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mirror of https://github.com/vladmandic/automatic synced 2026-09-19 01:04:32 +02:00
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fix(lora-extract): stop overwriting the module selection list; fix dead no-LoRA guard

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make_lora reassigned the 'modules' selection arg to a named_modules() generator, so the subsequent 'te'/'unet' in modules checks tested an exhausted generator and silently skipped TE2 + UNet extraction. Also 'loaded_lora() == ""' never matched a loaded model (returns a list), so the no-LoRA-detected guard never fired.

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
QualiaRain
2026-06-13 00:17:17 -04:00
parent d227a46406
commit ca730fa3e6
1 changed files with 4 additions and 7 deletions
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modules/lora/lora_extract.py
+4 -7
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@@ -122,7 +122,7 @@ def make_lora(fn, maxrank, auto_rank, rank_ratio, modules, overwrite):
log.warning(msg)
yield msg
return
if loaded_lora() == "":
if not loaded_lora():
msg = "LoRA extract: no LoRA detected"
log.warning(msg)
yield msg
@@ -141,8 +141,7 @@ def make_lora(fn, maxrank, auto_rank, rank_ratio, modules, overwrite):
with rp.Progress(rp.TextColumn('[cyan]LoRA extract'), rp.BarColumn(), rp.TaskProgressColumn(), rp.TimeRemainingColumn(), rp.TimeElapsedColumn(), rp.TextColumn('[cyan]{task.description}'), console=console) as progress:
if 'te' in modules and getattr(shared.sd_model, 'text_encoder', None) is not None:
modules = shared.sd_model.text_encoder.named_modules()
task = progress.add_task(description="te1 decompose", total=len(list(modules)))
task = progress.add_task(description="te1 decompose", total=len(list(shared.sd_model.text_encoder.named_modules())))
for name, module in shared.sd_model.text_encoder.named_modules():
progress.update(task, advance=1)
weights_backup = getattr(module, "network_weights_backup", None)
@@ -157,8 +156,7 @@ def make_lora(fn, maxrank, auto_rank, rank_ratio, modules, overwrite):
t1 = time.time()
if 'te' in modules and getattr(shared.sd_model, 'text_encoder_2', None) is not None:
modules = shared.sd_model.text_encoder_2.named_modules()
task = progress.add_task(description="te2 decompose", total=len(list(modules)))
task = progress.add_task(description="te2 decompose", total=len(list(shared.sd_model.text_encoder_2.named_modules())))
for name, module in shared.sd_model.text_encoder_2.named_modules():
progress.update(task, advance=1)
weights_backup = getattr(module, "network_weights_backup", None)
@@ -172,8 +170,7 @@ def make_lora(fn, maxrank, auto_rank, rank_ratio, modules, overwrite):
t2 = time.time()
if 'unet' in modules and getattr(shared.sd_model, 'unet', None) is not None:
modules = shared.sd_model.unet.named_modules()
task = progress.add_task(description="unet decompose", total=len(list(modules)))
task = progress.add_task(description="unet decompose", total=len(list(shared.sd_model.unet.named_modules())))
for name, module in shared.sd_model.unet.named_modules():
progress.update(task, advance=1)
weights_backup = getattr(module, "network_weights_backup", None)