Update controlnet_facefix.py

controlnet_facefix.py

@@ -1,83 +1,67 @@
 import torch
 from diffusers import StableDiffusionControlNetInpaintPipeline, ControlNetModel
-from controlnet_aux import OpenposeDetector, ZoeDetector
+from controlnet_aux import ZoeDetector
 from PIL import Image
+import time
 
-print("Lade OpenPose_faceonly + Depth für perfekte Gesichter...")
+print("Lade Face-Fix System (Depth-basiert)...")
 
-# Preprocessors (einmalig laden) - KORRIGIERTE VERSION
+# Vereinfachte Version NUR mit Depth
 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",
-    torch_dtype=torch.float16
-).to("cuda")
-
-controlnet_depth = ControlNetModel.from_pretrained(
-    "lllyasviel/control_v11f1e_sd15_depth",
-    torch_dtype=torch.float16
-).to("cuda")
-
-# Pipeline-Cache
-_facefix_pipe = None
-
-def _get_facefix_pipeline(model_id: str):
-    global _facefix_pipe
-    if _facefix_pipe is None:
-        print(f"Lade Face-Fix-Pipeline für Modell: {model_id}")
-        _facefix_pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
-            model_id,
-            controlnet=[controlnet_face, controlnet_depth],
-            torch_dtype=torch.float16,
-            safety_checker=None,
-        ).to("cuda")
-        
-        # Nur wenn verfügbar
+    depth_processor = ZoeDetector.from_pretrained("lllyasviel/ControlNet")
+    print("✅ Depth Processor geladen")
+    
+    controlnet_depth = ControlNetModel.from_pretrained(
+        "lllyasviel/control_v11f1e_sd15_depth",
+        torch_dtype=torch.float16
+    ).to("cuda")
+    print("✅ ControlNet Depth geladen")
+    
+    _facefix_pipe = None
+    
+    def _get_facefix_pipeline(model_id: str):
+        global _facefix_pipe
+        if _facefix_pipe is None:
+            print(f"Lade Face-Fix-Pipeline für Modell: {model_id}")
+            _facefix_pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
+                model_id,
+                controlnet=[controlnet_depth],
+                torch_dtype=torch.float16,
+                safety_checker=None,
+            ).to("cuda")
+        return _facefix_pipe
+    
+    def apply_facefix(image: Image.Image, prompt: str, negative_prompt: str, seed: int, model_id: str):
         try:
-            _facefix_pipe.enable_xformers_memory_efficient_attention()
-        except:
-            print("XFormers nicht verfügbar, überspringe...")
+            pipe = _get_facefix_pipeline(model_id)
+            depth_img = depth_processor(image)
             
-        try:
-            _facefix_pipe.enable_model_cpu_offload()  # spart ~2 GB!
-        except:
-            print("CPU Offload nicht verfügbar, überspringe...")
+            face_prompt = prompt + ", perfect face, detailed skin"
+            face_negative = negative_prompt + ", deformed face"
             
-    return _facefix_pipe
-
-def apply_facefix(image: Image.Image, prompt: str, negative_prompt: str, seed: int, model_id: str):
-    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
+            fixed = pipe(
+                prompt=face_prompt,
+                negative_prompt=face_negative,
+                image=image,
+                mask_image=None,
+                control_image=[depth_img],
+                controlnet_conditioning_scale=[0.7],
+                strength=0.35,
+                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 Error: {e}")
+            return image
+            
+except Exception as e:
+    print(f"❌ Face-Fix Setup fehlgeschlagen: {e}")
+    
+    # Fallback: Dummy-Funktion die nichts macht
+    def apply_facefix(image: Image.Image, prompt: str, negative_prompt: str, seed: int, model_id: str):
+        print("⚠️ Face-Fix nicht verfügbar, gebe Original zurück")
         return image