a review from ericspod July 18, 2025 14:26
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Hardcoded 4096 dimension in DiffusionModelEncoder prevents architectural flexibility
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