Delete controlnet_facefix.py

controlnet_facefix.py

@@ -1,228 +0,0 @@
-# controlnet_facefix.py - PURE QUALITY ENHANCEMENT WITH MINIMAL CHANGE
-import torch
-from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, AutoencoderKL
-from PIL import Image, ImageFilter, ImageEnhance
-import time
-import cv2
-import numpy as np
-from torchvision import transforms
-
-print("="*60)
-print("FACE-FIX: REINE QUALITÄTSVERBESSERUNG - MINIMALE ÄNDERUNG")
-print("="*60)
-
-_components_loaded = False
-_controlnet_depth = None  
-_controlnet_pose = None
-_pipeline = None
-
-def _initialize_components():
-    """Lade nur notwendige Komponenten"""
-    global _components_loaded, _controlnet_depth, _controlnet_pose
-    
-    if _components_loaded:
-        return True
-    
-    print("⚠️ Lade nur OpenPose (Depth wird deaktiviert)...")
-    
-    try:
-        # NUR OPENPOSE - Depth verändert zu viel
-        _controlnet_pose = ControlNetModel.from_pretrained(
-            "lllyasviel/sd-controlnet-openpose",
-            torch_dtype=torch.float16
-        )
-        print("✅ OpenPose geladen")
-        
-        # Depth wird NICHT geladen - es verändert den Hintergrund zu stark
-        _controlnet_depth = None
-        
-        _components_loaded = True
-        return True
-    except Exception as e:
-        print(f"❌ Fehler: {e}")
-        return False
-
-def _extract_precise_pose(image):
-    """SEHR PRÄZISE Pose-Extraktion nur für Gesicht"""
-    try:
-        img_array = np.array(image.convert("RGB"))
-        gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
-        
-        # EXTREM NIEDRIGE Thresholds für minimale Kanten
-        edges = cv2.Canny(gray, 15, 45)  # Nur feinste Kanten
-        
-        # Face detection für Fokus
-        face_cascade = cv2.CascadeClassifier(
-            cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'
-        )
-        faces = face_cascade.detectMultiScale(gray, 1.1, 4)
-        
-        # Erstelle leere Pose Map
-        pose_map = np.zeros_like(img_array)
-        
-        # Nur Gesichtskanten einfügen
-        if len(faces) > 0:
-            for (x, y, w, h) in faces:
-                # Extrahiere Gesichtsregion
-                face_region = edges[y:y+h, x:x+w]
-                # Nur 10% der stärksten Kanten behalten
-                threshold = np.percentile(face_region[face_region > 0], 90)
-                face_region[face_region < threshold] = 0
-                pose_map[y:y+h, x:x+w, 0] = face_region
-        else:
-            # Falls kein Gesicht erkannt, minimale Kanten
-            pose_map[..., 0] = edges * 0.3  # Noch schwächer
-        
-        return Image.fromarray(pose_map)
-    except:
-        # Fallback: minimale Kanten
-        gray = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2GRAY)
-        edges = cv2.Canny(gray, 10, 30) * 0.2  # Sehr schwach
-        return Image.fromarray(edges).convert("RGB")
-
-def _apply_face_enhancement(image):
-    """EINFACHE Face Enhancement ohne AI"""
-    try:
-        img_array = np.array(image.convert("RGB"))
-        
-        # 1. Scharfe Kanten (nur leicht)
-        sharpened = cv2.filter2D(img_array, -1, 
-            np.array([[-0.5, -0.5, -0.5],
-                      [-0.5,  5.0, -0.5],
-                      [-0.5, -0.5, -0.5]]) / 3.0)
-        
-        # 2. Leichter De-Noise
-        denoised = cv2.fastNlMeansDenoisingColored(sharpened, None, 3, 3, 7, 21)
-        
-        # 3. Kontrast leicht erhöhen
-        lab = cv2.cvtColor(denoised, cv2.COLOR_RGB2LAB)
-        l, a, b = cv2.split(lab)
-        clahe = cv2.createCLAHE(clipLimit=1.0, tileGridSize=(8,8))
-        l = clahe.apply(l)
-        enhanced = cv2.merge([l, a, b])
-        enhanced = cv2.cvtColor(enhanced, cv2.COLOR_LAB2RGB)
-        
-        return Image.fromarray(enhanced)
-    except:
-        return image
-
-def apply_facefix(image: Image.Image, prompt: str, negative_prompt: str, seed: int, model_id: str):
-    """
-    SUPER-SUBTILE QUALITÄTSVERBESSERUNG
-    
-    Strategie:
-    1. NUR OpenPose (kein Depth - das verändert zu viel)
-    2. SEHR niedrige ControlNet-Stärke
-    3. Fast kein CFG Scale
-    4. Identischer Prompt
-    """
-    print("\n" + "🎯"*50)
-    print("SUBTILE QUALITÄTSVERBESSERUNG")
-    print(f"  Größe: {image.size}")
-    print("🎯"*50)
-    
-    start_time = time.time()
-    
-    # OPTION 1: Einfache non-AI Verbesserung (empfohlen)
-    print("\n⚡ OPTION 1: Einfache non-AI Verbesserung...")
-    enhanced = _apply_face_enhancement(image)
-    
-    # Optional: AI-Verbesserung nur wenn nötig
-    use_ai_enhancement = False  # Auf False setzen für minimale Änderung
-    
-    if not use_ai_enhancement:
-        duration = time.time() - start_time
-        print(f"✅ Non-AI Verbesserung in {duration:.1f}s")
-        return enhanced
-    
-    # OPTION 2: Minimale AI-Verbesserung (falls gewünscht)
-    print("⚠️  Starte MINIMALE AI-Verbesserung...")
-    
-    if not _initialize_components():
-        return enhanced
-    
-    # Control Map vorbereiten
-    original_size = image.size
-    control_size = (512, 512)
-    resized_image = image.resize(control_size, Image.Resampling.LANCZOS)
-    
-    # MINIMALE Pose Map
-    pose_img = _extract_precise_pose(resized_image)
-    pose_img.save("debug_minimal_pose.png")
-    
-    # Pipeline (nur falls nicht geladen)
-    global _pipeline
-    if _pipeline is None:
-        try:
-            print("🔄 Lade Pipeline...")
-            _pipeline = StableDiffusionControlNetPipeline.from_pretrained(
-                model_id,
-                controlnet=[_controlnet_pose],  # NUR OpenPose!
-                torch_dtype=torch.float16,
-                safety_checker=None,
-                requires_safety_checker=False,
-            )
-            
-            _pipeline.enable_attention_slicing()
-            _pipeline.enable_vae_slicing()
-            
-            print("✅ Pipeline geladen")
-        except Exception as e:
-            print(f"❌ Pipeline Fehler: {e}")
-            return enhanced
-    
-    try:
-        device = "cuda" if torch.cuda.is_available() else "cpu"
-        print(f"  Device: {device}")
-        pipeline = _pipeline.to(device)
-        
-        # KRITISCHE ÄNDERUNGEN:
-        # 1. GLEICHER PROMPT wie ursprünglich
-        # 2. SEHR niedrige ControlNet-Stärke
-        # 3. FAST KEIN CFG
-        
-        print("\n⚙️  EXTREM SUBTILE PARAMETER:")
-        print("   • OpenPose Strength: 0.3 (SEHR NIEDRIG)")
-        print("   • Steps: 15 (wenig)")
-        print("   • CFG: 2.0 (fast kein Guidance)")
-        print("   • Gleicher Seed")
-        
-        result = pipeline(
-            prompt=prompt,  # WICHTIG: GLEICHER PROMPT!
-            negative_prompt=f"{negative_prompt}, deformed, blurry",
-            image=[pose_img],  # Nur Pose
-            controlnet_conditioning_scale=[0.3],  # EXTREM NIEDRIG
-            num_inference_steps=15,               # WENIG Steps
-            guidance_scale=2.0,                   # FAST KEIN CFG
-            generator=torch.Generator(device).manual_seed(seed + 100),  # Leicht anderer Seed
-            height=512,
-            width=512,
-        ).images[0]
-        
-        # Zurück auf Originalgröße
-        if original_size != (512, 512):
-            result = result.resize(original_size, Image.Resampling.LANCZOS)
-        
-        # 50/50 Blend mit Original für noch weniger Änderung
-        result_array = np.array(result).astype(float)
-        original_array = np.array(image).astype(float)
-        
-        # 70% Original, 30% AI-result
-        blended = (original_array * 0.7 + result_array * 0.3).astype(np.uint8)
-        final_result = Image.fromarray(blended)
-        
-        duration = time.time() - start_time
-        print(f"\n✅ SUBTILE VERBESSERUNG in {duration:.1f}s")
-        print(f"   • 70% Original, 30% AI")
-        print(f"   • OpenPose: 0.3")
-        print(f"   • CFG: 2.0")
-        
-        return final_result
-        
-    except Exception as e:
-        print(f"\n❌ AI-Verbesserung fehlgeschlagen: {e}")
-        return enhanced
-
-print("="*60)
-print("FACE-FIX: REINE QUALITÄTSVERBESSERUNG")
-print("="*60)