Update controlnet_module.py

controlnet_module.py

@@ -143,67 +143,75 @@ class ControlNetProcessor:
             return self.pipe_canny
 
     def generate_with_controlnet(
-    self, image, prompt, negative_prompt,
-    steps, guidance_scale, controlnet_strength,
-    progress=None, keep_environment=False
-):
-    """Generiert Bild mit ControlNet und Fortschrittsanzeige"""
-    try:
-        # --- KORREKTE LOGIK ---
-        if keep_environment:
-            # UMGEBUNG BEIBEHALTEN, PERSON ÄNDERN
-            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)
-
-        # Zufälliger Seed
-        seed = random.randint(0, 2**32 - 1)
-        generator = torch.Generator(device=self.device).manual_seed(seed)
-        print(f"ControlNet Seed: {seed}")
-
-        # 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_image,
-            negative_prompt=negative_prompt,
-            num_inference_steps=int(steps),
-            guidance_scale=guidance_scale,
-            generator=generator,
-            controlnet_conditioning_scale=controlnet_strength,
-            height=512,
-            width=512,
-            output_type="pil",
-            callback_on_step_end=callback,
-            callback_on_step_end_tensor_inputs=[],
-        )
-
-        print("✅ ControlNet abgeschlossen!")
-        
-        # DREI Werte zurückgeben
-        return result.images[0], conditioning_image, inpaint_input
-
-    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        
-
-   
+        self, image, prompt, negative_prompt,
+        steps, guidance_scale, controlnet_strength,
+        progress=None, keep_environment=False
+    ):
+        """Generiert Bild mit ControlNet und Fortschrittsanzeige"""
+        try:
+            # --- KORREKTE LOGIK ---
+            if keep_environment:
+                # UMGEBUNG BEIBEHALTEN, PERSON ÄNDERN
+                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)
+
+            # Zufälliger Seed
+            seed = random.randint(0, 2**32 - 1)
+            generator = torch.Generator(device=self.device).manual_seed(seed)
+            print(f"ControlNet Seed: {seed}")
+
+            # 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_image,
+                negative_prompt=negative_prompt,
+                num_inference_steps=int(steps),
+                guidance_scale=guidance_scale,
+                generator=generator,
+                controlnet_conditioning_scale=controlnet_strength,
+                height=512,
+                width=512,
+                output_type="pil",
+                callback_on_step_end=callback,
+                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
+
+        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
+
     def prepare_inpaint_input(self, image, keep_environment=False):
         """
         Bereitet das Input-Bild für Inpaint vor