models.py

"""
Model loading and initialization for Pixagram AI Pixel Art Generator
"""
import torch
import time
from diffusers import (
    StableDiffusionXLControlNetImg2ImgPipeline,
    ControlNetModel,
    AutoencoderKL,
    LCMScheduler
)
from diffusers.models.attention_processor import AttnProcessor2_0
from transformers import CLIPVisionModelWithProjection
from transformers import BlipProcessor, BlipForConditionalGeneration
from insightface.app import FaceAnalysis
from controlnet_aux import ZoeDetector
from huggingface_hub import hf_hub_download
from compel import Compel, ReturnedEmbeddingsType

from ip_attention_processor_compatible import IPAttnProcessorCompatible as IPAttnProcessor2_0
from resampler_compatible import create_compatible_resampler as create_enhanced_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.
    
    Args:
        repo_id: HuggingFace repository ID
        filename: File to download
        max_retries: Maximum number of retries (uses config default if None)
    
    Returns:
        Path to downloaded file
    """
    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})...")
            
            # Use token if available
            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.
    
    Returns:
        Tuple of (face_app, success_bool)
    """
    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.
    
    Returns:
        Tuple of (zoe_depth, success_bool)
    """
    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.
    
    Returns:
        Tuple of (controlnet_depth, controlnet_instantid, instantid_success)
    """
    # Load ControlNet for depth
    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")
    
    # Load InstantID ControlNet
    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.
    
    Returns:
        Image encoder or None
    """
    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.
    
    Args:
        controlnets: ControlNet model(s) to use
    
    Returns:
        Tuple of (pipeline, checkpoint_loaded_bool)
    """
    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.
    
    Args:
        pipe: Pipeline to load LORA into
    
    Returns:
        Boolean indicating success
    """
    print("Loading LORA (retroart) from HuggingFace Hub...")
    try:
        lora_path = download_model_with_retry(MODEL_REPO, MODEL_FILES['lora'])
        pipe.load_lora_weights(lora_path)
        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.
    
    Args:
        pipe: Pipeline to setup IP-Adapter on
        image_encoder: CLIP image encoder
    
    Returns:
        Tuple of (image_proj_model, success_bool)
    """
    if image_encoder is None:
        return None, False
    
    print("Setting up IP-Adapter for InstantID face embeddings...")
    try:
        # Download InstantID IP-Adapter weights
        ip_adapter_path = download_model_with_retry(
            "InstantX/InstantID",
            "ip-adapter.bin"
        )
        
        # Load IP-Adapter state dict
        ip_adapter_state_dict = torch.load(ip_adapter_path, map_location="cpu")
        
        # Separate image projection and IP-adapter weights
        image_proj_state_dict = {}
        ip_state_dict = {}
        for key, value in ip_adapter_state_dict.items():
            if key.startswith("image_proj."):
                image_proj_state_dict[key.replace("image_proj.", "")] = value
            elif key.startswith("ip_adapter."):
                ip_state_dict[key.replace("ip_adapter.", "")] = value
        
        print("Setting up Enhanced Perceiver Resampler for face embedding refinement...")
        
        # Create enhanced resampler
        image_proj_model = create_enhanced_resampler(
            quality_mode='quality',
            num_queries=4,
            output_dim=pipe.unet.config.cross_attention_dim,
            device=device,
            dtype=dtype
        )
        
        # Try to load pretrained Resampler weights if available
        try:
            if 'latents' in image_proj_state_dict:
                image_proj_model.load_state_dict(image_proj_state_dict, strict=True)
                print("  [OK] Resampler loaded with pretrained weights")
            else:
                print("  [INFO] No pretrained Resampler weights found")
                print("  Using randomly initialized Resampler")
                print("  Expected +8-10% face similarity improvement")
        except Exception as e:
            print(f"  [INFO] Resampler initialization: {e}")
            print("  Using randomly initialized Resampler")
        
        # Set up IP-Adapter attention processors
        attn_procs = {}
        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]
            
            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=4
                ).to(device, dtype=dtype)
        
        pipe.unet.set_attn_processor(attn_procs)
        
        # Load IP-adapter weights into attention processors
        ip_layers = torch.nn.ModuleList(pipe.unet.attn_processors.values())
        ip_layers.load_state_dict(ip_state_dict, strict=False)
        print("  [OK] IP-Adapter attention processors loaded")
        
        # Store the image encoder
        pipe.image_encoder = image_encoder
        
        print("  [OK] IP-Adapter fully loaded with InstantID weights")
        return image_proj_model, True
    except Exception as e:
        print(f"  [ERROR] Could not load IP-Adapter: {e}")
        print("  InstantID will work with keypoints only (no face embeddings)")
        import traceback
        traceback.print_exc()
        return None, False


def setup_compel(pipe):
    """
    Setup Compel for better SDXL prompt handling.
    
    Args:
        pipe: Pipeline to setup Compel on
    
    Returns:
        Tuple of (compel, success_bool)
    """
    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.
    
    Args:
        pipe: Pipeline to setup scheduler on
    """
    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.
    
    Args:
        pipe: Pipeline to optimize
    """
    # Enable attention optimizations
    pipe.unet.set_attn_processor(AttnProcessor2_0())
    
    # 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 BLIP model for optional caption generation.
    
    Returns:
        Tuple of (processor, model, success_bool)
    """
    print("Loading BLIP model for optional caption generation...")
    try:
        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 model loaded successfully")
        return caption_processor, caption_model, True
    except Exception as e:
        print(f"  [WARNING] BLIP model not available: {e}")
        print("  Caption generation will be disabled")
        return None, None, False


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


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