Fix LoRA set_adapters when transformer compiled

app.py

@@ -273,10 +273,37 @@ def attach_lora(pipeline: ZImagePipeline) -> Tuple[bool, str | None]:
 def set_lora_scale(pipeline: ZImagePipeline, scale: float) -> None:
     weight = max(float(scale), 0.0)
     adapter = lora_adapter_name or "default"
+    compiled_transformer = None
     try:
-        pipeline.set_adapters([adapter], adapter_weights=[weight])
-    except TypeError:
-        pipeline.set_adapters([adapter], weights=[weight])
+        transformer = getattr(pipeline, "transformer", None)
+        if transformer is not None and hasattr(transformer, "_orig_mod"):
+            compiled_transformer = transformer
+            pipeline.transformer = transformer._orig_mod
+
+        try:
+            pipeline.set_adapters([adapter], adapter_weights=[weight])
+        except TypeError:
+            pipeline.set_adapters([adapter], weights=[weight])
+        except ValueError as exc:
+            msg = str(exc)
+            present_match = re.search(r"present adapters:\s*(\{[^}]*\}|set\([^)]*\))", msg)
+            if present_match:
+                present_names = re.findall(r"'([^']+)'", present_match.group(1))
+            else:
+                present_names = []
+            if present_names:
+                global lora_adapter_name
+                lora_adapter_name = present_names[0]
+                adapter = lora_adapter_name
+                try:
+                    pipeline.set_adapters([adapter], adapter_weights=[weight])
+                except TypeError:
+                    pipeline.set_adapters([adapter], weights=[weight])
+            else:
+                raise
+    finally:
+        if compiled_transformer is not None:
+            pipeline.transformer = compiled_transformer
 
 
 def load_models() -> Tuple[ZImagePipeline, bool, str | None]:
@@ -498,10 +525,13 @@ def generate_image(
     )
 
     if lora_loaded:
-        if use_lora:
-            set_lora_scale(pipeline, float(lora_scale))
-        else:
-            set_lora_scale(pipeline, 0.0)
+        try:
+            if use_lora:
+                set_lora_scale(pipeline, float(lora_scale))
+            else:
+                set_lora_scale(pipeline, 0.0)
+        except Exception as exc:  # noqa: BLE001
+            print(f"LoRA scale update failed (continuing without changing LoRA state): {exc}")
 
     with torch.inference_mode():
         image = pipeline(