Fix fp16 intermediates giving different results.

[comfyanonymous]

No description provided.

[coderabbitai]

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📝 Walkthrough

Walkthrough

The prepare_noise_inner function in comfy/sample.py has been modified to standardize noise generation. The function now always creates random noise initially on the CPU using torch.float32 precision, then converts the resulting tensor(s) to match the latent_image.dtype. This unified approach applies regardless of whether noise_inds is None (generating a single noise tensor) or provided (generating per-index noise tensors before concatenation).

🚥 Pre-merge checks | ✅ 1 | ❌ 2

❌ Failed checks (1 warning, 1 inconclusive)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.
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 what issue is being fixed and why the change to always generate noise on CPU with float32 resolves the fp16 problem.

✅ Passed checks (1 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title directly references the specific issue being fixed: fp16 intermediates giving different results, which aligns with the code change that ensures consistent noise generation by using float32 initially.

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