models.py

"""
Models.py - Following examplewithface.py EXACTLY
NO MultiControlNetModel wrapper!
NO fuse_lora with scale!
"""
import torch
import time
import os
from diffusers import ControlNetModel, AutoencoderKL, LCMScheduler
from insightface.app import FaceAnalysis
from controlnet_aux import ZoeDetector
from huggingface_hub import hf_hub_download, snapshot_download
from safetensors.torch import load_file
from compel import Compel, ReturnedEmbeddingsType

from pipeline_stable_diffusion_xl_instantid_img2img import (
    StableDiffusionXLInstantIDImg2ImgPipeline,
    draw_kps
)

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):
    if max_retries is None:
        max_retries = DOWNLOAD_CONFIG['max_retries']
    
    for attempt in range(max_retries):
        try:
            kwargs = {"repo_type": "model"}
            if HUGGINGFACE_TOKEN:
                kwargs["token"] = HUGGINGFACE_TOKEN
            
            path = hf_hub_download(repo_id=repo_id, filename=filename, **kwargs)
            return path
        except Exception as e:
            if attempt < max_retries - 1:
                time.sleep(DOWNLOAD_CONFIG['retry_delay'])
            else:
                raise
    return None


def load_face_analysis():
    """examplewithface.py line 113"""
    print("Loading face analysis...")
    try:
        # Download antelopev2 model
        snapshot_download(
            repo_id="DIAMONIK7777/antelopev2",
            local_dir="/data/models/antelopev2"
        )
        
        # examplewithface.py line 113 pattern
        app = FaceAnalysis(name='antelopev2', root='/data', providers=['CPUExecutionProvider'])
        app.prepare(ctx_id=0, det_size=(640, 640))
        
        print("  [OK] Face analysis loaded")
        return app, True
    except Exception as e:
        print(f"  [ERROR] Face analysis failed: {e}")
        import traceback
        traceback.print_exc()
        return None, False


def load_depth_detector():
    """examplewithface.py line 151-155"""
    print("Loading Zoe Depth...")
    try:
        zoe = ZoeDetector.from_pretrained("lllyasviel/Annotators")
        zoe.to(device)  # examplewithface.py line 155
        print("  [OK] Zoe Depth loaded")
        return zoe, True
    except Exception as e:
        print(f"  [WARNING] Zoe unavailable: {e}")
        return None, False


def load_controlnets():
    """examplewithface.py lines 122-126"""
    print("Loading ControlNets...")
    
    # Load but don't move to device yet - pipe.to(device) will handle it
    identitynet = ControlNetModel.from_pretrained(
        "InstantX/InstantID",
        subfolder="ControlNetModel",
        torch_dtype=dtype
    )
    print("  [OK] InstantID ControlNet")
    
    zoedepthnet = ControlNetModel.from_pretrained(
        "diffusers/controlnet-zoe-depth-sdxl-1.0",
        torch_dtype=dtype
    )
    print("  [OK] Zoe Depth ControlNet")
    
    return identitynet, zoedepthnet


def load_sdxl_pipeline(controlnets):
    """
    examplewithface.py lines 128-145
    CRITICAL: Pass controlnets as LIST - NO MultiControlNetModel!
    """
    print("Loading pipeline...")
    
    # Load VAE (line 128)
    vae = AutoencoderKL.from_pretrained(
        "madebyollin/sdxl-vae-fp16-fix",
        torch_dtype=dtype
    )
    print("  [OK] VAE loaded")
    
    # Create pipeline (line 134) - controlnets as LIST!
    pipe = StableDiffusionXLInstantIDImg2ImgPipeline.from_pretrained(
        "frankjoshua/albedobaseXL_v21",
        vae=vae,
        controlnet=controlnets,  # ← LIST [identitynet, zoedepthnet] - NO WRAPPER!
        torch_dtype=dtype
    )
    print("  [OK] Pipeline created with direct controlnet list")
    
    # LCM scheduler
    pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
    print("  [OK] LCM scheduler")
    
    # IP-Adapter (line 139)
    ip_adapter_path = download_model_with_retry("InstantX/InstantID", "ip-adapter.bin")
    pipe.load_ip_adapter_instantid(ip_adapter_path)
    pipe.set_ip_adapter_scale(0.8)
    print("  [OK] IP-Adapter loaded")
    
    pipe = pipe.to(device)
    print("  [OK] Pipeline ready (following examplewithface.py EXACTLY)")
    return pipe, True


# Global LoRA state
lora_path_cached = None


def load_lora(pipe):
    """Load LoRA - store path for later use"""
    print("Loading LoRA...")
    global lora_path_cached
    
    try:
        lora_path = download_model_with_retry(MODEL_REPO, MODEL_FILES['lora'])
        lora_path_cached = lora_path
        print(f"  [OK] LoRA path stored")
        return True
    except Exception as e:
        print(f"  [WARNING] LoRA failed: {e}")
        return False


def fuse_lora_with_scale(pipe, lora_scale):
    """
    Modern approach: Load LoRA and let cross_attention_kwargs apply scale
    """
    global lora_path_cached
    
    if lora_path_cached is None:
        return False
    
    try:
        # Unload previous
        try:
            pipe.unload_lora_weights()
        except:
            pass
        
        # Load LoRA
        print(f"  [LORA] Loading with scale {lora_scale}...")
        pipe.load_lora_weights(lora_path_cached)
        print(f"  [OK] LoRA loaded (scale will be applied via cross_attention_kwargs)")
        
        return True
    except Exception as e:
        print(f"  [ERROR] LoRA failed: {e}")
        return False


def setup_compel(pipe):
    """examplewithface.py line 145"""
    print("Setting up Compel...")
    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 ready")
        return compel, True
    except Exception as e:
        print(f"  [WARNING] Compel unavailable: {e}")
        return None, False


def setup_scheduler(pipe):
    pass


def optimize_pipeline(pipe):
    if device == "cuda":
        try:
            pipe.enable_xformers_memory_efficient_attention()
            print("  [OK] xformers enabled")
        except:
            pass
    
    if hasattr(pipe, 'enable_vae_slicing'):
        pipe.enable_vae_slicing()
    if hasattr(pipe, 'enable_vae_tiling'):
        pipe.enable_vae_tiling()


def load_caption_model():
    print("Loading caption model...")
    try:
        from transformers import AutoProcessor, AutoModelForCausalLM
        processor = AutoProcessor.from_pretrained("microsoft/git-large-coco")
        model = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco", torch_dtype=dtype).to("cpu")
        print("  [OK] GIT-Large")
        return processor, model, True, 'git'
    except:
        try:
            from transformers import BlipProcessor, BlipForConditionalGeneration
            processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
            model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=dtype).to("cpu")
            print("  [OK] BLIP")
            return processor, model, True, 'blip'
        except:
            return None, None, False, 'none'


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


__all__ = ['draw_kps', 'fuse_lora_with_scale']

print("[OK] models.py ready - NO MultiControlNetModel, following examplewithface.py")