Update generator.py

generator.py

@@ -1,5 +1,6 @@
 """
 Generation logic for Pixagram AI Pixel Art Generator
+UPDATED VERSION with simplified InstantID face preservation
 """
 import torch
 import numpy as np
@@ -14,17 +15,18 @@ from config import (
 )
 from utils import (
     sanitize_text, enhanced_color_match, color_match, create_face_mask,
-    draw_kps, get_demographic_description, calculate_optimal_size, enhance_face_crop
+    draw_kps, get_demographic_description, calculate_optimal_size, enhance_face_crop,
+    safe_image_size, ensure_int
 )
 from models import (
-    load_face_analysis, load_depth_detector, load_controlnets, load_image_encoder,
-    load_sdxl_pipeline, load_lora, setup_ip_adapter, setup_compel,
+    load_face_analysis, load_depth_detector, load_controlnets,
+    load_sdxl_pipeline, load_lora, setup_compel,
     setup_scheduler, optimize_pipeline, load_caption_model, set_clip_skip
 )
 
 
 class RetroArtConverter:
-    """Main class for retro art generation"""
+    """Main class for retro art generation with InstantID face preservation"""
     
     def __init__(self):
         self.device = device
@@ -33,8 +35,7 @@ class RetroArtConverter:
             'custom_checkpoint': False,
             'lora': False,
             'instantid': False,
-            'zoe_depth': False,
-            'ip_adapter': False
+            'zoe_depth': False
         }
         
         # Initialize face analysis
@@ -44,27 +45,14 @@ class RetroArtConverter:
         self.zoe_depth, zoe_success = load_depth_detector()
         self.models_loaded['zoe_depth'] = zoe_success
         
-        # Load ControlNets
-        controlnet_depth, self.controlnet_instantid, instantid_success = load_controlnets()
-        self.controlnet_depth = controlnet_depth
-        self.instantid_enabled = instantid_success
-        self.models_loaded['instantid'] = instantid_success
+        # Load ControlNets - ALWAYS as list for InstantID pipeline
+        controlnet_instantid, controlnet_depth = load_controlnets()
+        controlnets = [controlnet_instantid, controlnet_depth]
+        self.models_loaded['instantid'] = True
         
-        # Load image encoder
-        if self.instantid_enabled:
-            self.image_encoder = load_image_encoder()
-        else:
-            self.image_encoder = None
-        
-        # Determine which controlnets to use
-        if self.instantid_enabled and self.controlnet_instantid is not None:
-            controlnets = [self.controlnet_instantid, controlnet_depth]
-            print(f"Initializing with multiple ControlNets: InstantID + Depth")
-        else:
-            controlnets = controlnet_depth
-            print(f"Initializing with single ControlNet: Depth only")
+        print("Initializing InstantID pipeline with Face + Depth ControlNets")
         
-        # Load SDXL pipeline
+        # Load SDXL pipeline with InstantID (handles IP-Adapter internally)
         self.pipe, checkpoint_success = load_sdxl_pipeline(controlnets)
         self.models_loaded['custom_checkpoint'] = checkpoint_success
         
@@ -72,15 +60,6 @@ class RetroArtConverter:
         lora_success = load_lora(self.pipe)
         self.models_loaded['lora'] = lora_success
         
-        # Setup IP-Adapter
-        if self.instantid_enabled and self.image_encoder is not None:
-            self.image_proj_model, ip_adapter_success = setup_ip_adapter(self.pipe, self.image_encoder)
-            self.models_loaded['ip_adapter'] = ip_adapter_success
-        else:
-            print("[INFO] Face preservation: InstantID ControlNet keypoints only")
-            self.models_loaded['ip_adapter'] = False
-            self.image_proj_model = None
-        
         # Setup Compel
         self.compel, self.use_compel = setup_compel(self.pipe)
         
@@ -99,21 +78,14 @@ class RetroArtConverter:
                 print("  [OK] Using GIT for detailed captions")
             elif self.caption_model_type == "blip":
                 print("  [OK] Using BLIP for standard captions")
-            else:
-                print("  [OK] Caption model loaded")
-        
         
         # Set CLIP skip
         set_clip_skip(self.pipe)
         
-        # Track controlnet configuration
-        self.using_multiple_controlnets = isinstance(controlnets, list)
-        print(f"Pipeline initialized with {'multiple' if self.using_multiple_controlnets else 'single'} ControlNet(s)")
-        
         # Print model status
         self._print_status()
         
-        print("  [OK] Model initialization complete!")
+        print("  [OK] Model initialization complete with InstantID!")
     
     def _print_status(self):
         """Print model loading status"""
@@ -121,309 +93,82 @@ class RetroArtConverter:
         for model, loaded in self.models_loaded.items():
             status = "[OK] LOADED" if loaded else "[FALLBACK/DISABLED]"
             print(f"{model}: {status}")
+        print("InstantID Pipeline: [OK] Active with built-in IP-Adapter")
         print("===================\n")
-        
-        print("=== UPGRADE VERIFICATION ===")
-        try:
-            from resampler_enhanced import EnhancedResampler
-            from ip_attention_processor_enhanced import EnhancedIPAttnProcessor2_0
-            
-            resampler_check = isinstance(self.image_proj_model, EnhancedResampler) if hasattr(self, 'image_proj_model') and self.image_proj_model is not None else False
-            custom_attn_check = any(isinstance(p, EnhancedIPAttnProcessor2_0) for p in self.pipe.unet.attn_processors.values()) if hasattr(self, 'pipe') else False
-            
-            print(f"Enhanced Perceiver Resampler: {'[OK] ACTIVE' if resampler_check else '[INFO] Not active'}")
-            print(f"Enhanced IP-Adapter Attention: {'[OK] ACTIVE' if custom_attn_check else '[INFO] Not active'}")
-            
-            if resampler_check and custom_attn_check:
-                print("[SUCCESS] Face preservation upgrade fully active")
-                print("  Expected improvement: +10-15% face similarity")
-            elif resampler_check or custom_attn_check:
-                print("[PARTIAL] Some upgrades active")
-            else:
-                print("[INFO] Using standard components")
-        except Exception as e:
-            print(f"[INFO] Verification skipped: {e}")
-        print("============================\n")
     
     def get_depth_map(self, image):
-            """Generate depth map using Zoe Depth"""
-            if self.zoe_depth is not None:
-                try:
-                    if image.mode != 'RGB':
-                        image = image.convert('RGB')
-                    
-                    orig_width, orig_height = image.size
-                    # **FIX 1 START: Ensure all size variables are standard Python int**
-                    orig_width = int(orig_width)
-                    orig_height = int(orig_height)
-                    
-                    # FIXED: Use multiples of 64 (not 32)
-                    target_width = int((orig_width // 64) * 64)
-                    target_height = int((orig_height // 64) * 64)
-                    
-                    target_width = int(max(64, target_width))
-                    target_height = int(max(64, target_height))
-                    
-                    # Create an explicit tuple of standard ints
-                    size_for_depth = (int(target_width), int(target_height))
-                    
-                    # Always resize using the explicit int tuple to avoid numpy.int64 issues
-                    # This replaces the conditional resize
-                    image_for_depth = image.resize(size_for_depth, Image.LANCZOS)
-                    
-                    if target_width != orig_width or target_height != orig_height:
-                        print(f"[DEPTH] Resized for ZoeDetector: {orig_width}x{orig_height} -> {target_width}x{target_height}")
-                    
-                    # FIXED: Add torch.no_grad() wrapper
-                    with torch.no_grad():
-                        depth_image = self.zoe_depth(image_for_depth) # Use the correctly-typed resized image
-                    
-                    depth_width, depth_height = depth_image.size
-                    if depth_width != orig_width or depth_height != orig_height:
-                        # Resize back to the original size that get_depth_map received
-                        depth_image = depth_image.resize((int(orig_width), int(orig_height)), Image.LANCZOS)
-                    # **FIX 1 END**
-                    
-                    print(f"[DEPTH] Zoe depth map generated: {orig_width}x{orig_height}")
-                    return depth_image
-                    
-                except Exception as e:
-                    print(f"[DEPTH] ZoeDetector failed ({e}), falling back to grayscale depth")
-                    gray = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
-                    depth_colored = cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB)
-                    return Image.fromarray(depth_colored)
-            else:
-                gray = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
-                depth_colored = cv2.cvtColor(gray, cv2.COLOR_GRAY2RGB)
-                return Image.fromarray(depth_colored)    
-
-    
-    def add_trigger_word(self, prompt):
-        """Add trigger word to prompt if not present"""
-        if TRIGGER_WORD.lower() not in prompt.lower():
-            # **FIX 3 START: Handle empty or blank prompt**
-            if not prompt or not prompt.strip():
-                return TRIGGER_WORD
-            # **FIX 3 END**
-            return f"{TRIGGER_WORD}, {prompt}"
-        return prompt
-    
-    def extract_multi_scale_face(self, face_crop, face):
-        """
-        Extract face features at multiple scales for better detail.
-        +1-2% improvement in face preservation.
-        """
-        try:
-            multi_scale_embeds = []
-            
-            for scale in MULTI_SCALE_FACTORS:
-                # Resize
-                w, h = face_crop.size
-                scaled_size = (int(w * scale), int(h * scale))
-                scaled_crop = face_crop.resize(scaled_size, Image.LANCZOS)
+        """Generate depth map using Zoe Depth"""
+        if self.zoe_depth is not None:
+            try:
+                if image.mode != 'RGB':
+                    image = image.convert('RGB')
                 
-                # Pad/crop back to original
-                scaled_crop = scaled_crop.resize((w, h), Image.LANCZOS)
+                # Use safe helpers for type safety
+                orig_width, orig_height = safe_image_size(image)
                 
-                # Extract features
-                scaled_array = cv2.cvtColor(np.array(scaled_crop), cv2.COLOR_RGB2BGR)
-                scaled_faces = self.face_app.get(scaled_array)
+                # FIXED: Use multiples of 64 (not 32)
+                target_width = ensure_int((orig_width // 64) * 64)
+                target_height = ensure_int((orig_height // 64) * 64)
                 
-                if len(scaled_faces) > 0:
-                    multi_scale_embeds.append(scaled_faces[0].normed_embedding)
-            
-            # Average embeddings
-            if len(multi_scale_embeds) > 0:
-                averaged = np.mean(multi_scale_embeds, axis=0)
-                # Renormalize
-                averaged = averaged / np.linalg.norm(averaged)
-                print(f"[MULTI-SCALE] Combined {len(multi_scale_embeds)} scales")
-                return averaged
-            
-            return face.normed_embedding
-        
-        except Exception as e:
-            print(f"[MULTI-SCALE] Failed: {e}, using single scale")
-            return face.normed_embedding
-    
-    def detect_face_quality(self, face):
-        """
-        Detect face quality and adaptively adjust parameters.
-        +2-3% consistency improvement.
-        """
-        try:
-            bbox = face.bbox
-            face_size = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
-            det_score = float(face.det_score) if hasattr(face, 'det_score') else 1.0
-            
-            # Small face -> boost identity preservation
-            if face_size < ADAPTIVE_THRESHOLDS['small_face_size']:
-                return ADAPTIVE_PARAMS['small_face'].copy()
-            
-            # Low confidence -> boost preservation
-            elif det_score < ADAPTIVE_THRESHOLDS['low_confidence']:
-                return ADAPTIVE_PARAMS['low_confidence'].copy()
-            
-            # Check for profile/side view (if pose available)
-            elif hasattr(face, 'pose') and len(face.pose) > 1:
-                try:
-                    yaw = float(face.pose[1])
-                    if abs(yaw) > ADAPTIVE_THRESHOLDS['profile_angle']:
-                        return ADAPTIVE_PARAMS['profile_view'].copy()
-                except (ValueError, TypeError, IndexError):
-                    pass
-            
-            # Good quality face - use provided parameters
-            return None
-        
-        except Exception as e:
-            print(f"[ADAPTIVE] Quality detection failed: {e}")
-            return None
-    
-    def validate_and_adjust_parameters(self, strength, guidance_scale, lora_scale, 
-                                       identity_preservation, identity_control_scale,
-                                       depth_control_scale, consistency_mode=True):
-        """
-        Enhanced parameter validation with stricter rules for consistency.
-        """
-        if consistency_mode:
-            print("[CONSISTENCY] Applying strict parameter validation...")
-            adjustments = []
-            
-            # Rule 1: Strong inverse relationship between identity and LORA
-            if identity_preservation > 1.2:
-                original_lora = lora_scale
-                lora_scale = min(lora_scale, 1.0)
-                if abs(lora_scale - original_lora) > 0.01:
-                    adjustments.append(f"LORA: {original_lora:.2f}->{lora_scale:.2f} (high identity)")
-            
-            # Rule 2: Strength-based profile activation
-            if strength < 0.5:
-                # Maximum preservation mode
-                if identity_preservation < 1.3:
-                    original_identity = identity_preservation
-                    identity_preservation = 1.3
-                    adjustments.append(f"Identity: {original_identity:.2f}->{identity_preservation:.2f} (max preservation)")
-                if lora_scale > 0.9:
-                    original_lora = lora_scale
-                    lora_scale = 0.9
-                    adjustments.append(f"LORA: {original_lora:.2f}->{lora_scale:.2f} (max preservation)")
-                if guidance_scale > 1.3:
-                    original_cfg = guidance_scale
-                    guidance_scale = 1.3
-                    adjustments.append(f"CFG: {original_cfg:.2f}->{guidance_scale:.2f} (max preservation)")
-                    
-            elif strength > 0.7:
-                # Artistic transformation mode
-                if identity_preservation > 1.0:
-                    original_identity = identity_preservation
-                    identity_preservation = 1.0
-                    adjustments.append(f"Identity: {original_identity:.2f}->{identity_preservation:.2f} (artistic mode)")
-                if lora_scale < 1.2:
-                    original_lora = lora_scale
-                    lora_scale = 1.2
-                    adjustments.append(f"LORA: {original_lora:.2f}->{lora_scale:.2f} (artistic mode)")
-            
-            # Rule 3: CFG-LORA relationship
-            if guidance_scale > 1.4 and lora_scale > 1.2:
-                original_lora = lora_scale
-                lora_scale = 1.1
-                adjustments.append(f"LORA: {original_lora:.2f}->{lora_scale:.2f} (high CFG detected)")
-            
-            # Rule 4: LCM sweet spot enforcement
-            original_cfg = guidance_scale
-            guidance_scale = max(1.0, min(guidance_scale, 1.5))
-            if abs(guidance_scale - original_cfg) > 0.01:
-                adjustments.append(f"CFG: {original_cfg:.2f}->{guidance_scale:.2f} (LCM optimal)")
-            
-            # Rule 5: ControlNet balance
-            total_control = identity_control_scale + depth_control_scale
-            if total_control > 1.7:
-                scale_factor = 1.7 / total_control
-                original_id_ctrl = identity_control_scale
-                original_depth_ctrl = depth_control_scale
-                identity_control_scale *= scale_factor
-                depth_control_scale *= scale_factor
-                adjustments.append(f"ControlNets balanced: ID {original_id_ctrl:.2f}->{identity_control_scale:.2f}, Depth {original_depth_ctrl:.2f}->{depth_control_scale:.2f}")
-            
-            # Report adjustments
-            if adjustments:
-                print("  [OK] Applied adjustments:")
-                for adj in adjustments:
-                    print(f"    - {adj}")
-            else:
-                print("  [OK] Parameters already optimal")
-        
-        return strength, guidance_scale, lora_scale, identity_preservation, identity_control_scale, depth_control_scale
-    
-    def generate_caption(self, image, max_length=None, num_beams=None):
-        """Generate a descriptive caption for the image (supports BLIP-2, GIT, BLIP)."""
-        if not self.caption_enabled or self.caption_model is None:
-            return None
-        
-        # Set defaults based on model type
-        if max_length is None:
-            if self.caption_model_type == "blip2":
-                max_length = 50  # BLIP-2 can handle longer captions
-            elif self.caption_model_type == "git":
-                max_length = 40  # GIT also produces good long captions
-            else:
-                max_length = CAPTION_CONFIG['max_length']  # BLIP base (20)
-        
-        if num_beams is None:
-            num_beams = CAPTION_CONFIG['num_beams']
-        
-        try:
-            if self.caption_model_type == "blip2":
-                # BLIP-2 specific processing
-                inputs = self.caption_processor(image, return_tensors="pt").to(self.device, self.dtype)
+                target_width = ensure_int(max(64, target_width))
+                target_height = ensure_int(max(64, target_height))
                 
-                with torch.no_grad():
-                    output = self.caption_model.generate(
-                        **inputs,
-                        max_length=max_length,
-                        num_beams=num_beams,
-                        min_length=10,  # Encourage longer captions
-                        length_penalty=1.0,
-                        repetition_penalty=1.5,
-                        early_stopping=True
-                    )
+                # Create an explicit tuple of standard ints
+                size_for_depth = (target_width, target_height)
                 
-                caption = self.caption_processor.decode(output[0], skip_special_tokens=True)
+                # Always resize using the explicit int tuple
+                image_for_depth = image.resize(size_for_depth, Image.LANCZOS)
                 
-            elif self.caption_model_type == "git":
-                # GIT specific processing
-                inputs = self.caption_processor(images=image, return_tensors="pt").to(self.device, self.dtype)
+                # Generate depth map
+                depth_image = self.zoe_depth(image_for_depth, detect_resolution=512, image_resolution=1024)
                 
-                with torch.no_grad():
-                    output = self.caption_model.generate(
-                        pixel_values=inputs.pixel_values,
-                        max_length=max_length,
-                        num_beams=num_beams,
-                        min_length=10,
-                        length_penalty=1.0,
-                        repetition_penalty=1.5,
-                        early_stopping=True
-                    )
+                # Resize to match original if needed
+                if (depth_image.width, depth_image.height) != (orig_width, orig_height):
+                    depth_image = depth_image.resize((orig_width, orig_height), Image.LANCZOS)
                 
-                caption = self.caption_processor.batch_decode(output, skip_special_tokens=True)[0]
+                # Convert to RGB if needed
+                if depth_image.mode != 'RGB':
+                    depth_image = depth_image.convert('RGB')
                 
-            else:
-                # BLIP base processing
-                inputs = self.caption_processor(image, return_tensors="pt").to(self.device, self.dtype)
+                return depth_image, np.array(depth_image)
                 
-                with torch.no_grad():
-                    output = self.caption_model.generate(
-                        **inputs,
-                        max_length=max_length,
-                        num_beams=num_beams,
-                        early_stopping=True
-                    )
+            except Exception as e:
+                print(f"Depth map generation failed: {e}")
+                import traceback
+                traceback.print_exc()
+                return None, None
+        else:
+            print("  Zoe Depth not available")
+            return None, None
+    
+    def generate_caption(self, image):
+        """Generate caption for image using loaded caption model"""
+        if not self.caption_enabled or self.caption_model is None:
+            return None
+        
+        try:
+            if self.caption_model_type == 'git':
+                # GIT model
+                pixel_values = self.caption_processor(images=image, return_tensors="pt").pixel_values
+                pixel_values = pixel_values.to(device=self.device, dtype=self.dtype)
+                
+                generated_ids = self.caption_model.generate(
+                    pixel_values=pixel_values,
+                    max_length=CAPTION_CONFIG['max_length']
+                )
+                caption = self.caption_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
                 
-                caption = self.caption_processor.decode(output[0], skip_special_tokens=True)
+            elif self.caption_model_type == 'blip':
+                # BLIP model
+                inputs = self.caption_processor(image, return_tensors="pt").to(self.device, self.dtype)
+                out = self.caption_model.generate(**inputs, max_new_tokens=CAPTION_CONFIG['max_length'])
+                caption = self.caption_processor.decode(out[0], skip_special_tokens=True)
+            
+            else:
+                return None
+            
+            return sanitize_text(caption)
             
-            return caption.strip()
-        
         except Exception as e:
             print(f"Caption generation failed: {e}")
             return None
@@ -431,134 +176,111 @@ class RetroArtConverter:
     def generate_retro_art(
         self,
         input_image,
-        prompt="retro game character, vibrant colors, detailed",
-        negative_prompt="blurry, low quality, ugly, distorted",
+        prompt,
+        negative_prompt="",
         num_inference_steps=12,
-        guidance_scale=1.0,
-        depth_control_scale=0.8,
+        guidance_scale=1.3,
+        depth_control_scale=0.75,
         identity_control_scale=0.85,
         lora_scale=1.0,
-        identity_preservation=0.8,
-        strength=0.75,
+        identity_preservation=1.2,
+        strength=0.50,
         enable_color_matching=False,
         consistency_mode=True,
         seed=-1
     ):
-        """Generate retro art with img2img pipeline and enhanced InstantID"""
-        
-        # Sanitize text inputs
-        prompt = sanitize_text(prompt)
-        negative_prompt = sanitize_text(negative_prompt)
+        """
+        Generate retro art with InstantID face preservation.
         
-        # **FIX 3 START: Ensure blank negative prompts are empty strings for Compel**
-        if not negative_prompt or not negative_prompt.strip():
-            negative_prompt = ""
-        # **FIX 3 END**
+        UPDATED: Simplified face embedding handling using InstantID pipeline.
+        """
         
-        # Apply parameter validation
+        # Validate and adjust parameters if consistency mode is enabled
         if consistency_mode:
-            print("\n[CONSISTENCY] Validating and adjusting parameters...")
-            strength, guidance_scale, lora_scale, identity_preservation, identity_control_scale, depth_control_scale = \
-                self.validate_and_adjust_parameters(
-                    strength, guidance_scale, lora_scale, identity_preservation, 
-                    identity_control_scale, depth_control_scale, consistency_mode
-                )
-        
-        # Add trigger word (handles blank prompt fix)
-        prompt = self.add_trigger_word(prompt)
-        
-        # Calculate optimal size with flexible aspect ratio support
-        original_width, original_height = input_image.size
-        target_width, target_height = calculate_optimal_size(original_width, original_height)
+            # Ensure guidance scale is in optimal range for LCM
+            if guidance_scale < 1.0:
+                guidance_scale = 1.0
+            elif guidance_scale > 1.8:
+                guidance_scale = 1.8
+            
+            # Ensure identity preservation and lora scale balance
+            if identity_preservation > 1.5 and lora_scale > 1.2:
+                lora_scale = min(lora_scale, 1.0)
+            
+            # Ensure strength is reasonable
+            if strength < 0.3:
+                strength = 0.3
+            elif strength > 0.8:
+                strength = 0.8
         
-        print(f"Resizing from {original_width}x{original_height} to {target_width}x{target_height}")
-        print(f"Prompt: {prompt}")
-        print(f"Img2Img Strength: {strength}")
+        # Calculate optimal size
+        orig_width, orig_height = safe_image_size(input_image)
+        optimal_width, optimal_height = calculate_optimal_size(orig_width, orig_height)
         
-        # Resize with high quality
-        resized_image = input_image.resize((int(target_width), int(target_height)), Image.LANCZOS)
+        # Resize image
+        resized_image = input_image.resize((optimal_width, optimal_height), Image.LANCZOS)
         
         # Generate depth map
-        print("Generating Zoe depth map...")
-        depth_image = self.get_depth_map(resized_image)
-        if depth_image.size != (target_width, target_height):
-            depth_image = depth_image.resize((int(target_width), int(target_height)), Image.LANCZOS)
+        print("Generating depth map...")
+        depth_image, depth_array = self.get_depth_map(resized_image)
+        
+        if depth_image is None:
+            raise RuntimeError("Failed to generate depth map")
         
-        # Handle face detection
-        using_multiple_controlnets = self.using_multiple_controlnets
+        # Detect faces
+        print("Detecting faces...")
+        has_detected_faces = False
         face_kps_image = None
         face_embeddings = None
-        face_crop_enhanced = None
-        has_detected_faces = False
         face_bbox_original = None
         
-        if using_multiple_controlnets and self.face_app is not None:
-            print("Detecting faces and extracting keypoints...")
-            img_array = cv2.cvtColor(np.array(resized_image), cv2.COLOR_RGB2BGR)
-            faces = self.face_app.get(img_array)
-            
-            if len(faces) > 0:
-                has_detected_faces = True
-                print(f"Detected {len(faces)} face(s)")
-                
-                # Get largest face
-                face = sorted(faces, key=lambda x: (x.bbox[2] - x.bbox[0]) * (x.bbox[3] - x.bbox[1]))[-1]
-                
-                # ADAPTIVE PARAMETERS
-                adaptive_params = self.detect_face_quality(face)
-                if adaptive_params is not None:
-                    print(f"[ADAPTIVE] {adaptive_params['reason']}")
-                    identity_preservation = adaptive_params['identity_preservation']
-                    identity_control_scale = adaptive_params['identity_control_scale']
-                    guidance_scale = adaptive_params['guidance_scale']
-                    lora_scale = adaptive_params['lora_scale']
-                
-                # Extract face embeddings
-                face_embeddings_base = face.normed_embedding
-                
-                # Extract face crop
-                bbox = face.bbox.astype(int)
-                x1, y1, x2, y2 = bbox[0], bbox[1], bbox[2], bbox[3]
-                face_bbox_original = [x1, y1, x2, y2]
-                
-                # Add padding
-                face_width = x2 - x1
-                face_height = y2 - y1
-                padding_x = int(face_width * 0.3)
-                padding_y = int(face_height * 0.3)
-                x1 = max(0, x1 - padding_x)
-                y1 = max(0, y1 - padding_y)
-                x2 = min(resized_image.width, x2 + padding_x)
-                y2 = min(resized_image.height, y2 + padding_y)
-                
-                # Crop face region
-                face_crop = resized_image.crop((x1, y1, x2, y2))
-                
-                # MULTI-SCALE PROCESSING
-                face_embeddings = self.extract_multi_scale_face(face_crop, face)
-                
-                # Enhance face crop
-                face_crop_enhanced = enhance_face_crop(face_crop)
-                
-                # Draw keypoints
-                face_kps = face.kps
-                face_kps_image = draw_kps(resized_image, face_kps)
-                
-                # ENHANCED: Extract comprehensive facial attributes
-                from utils import get_facial_attributes, build_enhanced_prompt
-                facial_attrs = get_facial_attributes(face)
-                
-                # Update prompt with detected attributes
-                prompt = build_enhanced_prompt(prompt, facial_attrs, TRIGGER_WORD)
-                
-                # Legacy output for compatibility
-                age = facial_attrs['age']
-                gender_code = facial_attrs['gender']
-                det_score = facial_attrs['quality']
+        if self.face_detection_enabled and self.face_app is not None:
+            try:
+                faces = self.face_app.get(np.array(resized_image))
                 
-                gender_str = 'M' if gender_code == 1 else ('F' if gender_code == 0 else 'N/A')
-                print(f"Face info: bbox={face.bbox}, age={age if age else 'N/A'}, gender={gender_str}")
-                print(f"Face crop size: {face_crop.size}, enhanced: {face_crop_enhanced.size if face_crop_enhanced else 'N/A'}")
+                if len(faces) > 0:
+                    has_detected_faces = True
+                    face = faces[0]
+                    
+                    # Draw keypoints
+                    face_kps_image = draw_kps(resized_image, face.kps)
+                    
+                    # Get face embeddings (512D vector from InsightFace)
+                    face_embeddings = face.embedding
+                    
+                    # Get face bounding box for color matching
+                    face_bbox_original = face.bbox
+                    
+                    print(f"  [OK] Face detected")
+                    print(f"  - Embedding shape: {face_embeddings.shape}")
+                    print(f"  - Keypoints: {face.kps.shape}")
+                    print(f"  - Bbox: {face_bbox_original}")
+                    
+                    # Check for adaptive parameter adjustment
+                    face_area = (face.bbox[2] - face.bbox[0]) * (face.bbox[3] - face.bbox[1])
+                    det_score = face.det_score if hasattr(face, 'det_score') else 1.0
+                    
+                    # Apply adaptive adjustments
+                    if face_area < ADAPTIVE_THRESHOLDS['small_face_size']:
+                        print("  [ADAPTIVE] Small face detected - boosting preservation")
+                        identity_preservation = max(identity_preservation, ADAPTIVE_PARAMS['small_face']['identity_preservation'])
+                        identity_control_scale = max(identity_control_scale, ADAPTIVE_PARAMS['small_face']['identity_control_scale'])
+                    
+                    elif det_score < ADAPTIVE_THRESHOLDS['low_confidence']:
+                        print("  [ADAPTIVE] Low confidence - increasing identity weight")
+                        identity_preservation = max(identity_preservation, ADAPTIVE_PARAMS['low_confidence']['identity_preservation'])
+                        identity_control_scale = max(identity_control_scale, ADAPTIVE_PARAMS['low_confidence']['identity_control_scale'])
+                    
+                else:
+                    print("  No faces detected in image")
+                    
+            except Exception as e:
+                print(f"Face detection error: {e}")
+                has_detected_faces = False
+        
+        # Enhance prompt with trigger word
+        if TRIGGER_WORD not in prompt.lower():
+            prompt = f"{TRIGGER_WORD}, {prompt}"
         
         # Set LORA scale
         if hasattr(self.pipe, 'set_adapters') and self.models_loaded['lora']:
@@ -613,91 +335,54 @@ class RetroArtConverter:
         if hasattr(self.pipe, 'text_encoder'):
             pipe_kwargs["clip_skip"] = 2
         
-        # Configure ControlNet inputs
-        if using_multiple_controlnets and has_detected_faces and face_kps_image is not None:
-            print("Using InstantID (keypoints) + Depth ControlNets")
-            control_images = [face_kps_image, depth_image]
-            conditioning_scales = [identity_control_scale, depth_control_scale]
+        # ========================================
+        # SIMPLIFIED: Configure ControlNets + IP-Adapter
+        # ========================================
+        if has_detected_faces and face_kps_image is not None:
+            print("Using InstantID (keypoints + embeddings) + Depth ControlNets")
             
-            pipe_kwargs["control_image"] = control_images
-            pipe_kwargs["controlnet_conditioning_scale"] = conditioning_scales
-            
-            # Add face embeddings for IP-Adapter if available
-            if face_embeddings is not None and self.models_loaded.get('ip_adapter', False) and face_crop_enhanced is not None:
-                print(f"Processing InstantID face embeddings with Resampler...")
-                
-                with torch.no_grad():
-                    # Convert InsightFace embeddings to tensor
-                    face_emb_tensor = torch.from_numpy(face_embeddings).to(
-                        device=self.device, 
-                        dtype=self.dtype
-                    )
-                    
-                    # Reshape for Resampler: [1, 1, 512]
-                    face_emb_tensor = face_emb_tensor.reshape(1, -1, 512)
-                    
-                    # Pass through Resampler: [1, 1, 512] → [1, 16, 2048]
-                    face_proj_embeds = self.image_proj_model(face_emb_tensor)
-                    
-                    # Scale with identity preservation
-                    boosted_scale = identity_preservation * IDENTITY_BOOST_MULTIPLIER
-                    face_proj_embeds = face_proj_embeds * boosted_scale
-                    
-                    print(f"  - Face embedding: {face_emb_tensor.shape}")
-                    print(f"  - Resampler output: {face_proj_embeds.shape}")
-                    print(f"  - Scale: {boosted_scale:.2f}")
-                    
-                    # CRITICAL: Concatenate with text embeddings (not separate kwargs!)
-                    if 'prompt_embeds' in pipe_kwargs:
-                        # Compel encoded prompts
-                        original_embeds = pipe_kwargs['prompt_embeds']
-                        
-                        # Handle CFG (classifier-free guidance)
-                        if original_embeds.shape[0] > 1:  # Has negative + positive
-                            # Duplicate for negative + positive
-                            face_proj_embeds = torch.cat([
-                                torch.zeros_like(face_proj_embeds),  # Negative
-                                face_proj_embeds                      # Positive
-                            ], dim=0)
-                        
-                        # Concatenate: [batch, text_tokens, 2048] + [batch, 16, 2048]
-                        combined_embeds = torch.cat([original_embeds, face_proj_embeds], dim=1)
-                        pipe_kwargs['prompt_embeds'] = combined_embeds
-                        
-                        print(f"  - Text embeds: {original_embeds.shape}")
-                        print(f"  - Combined embeds: {combined_embeds.shape}")
-                        print(f"  [OK] Face embeddings concatenated successfully!")
-                        
-                    else:
-                        print(f"  [WARNING] Can't concatenate - no prompt_embeds (use Compel)")
+            # Control images: [face keypoints, depth map]
+            pipe_kwargs["control_image"] = [face_kps_image, depth_image]
             
-            elif has_detected_faces and self.models_loaded.get('ip_adapter', False):
-                # Face detected but embeddings unavailable
-                print("  Face detected but embeddings unavailable, using keypoints only")
-                # No need for dummy embeddings with concatenation approach
-        
-        elif using_multiple_controlnets and not has_detected_faces:
-            print("Multiple ControlNets available but no faces detected, using depth only")
-            control_images = [depth_image, depth_image]
-            conditioning_scales = [0.0, depth_control_scale]
+            # Conditioning scales: [identity, depth]
+            pipe_kwargs["controlnet_conditioning_scale"] = [
+                identity_control_scale,
+                depth_control_scale
+            ]
             
-            pipe_kwargs["control_image"] = control_images
-            pipe_kwargs["controlnet_conditioning_scale"] = conditioning_scales
+            # CRITICAL: Pass face embeddings for IP-Adapter
+            # The InstantID pipeline handles the Resampler internally!
+            if face_embeddings is not None:
+                print(f"Adding face embeddings for IP-Adapter...")
+                
+                # Just pass the embeddings - pipeline does everything!
+                pipe_kwargs["image_embeds"] = face_embeddings  # numpy array (512,)
+                
+                # Control IP-Adapter strength
+                boosted_scale = identity_preservation * IDENTITY_BOOST_MULTIPLIER
+                pipe_kwargs["ip_adapter_scale"] = boosted_scale
+                
+                print(f"  - Face embeddings shape: {face_embeddings.shape}")
+                print(f"  - IP-Adapter scale: {boosted_scale:.2f}")
+                print(f"  [OK] Face embeddings configured")
+            else:
+                print("  [WARNING] No face embeddings - using keypoints only")
         
         else:
-            print("Using Depth ControlNet only")
-            pipe_kwargs["control_image"] = depth_image
-            pipe_kwargs["controlnet_conditioning_scale"] = depth_control_scale
-        
+            print("No faces detected - using Depth ControlNet only")
+            
+            # Use depth for both ControlNet slots (identity scale = 0)
+            pipe_kwargs["control_image"] = [depth_image, depth_image]
+            pipe_kwargs["controlnet_conditioning_scale"] = [0.0, depth_control_scale]
         
         # Generate
         print(f"Generating with LCM: Steps={num_inference_steps}, CFG={guidance_scale}, Strength={strength}")
-        print(f"Controlnet scales - Identity: {identity_control_scale}, Depth: {depth_control_scale}")
+        print(f"ControlNet scales - Identity: {identity_control_scale}, Depth: {depth_control_scale}")
         result = self.pipe(**pipe_kwargs)
         
         generated_image = result.images[0]
         
-        # Post-processing
+        # Post-processing: Color matching
         if enable_color_matching and has_detected_faces:
             print("Applying enhanced face-aware color matching...")
             try:
@@ -721,7 +406,11 @@ class RetroArtConverter:
             except Exception as e:
                 print(f"Color matching failed: {e}")
         
+        # Memory cleanup
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        
         return generated_image
 
 
-print("[OK] Generator class ready")
+print("[OK] Generator class ready with InstantID pipeline")