Update app.py

app.py

@@ -197,7 +197,6 @@ def text_to_image(prompt, steps, guidance_scale, progress=gr.Progress()):
         traceback.print_exc()
         return None
 
-
 def img_to_image(image, prompt, neg_prompt, strength, steps, guidance_scale, face_preserve, bbox_x1, bbox_y1, bbox_x2, bbox_y2, progress=gr.Progress()):
     try:
         if image is None:
@@ -222,12 +221,17 @@ def img_to_image(image, prompt, neg_prompt, strength, steps, guidance_scale, fac
         
         print(f"🎯 ControlNet Step-Kalkulation: UI={steps}, Adj-Strength={adj_strength:.3f}, Echte Steps={actual_steps_from_strength}, ControlNet-Steps={controlnet_steps}")
 
-        # CONTROLNET-STRENGTH BERECHNEN: 50% der Inpaint-Strength
-        controlnet_strength = adj_strength * 0.5
-        print(f"🎯 ControlNet Strength: {controlnet_strength:.3f} (50% von Inpaint-Strength {adj_strength:.3f})")
+        # --- KONTROLLNET-STRENGTH ANPASSEN ABHÄNGIG VOM MODUS ---
+        if face_preserve:
+            # UMGEBUNG BEIBEHALTEN, PERSON ÄNDERN → HOHE STRENGTH FÜR CANNY
+            controlnet_strength = adj_strength * 0.8  # 80% für Canny
+            print(f"🎯 ControlNet Canny Modus: Strength = {controlnet_strength:.3f} (80% von {adj_strength:.3f})")
+        else:
+            # PERSON BEIBEHALTEN, UMGEBUNG ÄNDERN → NORMALE STRENGTH FÜR OPENPOSE
+            controlnet_strength = adj_strength * 0.5  # 50% für OpenPose
+            print(f"🎯 ControlNet OpenPose Modus: Strength = {controlnet_strength:.3f} (50% von {adj_strength:.3f})")
 
         # ControlNet Modul aufrufen - MIT ZWEI RÜCKGABEWERTEN
-        # ControlNet Modul aufrufen - MIT DREI RÜCKGABEWERTEN
         controlnet_output, inpaint_input = controlnet_processor.generate_with_controlnet(
             image=image,
             prompt=prompt,
@@ -248,7 +252,7 @@ def img_to_image(image, prompt, neg_prompt, strength, steps, guidance_scale, fac
         # --- INPAINT PIPELINE ---
         pipe = load_img2img()
         
-        # JETZT: inpaint_input verwenden (Conditioning Image für Inpaint)
+        # inpaint_input verwenden (IMMER Originalbild)
         img_resized = inpaint_input.convert("RGB").resize((IMG_SIZE, IMG_SIZE))
 
         # --- PARAMETER-TUNING ---
@@ -302,7 +306,7 @@ def img_to_image(image, prompt, neg_prompt, strength, steps, guidance_scale, fac
         result = pipe(
             prompt=prompt,
             negative_prompt=neg_prompt,
-            image=img_resized,  # JETZT: inpaint_input (Conditioning Image)
+            image=img_resized,  # inpaint_input (Originalbild)
             mask_image=mask,
             strength=adj_strength,
             num_inference_steps=int(steps),
@@ -339,7 +343,6 @@ def img_to_image(image, prompt, neg_prompt, strength, steps, guidance_scale, fac
         return None
 
 
-
 def update_bbox_from_image(image):
     """Aktualisiert die Bounding-Box-Koordinaten wenn ein Bild hochgeladen wird"""
     if image is None: