Update app.py

app.py

@@ -1159,17 +1159,28 @@ def img_to_image(image, prompt, neg_prompt, strength, steps, guidance_scale,
                 ui_strength = strength  # Benenne um für Klarheit
     
                 # 1. Basierend auf der UI-Stärke (strength) berechnen
-                adj_strength = ui_strength * 0.8
-                controlnet_strength = 0.5 - (ui_strength * 0.35)
-                pose_ratio = 0.75 - (ui_strength * 0.15)
-                canny_ratio = 0.25 + (ui_strength * 0.15)
-    
+                #adj_strength = ui_strength * 0.8
+                #controlnet_strength = 0.5 - (ui_strength * 0.35)
+                #pose_ratio = 0.75 - (ui_strength * 0.15)
+                #canny_ratio = 0.25 + (ui_strength * 0.15)
+
+                adj_strength = ui_strength
+                controlnet_strength = 0.0
+                pose_ratio = 0.0
+                canny_ratio = 0.0
+
+                conditioning_scale = [0.0, 0.0]
+                
                 # 2. Werte auf sinnvolle Bereiche begrenzen (Clipping)
-                adj_strength = max(0.1, min(adj_strength, 0.8))
-                controlnet_strength = max(0.15, min(controlnet_strength, 0.5))
-                pose_ratio = max(0.5, min(pose_ratio, 0.8))
-                canny_ratio = max(0.2, min(canny_ratio, 0.5))
-                 
+                #adj_strength = max(0.1, min(adj_strength, 0.8))
+                #controlnet_strength = max(0.15, min(controlnet_strength, 0.5))
+                #pose_ratio = max(0.5, min(pose_ratio, 0.8))
+                #canny_ratio = max(0.2, min(canny_ratio, 0.5))
+
+                #conditioning_scale = [
+                #    controlnet_strength * depth_ratio,  # Depth-Gewichtung
+                #    controlnet_strength * canny_ratio   # Canny-Gewichtung
+                #]
     
                 print(f"👤 Humanoid → Humanoid (UI-Stärke: {ui_strength})")
                 print(f"   adj_strength: {adj_strength:.2f}, controlnet: {controlnet_strength:.2f}")
@@ -1183,6 +1194,11 @@ def img_to_image(image, prompt, neg_prompt, strength, steps, guidance_scale,
                 controlnet_strength = 0.5                  
                 pose_ratio = 0.10   # 10% Pose
                 canny_ratio = 0.90  # 90% Canny
+
+                conditioning_scale = [
+                    controlnet_strength * depth_ratio,  # Depth-Gewichtung
+                    controlnet_strength * canny_ratio   # Canny-Gewichtung
+                ]
                 print("📦 Gegenstand → Gegenstand → Ratio 25:75 (Pose:Canny)")
     
             # Anpassung für Mensch → Tier
@@ -1193,11 +1209,11 @@ def img_to_image(image, prompt, neg_prompt, strength, steps, guidance_scale,
                 canny_ratio = 0.5  # 50% Canny
                 print("🐾 Mensch → Tier → Ratio 50:50 (Pose:Canny)")
     
-            # CONDITIONING SCALE BERECHNEN (genau wie environment_change)
-            conditioning_scale = [
-                controlnet_strength * pose_ratio,  # OpenPose
-                controlnet_strength * canny_ratio  # Canny
-            ]
+                # CONDITIONING SCALE BERECHNEN (genau wie environment_change)
+                conditioning_scale = [
+                    controlnet_strength * pose_ratio,  # OpenPose
+                    controlnet_strength * canny_ratio  # Canny
+                ]
     
             print(f"🎯 MODUS: Focus verändern")
             print(f"   Strength: {adj_strength}, ControlNet: {controlnet_strength}")