Update controlnet_module.py

controlnet_module.py

@@ -253,97 +253,34 @@ class ControlNetProcessor:
                     print(f"Fehler beim Laden von Multi-ControlNet Outside: {e}")
                     raise
             return self.pipe_multi_outside
-
-    def generate_with_controlnet(
-        self, image, prompt, negative_prompt,
-        steps, guidance_scale, controlnet_strength,
-        progress=None, keep_environment=False
-    ):
-        """
-        GENERIERT BILD MIT CONTROLNET
-        WICHTIG: Diese Funktion wird von app.py aufgerufen
-        
-        Parameter keep_environment bestimmt:
-        - True: "Umgebung ändern" und "Ausschließlich Gesicht" → Depth+Canny
-        - False: "Focus verändern" → OpenPose+Canny
-        
-        Die eigentliche Maskenlogik wird in app.py (create_face_mask) gehandhabt
-        """
-        try:
-            # --- LOGIK FÜR 3 MODI (VON APP.PY GESTEUERT) ---
-            if keep_environment:
-                # FALL 1 & 3: Umgebung ändern ODER Ausschließlich Gesicht → Depth + Canny
-                print("🎯 ControlNet: Depth + Canny (keep_environment=True)")
-                
-                # Beide Conditioning Maps erstellen
-                depth_image = self.extract_depth_map(image)
-                canny_image = self.extract_canny_edges(image)
-                print("✅ Depth + Canny Maps für Outside/Inside-Box erstellt")
-                
-                # Multi-ControlNet für Outside verwenden
-                conditioning_images = [depth_image, canny_image]
-                controlnet_type = "multi_outside"
-                
-                # Gewichtung: Depth 60%, Canny 40%
-                controlnet_conditioning_scale = [controlnet_strength * 0.6,  # Depth: 60% für räumliche Tiefe
-                                               controlnet_strength * 0.4]   # Canny: 40% für Strukturen
-                
-            else:
-                # FALL 2: Focus verändern → OpenPose + Canny
-                print("🎯 ControlNet: OpenPose + Canny (keep_environment=False)")
-                
-                # Beide Conditioning Maps erstellen
-                pose_image = self.extract_pose(image)
-                canny_image = self.extract_canny_edges(image)
-                print("✅ OpenPose + Canny Maps für Inside-Box erstellt")
-                
-                # Multi-ControlNet für Inside verwenden
-                conditioning_images = [pose_image, canny_image]
-                controlnet_type = "multi_inside"
-                
-                # Gewichtung: OpenPose 70%, Canny 30%
-                controlnet_conditioning_scale = [controlnet_strength * 0.7,  # OpenPose: 70% für Person
-                                               controlnet_strength * 0.3]   # Canny: 30% für Konturen
-
-            # Zufälliger Seed
-            seed = random.randint(0, 2**32 - 1)
-            generator = torch.Generator(device=self.device).manual_seed(seed)
-            print(f"ControlNet Seed: {seed}")
-
-            pipe = self.load_controlnet_pipeline(controlnet_type)
-
-            # Fortschritt-Callback
-            callback = ControlNetProgressCallback(progress, int(steps)) if progress is not None else None
-
-            print("🔄 ControlNet: Starte Pipeline...")
-
-            # ControlNet Generierung
-            result = pipe(
-                prompt=prompt,
-                image=conditioning_images,
-                negative_prompt=negative_prompt,
-                num_inference_steps=int(steps),
-                guidance_scale=guidance_scale,
-                generator=generator,
-                controlnet_conditioning_scale=controlnet_conditioning_scale,
-                height=512,
-                width=512,
-                output_type="pil",
-                callback_on_step_end=callback,
-                callback_on_step_end_tensor_inputs=[],
-            )
-
-            print("✅ ControlNet abgeschlossen!")
             
-            # Rückgabe: ControlNet-Output + Originalbild (für Inpaint)
-            return result.images[0], image
 
-        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
+    def prepare_controlnet_maps(self, image, keep_environment=False):
+    """
+    ERSTELLT NUR CONDITIONING-MAPS, generiert KEIN Bild.
+    """
+    print("🎯 ControlNet: Erstelle Conditioning-Maps...")
+    
+    if keep_environment:
+        # Depth + Canny
+        print("   Modus: Depth + Canny")
+        conditioning_images = [
+            self.extract_depth_map(image),
+            self.extract_canny_edges(image)
+        ]
+    else:
+        # OpenPose + Canny
+        print("   Modus: OpenPose + Canny")
+        conditioning_images = [
+            self.extract_pose(image),
+            self.extract_canny_edges(image)
+        ]
+    
+    print(f"✅ {len(conditioning_images)} Conditioning-Maps erstellt.")
+    return conditioning_images  # Rückgabe: Liste der PIL Images
+
+
+   
 
     def prepare_inpaint_input(self, image, keep_environment=False):
         """