Speed up ernie model by a bit on nvidia and use higher quality rope.

[comfyanonymous]

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[coderabbitai]

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📥 Commits

Reviewing files that changed from the base of the PR and between 08e93a3 and c7bc94d.

📒 Files selected for processing (2)

• comfy/ldm/cosmos/predict2.py
• comfy/ldm/ernie/model.py

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📝 Walkthrough

Walkthrough

This PR migrates rotary embedding computation and application from local implementations to the shared comfy.quant_ops library. The Ernie and Cosmos models now import comfy.quant_ops and use its apply_rope_split_half function. The local apply_rotary_emb helper is removed, and ErnieImageEmbedND3 is refactored to build rotary embeddings with a new tensor structure (stacked and reshaped to include explicit [..., 2, 2] dimensions). Type casting behavior is simplified by removing the .to(x.dtype) conversion in ErnieImageModel.forward().

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 inconclusive)

Check name	Status	Explanation	Resolution
Description check	❓ Inconclusive	No pull request description was provided by the author, making it impossible to assess relevance to the changeset.	Add a description explaining the performance improvements, rope quality enhancements, and any testing or benchmark results to provide context for reviewers.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title accurately describes the main changes: optimizing the ernie model performance on NVIDIA GPUs and improving the rope (rotary positional embedding) implementation quality.
Docstring Coverage	✅ Passed	No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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[Ph0rk0z]

Rope stuff broke nunchaku since it's looking for the old rope. At least the version I was using.