models.py

"""
Model loading and initialization for Pixagram AI Pixel Art Generator
FIXED VERSION with proper IP-Adapter and BLIP-2 support
"""
import torch
import time
from diffusers import (
    StableDiffusionXLControlNetImg2ImgPipeline,
    ControlNetModel,
    AutoencoderKL,
    LCMScheduler
)
from diffusers.models.attention_processor import AttnProcessor2_0
from transformers import CLIPVisionModelWithProjection
from insightface.app import FaceAnalysis
from controlnet_aux import ZoeDetector
from huggingface_hub import hf_hub_download
from compel import Compel, ReturnedEmbeddingsType

# Use reference implementation's attention processor
from attention_processor import IPAttnProcessor2_0, AttnProcessor
from resampler import Resampler

from config import (
    device, dtype, MODEL_REPO, MODEL_FILES, HUGGINGFACE_TOKEN,
    FACE_DETECTION_CONFIG, CLIP_SKIP, DOWNLOAD_CONFIG
)


def download_model_with_retry(repo_id, filename, max_retries=None):
    """Download model with retry logic and proper token handling."""
    if max_retries is None:
        max_retries = DOWNLOAD_CONFIG['max_retries']
    
    for attempt in range(max_retries):
        try:
            print(f"  Attempting to download {filename} (attempt {attempt + 1}/{max_retries})...")
            
            kwargs = {"repo_type": "model"}
            if HUGGINGFACE_TOKEN:
                kwargs["token"] = HUGGINGFACE_TOKEN
            
            path = hf_hub_download(
                repo_id=repo_id,
                filename=filename,
                **kwargs
            )
            print(f"  [OK] Downloaded: {filename}")
            return path
            
        except Exception as e:
            print(f"  [WARNING] Download attempt {attempt + 1} failed: {e}")
            
            if attempt < max_retries - 1:
                print(f"  Retrying in {DOWNLOAD_CONFIG['retry_delay']} seconds...")
                time.sleep(DOWNLOAD_CONFIG['retry_delay'])
            else:
                print(f"  [ERROR] Failed to download {filename} after {max_retries} attempts")
                raise
    
    return None


def load_face_analysis():
    """Load face analysis model with proper error handling."""
    print("Loading face analysis model...")
    try:
        face_app = FaceAnalysis(
            name=FACE_DETECTION_CONFIG['model_name'],
            root='./models/insightface',
            providers=['CUDAExecutionProvider', 'CPUExecutionProvider']
        )
        face_app.prepare(
            ctx_id=FACE_DETECTION_CONFIG['ctx_id'], 
            det_size=FACE_DETECTION_CONFIG['det_size']
        )
        print("  [OK] Face analysis model loaded successfully")
        return face_app, True
    except Exception as e:
        print(f"  [WARNING] Face detection not available: {e}")
        return None, False


def load_depth_detector():
    """Load Zoe Depth detector."""
    print("Loading Zoe Depth detector...")
    try:
        zoe_depth = ZoeDetector.from_pretrained("lllyasviel/Annotators")
        zoe_depth.to(device)
        print("  [OK] Zoe Depth loaded successfully")
        return zoe_depth, True
    except Exception as e:
        print(f"  [WARNING] Zoe Depth not available: {e}")
        return None, False


def load_controlnets():
    """Load ControlNet models."""
    print("Loading ControlNet Zoe Depth model...")
    controlnet_depth = ControlNetModel.from_pretrained(
        "diffusers/controlnet-zoe-depth-sdxl-1.0",
        torch_dtype=dtype
    ).to(device)
    print("  [OK] ControlNet Depth loaded")
    
    print("Loading InstantID ControlNet...")
    try:
        controlnet_instantid = ControlNetModel.from_pretrained(
            "InstantX/InstantID",
            subfolder="ControlNetModel",
            torch_dtype=dtype
        ).to(device)
        print("  [OK] InstantID ControlNet loaded successfully")
        return controlnet_depth, controlnet_instantid, True
    except Exception as e:
        print(f"  [WARNING] InstantID ControlNet not available: {e}")
        return controlnet_depth, None, False


def load_image_encoder():
    """Load CLIP Image Encoder for IP-Adapter."""
    print("Loading CLIP Image Encoder for IP-Adapter...")
    try:
        image_encoder = CLIPVisionModelWithProjection.from_pretrained(
            "h94/IP-Adapter",
            subfolder="models/image_encoder",
            torch_dtype=dtype
        ).to(device)
        print("  [OK] CLIP Image Encoder loaded successfully")
        return image_encoder
    except Exception as e:
        print(f"  [ERROR] Could not load image encoder: {e}")
        return None


def load_sdxl_pipeline(controlnets):
    """Load SDXL checkpoint from HuggingFace Hub."""
    print("Loading SDXL checkpoint (horizon) with bundled VAE from HuggingFace Hub...")
    try:
        model_path = download_model_with_retry(MODEL_REPO, MODEL_FILES['checkpoint'])
        
        pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_single_file(
            model_path,
            controlnet=controlnets,
            torch_dtype=dtype,
            use_safetensors=True
        ).to(device)
        print("  [OK] Custom checkpoint loaded successfully (VAE bundled)")
        return pipe, True
    except Exception as e:
        print(f"  [WARNING] Could not load custom checkpoint: {e}")
        print("  Using default SDXL base model")
        pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
            "stabilityai/stable-diffusion-xl-base-1.0",
            controlnet=controlnets,
            torch_dtype=dtype,
            use_safetensors=True
        ).to(device)
        return pipe, False


def load_lora(pipe):
    """Load LORA from HuggingFace Hub."""
    print("Loading LORA (retroart) from HuggingFace Hub...")
    try:
        lora_path = download_model_with_retry(MODEL_REPO, MODEL_FILES['lora'])
        # **FIX 2: Add adapter_name="retroart"**
        pipe.load_lora_weights(lora_path, adapter_name="retroart")
        print(f"  [OK] LORA loaded successfully")
        return True
    except Exception as e:
        print(f"  [WARNING] Could not load LORA: {e}")
        return False


def setup_ip_adapter(pipe, image_encoder):
    """
    Setup IP-Adapter for InstantID face embeddings - PROPER IMPLEMENTATION.
    Based on the reference InstantID pipeline.
    """
    if image_encoder is None:
        return None, False
    
    print("Setting up IP-Adapter for InstantID face embeddings (proper implementation)...")
    try:
        # Download InstantID weights
        ip_adapter_path = download_model_with_retry(
            "InstantX/InstantID",
            "ip-adapter.bin"
        )
        
        # Load full state dict
        state_dict = torch.load(ip_adapter_path, map_location="cpu")
        
        # Extract image_proj and ip_adapter weights
        image_proj_state_dict = {}
        ip_adapter_state_dict = {}
        
        for key, value in state_dict.items():
            if key.startswith("image_proj."):
                image_proj_state_dict[key.replace("image_proj.", "")] = value
            elif key.startswith("ip_adapter."):
                ip_adapter_state_dict[key.replace("ip_adapter.", "")] = value
        
        # Create Resampler (image projection model) with CORRECT parameters from reference
        print("Creating Resampler (Perceiver architecture)...")
        image_proj_model = Resampler(
            dim=1280,                                       # Hidden dimension
            depth=4,                                        # IMPORTANT: 4 layers (not 8!)
            dim_head=64,                                    # Dimension per head
            heads=20,                                       # Number of heads
            num_queries=16,                                 # Number of output tokens
            embedding_dim=512,                              # InsightFace embedding dim
            output_dim=pipe.unet.config.cross_attention_dim,  # SDXL cross-attention dim (2048)
            ff_mult=4                                       # Feedforward multiplier
        )
        
        image_proj_model.eval()
        image_proj_model = image_proj_model.to(device, dtype=dtype)
        
        # Load image_proj weights
        if image_proj_state_dict:
            try:
                image_proj_model.load_state_dict(image_proj_state_dict, strict=True)
                print("  [OK] Resampler loaded with pretrained weights")
            except Exception as e:
                print(f"  [WARNING] Could not load Resampler weights: {e}")
                print("  Using randomly initialized Resampler")
        else:
            print("  [WARNING] No image_proj weights found, using random initialization")
        
        # Setup IP-Adapter attention processors
        print("Setting up IP-Adapter attention processors...")
        attn_procs = {}
        num_tokens = 16  # Match Resampler num_queries
        
        for name in pipe.unet.attn_processors.keys():
            cross_attention_dim = None if name.endswith("attn1.processor") else pipe.unet.config.cross_attention_dim
            
            if name.startswith("mid_block"):
                hidden_size = pipe.unet.config.block_out_channels[-1]
            elif name.startswith("up_blocks"):
                block_id = int(name[len("up_blocks.")])
                hidden_size = list(reversed(pipe.unet.config.block_out_channels))[block_id]
            elif name.startswith("down_blocks"):
                block_id = int(name[len("down_blocks.")])
                hidden_size = pipe.unet.config.block_out_channels[block_id]
            else:
                hidden_size = pipe.unet.config.block_out_channels[-1]
            
            if cross_attention_dim is None:
                attn_procs[name] = AttnProcessor2_0()
            else:
                attn_procs[name] = IPAttnProcessor2_0(
                    hidden_size=hidden_size,
                    cross_attention_dim=cross_attention_dim,
                    scale=1.0,
                    num_tokens=num_tokens
                ).to(device, dtype=dtype)
        
        # Set attention processors
        pipe.unet.set_attn_processor(attn_procs)
        
        # Load IP-Adapter weights into attention processors
        if ip_adapter_state_dict:
            try:
                ip_layers = torch.nn.ModuleList(pipe.unet.attn_processors.values())
                ip_layers.load_state_dict(ip_adapter_state_dict, strict=False)
                print("  [OK] IP-Adapter attention weights loaded")
            except Exception as e:
                print(f"  [WARNING] Could not load IP-Adapter weights: {e}")
        else:
            print("  [WARNING] No ip_adapter weights found")
        
        # Store image encoder and projection model
        pipe.image_encoder = image_encoder
        
        print("  [OK] IP-Adapter fully loaded with InstantID architecture")
        print(f"  - Resampler: 4 layers, 20 heads, 16 output tokens")
        print(f"  - Face embeddings: 512D → 16x2048D")
        
        return image_proj_model, True
        
    except Exception as e:
        print(f"  [ERROR] Could not setup IP-Adapter: {e}")
        import traceback
        traceback.print_exc()
        return None, False


def setup_compel(pipe):
    """Setup Compel for better SDXL prompt handling."""
    print("Setting up Compel for enhanced prompt processing...")
    try:
        compel = Compel(
            tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
            text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
            returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
            requires_pooled=[False, True]
        )
        print("  [OK] Compel loaded successfully")
        return compel, True
    except Exception as e:
        print(f"  [WARNING] Compel not available: {e}")
        return None, False


def setup_scheduler(pipe):
    """Setup LCM scheduler."""
    print("Setting up LCM scheduler...")
    pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
    print("  [OK] LCM scheduler configured")


def optimize_pipeline(pipe):
    """Apply optimizations to pipeline."""
    # Try to enable xformers
    if device == "cuda":
        try:
            pipe.enable_xformers_memory_efficient_attention()
            print("  [OK] xformers enabled")
        except Exception as e:
            print(f"  [INFO] xformers not available: {e}")


def load_caption_model():
    """
    Load caption model with proper error handling.
    Tries multiple models in order of quality.
    """
    print("Loading caption model...")
    
    # Try GIT-Large first (good balance of quality and compatibility)
    try:
        from transformers import AutoProcessor, AutoModelForCausalLM
        
        print("  Attempting GIT-Large (recommended)...")
        caption_processor = AutoProcessor.from_pretrained("microsoft/git-large-coco")
        caption_model = AutoModelForCausalLM.from_pretrained(
            "microsoft/git-large-coco",
            torch_dtype=dtype
        ).to(device)
        print("  [OK] GIT-Large model loaded (produces detailed captions)")
        return caption_processor, caption_model, True, 'git'
    except Exception as e1:
        print(f"  [INFO] GIT-Large not available: {e1}")
        
        # Try BLIP base as fallback
        try:
            from transformers import BlipProcessor, BlipForConditionalGeneration
            
            print("  Attempting BLIP base (fallback)...")
            caption_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
            caption_model = BlipForConditionalGeneration.from_pretrained(
                "Salesforce/blip-image-captioning-base",
                torch_dtype=dtype
            ).to(device)
            print("  [OK] BLIP base model loaded (standard captions)")
            return caption_processor, caption_model, True, 'blip'
        except Exception as e2:
            print(f"  [WARNING] Caption models not available: {e2}")
            print("  Caption generation will be disabled")
            return None, None, False, 'none'


def set_clip_skip(pipe):
    """Set CLIP skip value."""
    if hasattr(pipe, 'text_encoder'):
        print(f"  [OK] CLIP skip set to {CLIP_SKIP}")


print("[OK] Model loading functions ready")