model.py

import torch
import os
import cv2
import numpy as np
from config import Config

from diffusers import (
    ControlNetModel, 
    LCMScheduler
)
from diffusers.models.controlnets.multicontrolnet import MultiControlNetModel

# Import the custom pipeline from your local file
from pipeline_stable_diffusion_xl_instantid_img2img import StableDiffusionXLInstantIDImg2ImgPipeline

from huggingface_hub import snapshot_download, hf_hub_download
from insightface.app import FaceAnalysis
# --- MODIFIED: Import new detectors ---
from controlnet_aux import LeresDetector, LineartAnimeDetector
# --- END MODIFIED ---

class ModelHandler:
    def __init__(self):
        self.pipeline = None
        self.app = None # InsightFace
        # --- MODIFIED: Rename detectors ---
        self.leres_detector = None
        self.lineart_anime_detector = None
        # --- END MODIFIED ---
        self.face_analysis_loaded = False

    def load_face_analysis(self):
        """
        Load face analysis model. 
        Downloads from HF Hub to the path insightface expects.
        Forces CPU to avoid ZeroGPU initialization errors.
        """
        print("Loading face analysis model...")
        
        # insightface expects models in '{root}/models/{name}'
        # Since our root='.' and name='antelopev2', the expected path is './models/antelopev2'
        model_path = os.path.join(Config.ANTELOPEV2_ROOT, "models", Config.ANTELOPEV2_NAME)

        if not os.path.exists(os.path.join(model_path, "scrfd_10g_bnkps.onnx")):
            print(f"Downloading AntelopeV2 models from {Config.ANTELOPEV2_REPO} to {model_path}...")
            try:
                snapshot_download(
                    repo_id=Config.ANTELOPEV2_REPO,
                    local_dir=model_path, # Download to the correct expected path
                )
            except Exception as e:
                print(f"  [ERROR] Failed to download AntelopeV2 models: {e}")
                return False

        try:
            # Initialize with root='.' and name='antelopev2'
            self.app = FaceAnalysis(
                name=Config.ANTELOPEV2_NAME,
                root=Config.ANTELOPEV2_ROOT, 
                providers=['CPUExecutionProvider'] 
            )
            self.app.prepare(ctx_id=0, det_size=(640, 640))
            print(f"  [OK] Face analysis model loaded successfully.")
            return True
            
        except Exception as e:
            print(f"  [WARNING] Face detection system failed to initialize: {e}") 
            return False

    def load_models(self):
        # 1. Load Face Analysis
        self.face_analysis_loaded = self.load_face_analysis()

        # 2. Load ControlNets
        print("Loading ControlNets (InstantID, Zoe, LineArt)...")

        # Load the InstantID ControlNet from the correct subfolder
        print("Loading InstantID ControlNet from subfolder 'ControlNetModel'...")
        cn_instantid = ControlNetModel.from_pretrained(
            Config.INSTANTID_REPO,   # "InstantX/InstantID"
            subfolder="ControlNetModel", # Correct casing
            torch_dtype=Config.DTYPE
        )
        print("  [OK] Loaded InstantID ControlNet.")
        
        # Load other ControlNets normally
        print("Loading Zoe and LineArt ControlNets...")
        cn_zoe = ControlNetModel.from_pretrained(Config.CN_ZOE_REPO, torch_dtype=Config.DTYPE)
        cn_lineart = ControlNetModel.from_pretrained(Config.CN_LINEART_REPO, torch_dtype=Config.DTYPE)

        # --- Manually wrap the list of models in a MultiControlNetModel ---
        print("Wrapping ControlNets in MultiControlNetModel...")
        controlnet_list = [cn_instantid, cn_zoe, cn_lineart]
        controlnet = MultiControlNetModel(controlnet_list)
        # --- End wrapping ---

        # 3. Load SDXL Pipeline
        print(f"Loading SDXL Pipeline ({Config.CHECKPOINT_FILENAME})...")
        
        # Manually download the checkpoint file first.
        checkpoint_local_path = os.path.join("./models", Config.CHECKPOINT_FILENAME)
        if not os.path.exists(checkpoint_local_path):
            print(f"Downloading checkpoint to {checkpoint_local_path}...")
            hf_hub_download(
                repo_id=Config.REPO_ID,
                filename=Config.CHECKPOINT_FILENAME,
                local_dir="./models",
                local_dir_use_symlinks=False
            )
        
        # Use the custom Img2Img pipeline class you provided, loading from the LOCAL FILE
        print(f"Loading pipeline from local file: {checkpoint_local_path}")
        self.pipeline = StableDiffusionXLInstantIDImg2ImgPipeline.from_single_file(
            checkpoint_local_path, # Pass the local path
            controlnet=controlnet, # Pass the single, wrapped object
            torch_dtype=Config.DTYPE,
            use_safetensors=True
        )
        
        self.pipeline.to(Config.DEVICE)

        # 4. Set Scheduler
        self.pipeline.scheduler = LCMScheduler.from_config(self.pipeline.scheduler.config)

        # 5. Load Adapters (IP-Adapter & LoRA)
        print("Loading Adapters (IP-Adapter & LoRA)...")
        
        # Download the ip-adapter.bin file and pass its local path
        ip_adapter_filename = "ip-adapter.bin"
        ip_adapter_local_path = os.path.join("./models", ip_adapter_filename)

        if not os.path.exists(ip_adapter_local_path):
            print(f"Downloading IP-Adapter to {ip_adapter_local_path}...")
            hf_hub_download(
                repo_id=Config.INSTANTID_REPO,
                filename=ip_adapter_filename,
                local_dir="./models",
                local_dir_use_symlinks=False
            )
        
        print(f"Loading IP-Adapter from local file: {ip_adapter_local_path}")
        self.pipeline.load_ip_adapter_instantid(ip_adapter_local_path) # Pass local path
        
        print("Loading LoRA weights...")
        self.pipeline.load_lora_weights(Config.REPO_ID, weight_name=Config.LORA_FILENAME)
        
        # --- NEW: Fuse LoRA at build time with fixed strength ---
        print(f"Fusing LoRA with scale {Config.LORA_STRENGTH}...")
        self.pipeline.fuse_lora(lora_scale=Config.LORA_STRENGTH)
        print("  [OK] LoRA fused.")
        
        # --- DISABLED torch.compile due to runtime errors ---
        # try:
        #     print("Compiling UNet with torch.compile...")
        #     self.pipeline.unet = torch.compile(self.pipeline.unet, mode="reduce-overhead", fullgraph=True)
        #     print("  [OK] UNet compiled.")
        # except Exception as e:
        #     print(f"  [WARNING] torch.compile failed: {e}. Running without compilation.")
        
        # 6. Load Preprocessors
        # --- MODIFIED: Load new detectors ---
        print("Loading Preprocessors (LeReS, LineArtAnime)...")
        self.leres_detector = LeresDetector.from_pretrained(Config.ANNOTATOR_REPO)
        self.lineart_anime_detector = LineartAnimeDetector.from_pretrained(Config.ANNOTATOR_REPO)
        # --- END MODIFIED ---
        
        print("--- All models loaded successfully ---")

    def get_face_embedding(self, image):
        """Extracts face embedding, returns None if no face is found."""
        if not self.face_analysis_loaded:
            return None
            
        try:
            # Convert PIL to CV2
            # --- FIX: Corrected OpenCV attribute ---
            cv2_img = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) 
            faces = self.app.get(cv2_img)
            
            if len(faces) == 0:
                return None
                
            # Sort by size (width * height) to find the main character
            faces = sorted(faces, key=lambda x: (x['bbox'][2]-x['bbox'][0])*(x['bbox'][3]-x['bbox'][1]), reverse=True)
            
            # Return the largest face
            return torch.tensor(faces[0].normed_embedding).unsqueeze(0)
        except Exception as e:
            print(f"Face embedding extraction failed: {e}")
            return None