model.py

import torch
import os
from config import Config

from diffusers import (
    StableDiffusionXLPipeline,
    LCMScheduler
)

from huggingface_hub import hf_hub_download

class ModelHandler:
    def __init__(self):
        self.pipeline = None

    def load_models(self):
        # 1. Load SDXL Text-to-Image Pipeline
        print(f"Loading SDXL Pipeline ({Config.CHECKPOINT_FILENAME})...")
        
        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
            )
        
        print(f"Loading pipeline from local file: {checkpoint_local_path}")
        # Use standard SDXL Text2Image pipeline
        self.pipeline = StableDiffusionXLPipeline.from_single_file(
            checkpoint_local_path,
            torch_dtype=Config.DTYPE,
            use_safetensors=True
        )
        
        self.pipeline.to(Config.DEVICE)

        # 2. Enable xFormers
        try:
            self.pipeline.enable_xformers_memory_efficient_attention()
            print("  [OK] xFormers memory efficient attention enabled.")
        except Exception as e:
            print(f"  [WARNING] Failed to enable xFormers: {e}")

        # 3. Set Scheduler (LCM)
        print("Configuring LCMScheduler...")
        scheduler_config = self.pipeline.scheduler.config
        # Disable clipping to prevent NaN artifacts with LCM
        scheduler_config['clip_sample'] = False 
        self.pipeline.scheduler = LCMScheduler.from_config(
            scheduler_config,
            timestep_spacing="trailing",
            beta_schedule="scaled_linear"
        )
        print("  [OK] LCMScheduler loaded (clip_sample=False).")

        # 4. Load LoRA
        print("Loading LoRA weights...")
        self.pipeline.load_lora_weights(Config.REPO_ID, weight_name=Config.LORA_FILENAME)
        
        print(f"Fusing LoRA with scale {Config.LORA_STRENGTH}...")
        self.pipeline.fuse_lora(lora_scale=Config.LORA_STRENGTH)
        print("  [OK] LoRA fused.")
        
        print("--- All models loaded successfully ---")