models/image_generator.py

import torch
from diffusers import DiffusionPipeline
from PIL import Image
import numpy as np
from typing import Optional, List, Union
import gc

class OmniGenImageGenerator:
    """Image generation using OmniGen2 model"""
    
    def __init__(self, device: str = "cuda"):
        self.device = device if torch.cuda.is_available() else "cpu"
        self.pipeline = None
        self.model_id = "OmniGen2/OmniGen2"  # Placeholder - actual model path may differ
        
        # Generation parameters
        self.default_width = 512
        self.default_height = 512
        self.num_inference_steps = 30
        self.guidance_scale = 7.5
        
        # Memory optimization
        self.enable_attention_slicing = True
        self.enable_vae_slicing = True
        self.enable_cpu_offload = self.device == "cuda"
    
    def load_model(self):
        """Lazy load the image generation model"""
        if self.pipeline is None:
            try:
                # Determine torch dtype
                torch_dtype = torch.float16 if self.device == "cuda" else torch.float32
                
                # Load pipeline with optimizations
                self.pipeline = DiffusionPipeline.from_pretrained(
                    self.model_id,
                    torch_dtype=torch_dtype,
                    use_safetensors=True,
                    variant="fp16" if self.device == "cuda" else None
                )
                
                # Apply optimizations
                if self.device == "cuda":
                    if self.enable_cpu_offload:
                        self.pipeline.enable_sequential_cpu_offload()
                    else:
                        self.pipeline = self.pipeline.to(self.device)
                    
                    if self.enable_attention_slicing:
                        self.pipeline.enable_attention_slicing(1)
                    
                    if self.enable_vae_slicing:
                        self.pipeline.enable_vae_slicing()
                else:
                    self.pipeline = self.pipeline.to(self.device)
                
                # Compile for faster inference (if available)
                if hasattr(torch, 'compile') and self.device == "cuda":
                    try:
                        self.pipeline.unet = torch.compile(self.pipeline.unet, mode="reduce-overhead")
                    except:
                        pass  # Compilation is optional
                
            except Exception as e:
                print(f"Failed to load image generation model: {e}")
                # Try fallback to stable diffusion
                try:
                    self.model_id = "runwayml/stable-diffusion-v1-5"
                    self._load_fallback_model()
                except:
                    raise
    
    def _load_fallback_model(self):
        """Load fallback Stable Diffusion model"""
        from diffusers import StableDiffusionPipeline
        
        torch_dtype = torch.float16 if self.device == "cuda" else torch.float32
        
        self.pipeline = StableDiffusionPipeline.from_pretrained(
            self.model_id,
            torch_dtype=torch_dtype,
            use_safetensors=True
        )
        
        if self.device == "cuda" and self.enable_cpu_offload:
            self.pipeline.enable_sequential_cpu_offload()
        else:
            self.pipeline = self.pipeline.to(self.device)
    
    def generate(self, 
                prompt: str,
                reference_images: Optional[List[Union[str, Image.Image]]] = None,
                negative_prompt: Optional[str] = None,
                width: Optional[int] = None,
                height: Optional[int] = None,
                num_images: int = 1,
                seed: Optional[int] = None) -> Union[Image.Image, List[Image.Image]]:
        """Generate monster image from prompt"""
        
        try:
            # Load model if needed
            self.load_model()
            
            # Set dimensions
            width = width or self.default_width
            height = height or self.default_height
            
            # Ensure dimensions are multiples of 8
            width = (width // 8) * 8
            height = (height // 8) * 8
            
            # Enhance prompt for monster generation
            enhanced_prompt = self._enhance_prompt(prompt)
            
            # Default negative prompt for quality
            if negative_prompt is None:
                negative_prompt = (
                    "low quality, blurry, distorted, disfigured, "
                    "bad anatomy, wrong proportions, ugly, duplicate, "
                    "morbid, mutilated, extra limbs, malformed"
                )
            
            # Set seed for reproducibility
            generator = None
            if seed is not None:
                generator = torch.Generator(device=self.device).manual_seed(seed)
            
            # Generate images
            with torch.no_grad():
                if hasattr(self.pipeline, '__call__'):
                    # Standard diffusion pipeline
                    images = self.pipeline(
                        prompt=enhanced_prompt,
                        negative_prompt=negative_prompt,
                        width=width,
                        height=height,
                        num_inference_steps=self.num_inference_steps,
                        guidance_scale=self.guidance_scale,
                        num_images_per_prompt=num_images,
                        generator=generator
                    ).images
                else:
                    # OmniGen specific generation (if different API)
                    images = self._omnigen_generate(
                        enhanced_prompt, 
                        reference_images,
                        width, 
                        height, 
                        num_images
                    )
            
            # Clean up memory
            if self.device == "cuda":
                torch.cuda.empty_cache()
            
            # Return single image or list
            if num_images == 1:
                return images[0]
            return images
            
        except Exception as e:
            print(f"Image generation error: {e}")
            # Return fallback image
            return self._generate_fallback_image(width, height)
    
    def _enhance_prompt(self, base_prompt: str) -> str:
        """Enhance prompt for better monster generation"""
        enhancements = [
            "digital art",
            "creature design", 
            "game character",
            "detailed",
            "vibrant colors",
            "fantasy creature",
            "high quality",
            "professional artwork"
        ]
        
        # Combine base prompt with enhancements
        enhanced = f"{base_prompt}, {', '.join(enhancements)}"
        
        return enhanced
    
    def _omnigen_generate(self, prompt: str, reference_images: Optional[List], 
                         width: int, height: int, num_images: int) -> List[Image.Image]:
        """OmniGen specific generation with multimodal inputs"""
        # This would be implemented based on OmniGen's specific API
        # For now, fall back to standard generation
        return self.pipeline(
            prompt=prompt,
            width=width,
            height=height,
            num_images_per_prompt=num_images
        ).images
    
    def _generate_fallback_image(self, width: int, height: int) -> Image.Image:
        """Generate a fallback monster image"""
        # Create a simple procedural monster image
        img_array = np.zeros((height, width, 3), dtype=np.uint8)
        
        # Add some basic shapes and colors
        center_x, center_y = width // 2, height // 2
        radius = min(width, height) // 3
        
        # Create circular body
        y, x = np.ogrid[:height, :width]
        mask = (x - center_x)**2 + (y - center_y)**2 <= radius**2
        
        # Random monster color
        color = np.random.randint(50, 200, size=3)
        img_array[mask] = color
        
        # Add eyes
        eye_y = center_y - radius // 3
        eye_left_x = center_x - radius // 3
        eye_right_x = center_x + radius // 3
        eye_radius = radius // 8
        
        # Left eye
        eye_mask = (x - eye_left_x)**2 + (y - eye_y)**2 <= eye_radius**2
        img_array[eye_mask] = [255, 255, 255]
        
        # Right eye  
        eye_mask = (x - eye_right_x)**2 + (y - eye_y)**2 <= eye_radius**2
        img_array[eye_mask] = [255, 255, 255]
        
        # Convert to PIL Image
        return Image.fromarray(img_array)
    
    def edit_image(self, 
                  image: Union[str, Image.Image],
                  prompt: str,
                  mask: Optional[Union[str, Image.Image]] = None) -> Image.Image:
        """Edit existing image (for future monster customization)"""
        # This would implement image editing capabilities
        raise NotImplementedError("Image editing not yet implemented")
    
    def to(self, device: str):
        """Move pipeline to specified device"""
        self.device = device
        if self.pipeline:
            if device == "cuda" and self.enable_cpu_offload:
                self.pipeline.enable_sequential_cpu_offload()
            else:
                self.pipeline = self.pipeline.to(device)
    
    def __del__(self):
        """Cleanup when object is destroyed"""
        if self.pipeline:
            del self.pipeline
        gc.collect()
        if torch.cuda.is_available():
            torch.cuda.empty_cache()