Fix flaky FLUX LoRA unit test (#6899)

## Summary

This PR attempts to fix a flaky FLUX LoRA unit test.
Example test failure:
https://github.com/invoke-ai/InvokeAI/actions/runs/10958325913/job/30428299328?pr=6898

The failure _seems_ to be caused by a numerical precision error, but I
haven't been able to reproduce it locally. I have reduced the tolerance
of the offending comparison, and am pretty confident that this will
solve the issue.

## QA Instructions

No QA necessary.

## Checklist

- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_

tests/backend/lora/test_lora_patcher.py

@@ -192,4 +192,6 @@ def test_apply_lora_sidecar_patches_matches_apply_lora_patches(num_layers: int):
     with LoRAPatcher.apply_lora_sidecar_patches(model=model, patches=lora_models, prefix="", dtype=dtype):
         output_lora_sidecar_patches = model(input)
 
-    assert torch.allclose(output_lora_patches, output_lora_sidecar_patches)
+    # Note: We set atol=1e-5 because the test failed occasionally with the default atol=1e-8. Slight numerical
+    # differences are tolerable and expected due to the difference between sidecar vs. patching.
+    assert torch.allclose(output_lora_patches, output_lora_sidecar_patches, atol=1e-5)