Update controlnet_module.py

controlnet_module.py

@@ -155,13 +155,11 @@ class ControlNetProcessor:
                 controlnet_type = "canny"  # ✅ Canny behält Umgebung
                 print("🎯 ControlNet Modus: Umgebung beibehalten (Canny Edge)")
                 conditioning_image = self.extract_canny_edges(image)
-                inpaint_input = image  # ✅ ORIGINALBILD für Inpaint!
             else:
                 # PERSON BEIBEHALTEN, UMGEBUNG ÄNDERN  
                 controlnet_type = "openpose"  # ✅ OpenPose behält Person
                 print("🎯 ControlNet Modus: Person beibehalten (OpenPose)")
                 conditioning_image = self.extract_pose(image)
-                inpaint_input = conditioning_image  # ✅ POSE-MAP für Inpaint
 
             pipe = self.load_controlnet_pipeline(controlnet_type)
 
@@ -191,26 +189,17 @@ class ControlNetProcessor:
                 callback_on_step_end_tensor_inputs=[],
             )
 
-            # Debug-Ausgabe Scheduler Steps
-            try:
-                scheduler = pipe.scheduler
-                if hasattr(scheduler, "timesteps"):
-                    actual_steps = len(scheduler.timesteps)
-                    print(f"🎯 CONTROLNET TATSÄCHLICHE STEPS: {actual_steps} (von {steps} angefordert)")
-            except Exception as e:
-                print(f"⚠️ Konnte ControlNet Scheduler-Info nicht auslesen: {e}")
-
             print("✅ ControlNet abgeschlossen!")
             
-            # DREI Werte zurückgeben
-            return result.images[0], conditioning_image, inpaint_input
+            # ZWEI Werte zurückgeben: ControlNet-Output + ORIGINALBILD für Inpaint
+            return result.images[0], image  # ← IMMER Originalbild für Inpaint!
 
         except Exception as e:
             print(f"❌ Fehler in ControlNet: {e}")
             import traceback
             traceback.print_exc()
             error_image = image.convert("RGB").resize((512, 512))
-            return error_image, error_image, error_image
+            return error_image, error_image
 
     def prepare_inpaint_input(self, image, keep_environment=False):
         """