Update controlnet_facefix.py

controlnet_facefix.py

@@ -5,14 +5,20 @@ from PIL import Image
 
 print("Lade OpenPose_faceonly + Depth für perfekte Gesichter...")
 
-# Preprocessors (einmalig laden)
-openpose_face = OpenposeDetector.from_pretrained("lllyasviel/ControlNet", model_name="openpose_face")
+# Preprocessors (einmalig laden) - KORRIGIERTE VERSION
+try:
+    # Moderne Version
+    openpose_face = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
+except:
+    # Alternative/ältere Version
+    from controlnet_aux.open_pose import OpenposeDetector
+    openpose_face = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
+
 depth_processor = ZoeDetector.from_pretrained("lllyasviel/ControlNet")
 
 # ControlNet Modelle (bleiben im VRAM)
 controlnet_face = ControlNetModel.from_pretrained(
     "lllyasviel/control_v11p_sd15_openpose",
-    subfolder="faceonly",
     torch_dtype=torch.float16
 ).to("cuda")
 
@@ -34,27 +40,44 @@ def _get_facefix_pipeline(model_id: str):
             torch_dtype=torch.float16,
             safety_checker=None,
         ).to("cuda")
-        _facefix_pipe.enable_xformers_memory_efficient_attention()
-        _facefix_pipe.enable_model_cpu_offload()  # spart ~2 GB!
+        
+        # Nur wenn verfügbar
+        try:
+            _facefix_pipe.enable_xformers_memory_efficient_attention()
+        except:
+            print("XFormers nicht verfügbar, überspringe...")
+            
+        try:
+            _facefix_pipe.enable_model_cpu_offload()  # spart ~2 GB!
+        except:
+            print("CPU Offload nicht verfügbar, überspringe...")
+            
     return _facefix_pipe
 
 def apply_facefix(image: Image.Image, prompt: str, negative_prompt: str, seed: int, model_id: str):
-    pipe = _get_facefix_pipeline(model_id)
-
-    # Control-Images erzeugen
-    pose_img = openpose_face(image)
-    depth_img = depth_processor(image)
-
-    fixed = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        image=image,
-        control_image=[pose_img, depth_img],
-        controlnet_conditioning_scale=[0.85, 0.60],
-        strength=0.42,
-        num_inference_steps=20,
-        guidance_scale=7.0,
-        generator=torch.Generator("cuda").manual_seed(seed),
-    ).images[0]
-
-    return fixed
+    try:
+        pipe = _get_facefix_pipeline(model_id)
+
+        # Control-Images erzeugen
+        pose_img = openpose_face(image)
+        depth_img = depth_processor(image)
+
+        fixed = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image=image,
+            mask_image=None,  # Keine Maske für Face-Fix
+            control_image=[pose_img, depth_img],
+            controlnet_conditioning_scale=[0.85, 0.60],
+            strength=0.42,
+            num_inference_steps=20,
+            guidance_scale=7.0,
+            generator=torch.Generator("cuda").manual_seed(seed),
+        ).images[0]
+
+        return fixed
+        
+    except Exception as e:
+        print(f"Face-Fix Fehler: {e}")
+        # Bei Fehler das Originalbild zurückgeben
+        return image