Fix LoRA alpha not applied when the trainer stores it in file metadata - #15830
Fix LoRA alpha not applied when the trainer stores it in file metadata#15830Sravanjangam wants to merge 2 commits into
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SimpleTuner and other PEFT/diffusers trainers write alpha/rank into the safetensors header (e.g. transformer.lora_alpha=64, transformer.r=16) instead of a per-key .alpha tensor. load_lora only looked at the tensor, so these LoRAs ran at scale 1.0 instead of alpha/rank. Fall back to the metadata when a key has no .alpha tensor; explicit tensors still win. Metadata flows through the existing lora_metadata argument of load_lora_for_models.
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Merge Risk: 🟠 High · up to The metadata fallback can apply the wrong LoRA scale when namespaces differ and can propagate invalid values into model scaling, producing incorrect or unusable outputs. These correctness issues should be fixed before merging. 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
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In `@comfy/lora.py`:
- Around line 45-51: Update the metadata handling loop in comfy/lora.py to store
lora_alpha and rank values together by their metadata namespace, rather than in
independent first-value variables. When processing the current x, select the
matching namespace pair so alpha and rank cannot be mixed across text_encoder
and transformer metadata; add a regression test using distinct values for both
namespaces.
- Around line 47-62: Update the metadata parsing in the loop before the alpha
fallback so non-string values and non-finite alpha/rank values are ignored, and
ranks less than or equal to zero are not accepted. Ensure the fallback in the
to_load loop only divides meta_alpha by a valid positive finite meta_rank,
preserving existing behavior for valid metadata. Add regression coverage for NaN
and infinity metadata values.
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Fixes #12191
Problem
Trainers in the PEFT/diffusers family (e.g. SimpleTuner) store
alphaandrankonly in the safetensors header metadata (transformer.lora_alpha=64,transformer.r=16) instead of a per-key.alphatensor.load_loraonly looked at the tensor, so these LoRAs silently ran at scale 1.0 instead of alpha/rank — users compensated with strength 3-4x, which distorts the trained result.Change
comfy/lora.py:load_loragains an optionalmetadataargument; when a key has no.alphatensor, fall back tolora_alpha / rread from that metadata (plain and namespaced keys both handled; malformed values ignored).comfy/sd.py: thread the already-availablelora_metadatathroughload_lora_for_models..alphatensors still win over metadata; behavior without metadata is unchanged.Tests
New
tests/test_lora_alpha_metadata.py(5 tests): namespaced fallback, plain-key fallback, explicit-tensor precedence, no-metadata unchanged, malformed-metadata ignored.Environment: Python 3.12 / torch 2.11 CPU, current master (
82f839f5).Review updates (ee6bfd2)
Both findings addressed: