prod_model.py

from model import DesignModel
from PIL import Image
import numpy as np
from typing import List
import random
import time
import torch
from diffusers.pipelines.controlnet import StableDiffusionControlNetInpaintPipeline
from diffusers import ControlNetModel, UniPCMultistepScheduler, AutoPipelineForText2Image
from transformers import AutoImageProcessor, UperNetForSemanticSegmentation, AutoModelForDepthEstimation
import logging
import os
from datetime import datetime
import gc

# Set up logging
log_dir = "logs"
os.makedirs(log_dir, exist_ok=True)
log_file = os.path.join(log_dir, f"prod_model_{datetime.now().strftime('%Y%m%d')}.log")

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler(log_file),
        logging.StreamHandler()
    ]
)

class ProductionDesignModel(DesignModel):
    def __init__(self):
        """Initialize the production model with advanced architecture"""
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.dtype = torch.float16 if self.device == "cuda" else torch.float32
        
        # Setup logging
        logging.basicConfig(filename=f'logs/prod_model_{time.strftime("%Y%m%d")}.log',
                          level=logging.INFO,
                          format='%(asctime)s - %(levelname)s - %(message)s')
        
        self.seed = 323*111
        self.neg_prompt = "window, door, low resolution, banner, logo, watermark, text, deformed, blurry, out of focus, surreal, ugly, beginner"
        self.control_items = ["windowpane;window", "door;double;door"]
        self.additional_quality_suffix = "interior design, 4K, high resolution, photorealistic"
        
        try:
            logging.info(f"Initializing models on {self.device} with {self.dtype}")
            self._initialize_models()
            logging.info("Models initialized successfully")
        except Exception as e:
            logging.error(f"Error initializing models: {e}")
            raise

    def _initialize_models(self):
        """Initialize all required models and pipelines"""
        # Initialize ControlNet models
        self.controlnet_depth = ControlNetModel.from_pretrained(
            "controlnet_depth", torch_dtype=self.dtype, use_safetensors=True
        )
        self.controlnet_seg = ControlNetModel.from_pretrained(
            "own_controlnet", torch_dtype=self.dtype, use_safetensors=True
        )

        # Initialize main pipeline
        self.pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
            "SG161222/Realistic_Vision_V5.1_noVAE",
            controlnet=[self.controlnet_depth, self.controlnet_seg],
            safety_checker=None,
            torch_dtype=self.dtype
        )

        # Setup IP-Adapter
        self.pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models",
                                weight_name="ip-adapter_sd15.bin")
        self.pipe.set_ip_adapter_scale(0.4)
        self.pipe.scheduler = UniPCMultistepScheduler.from_config(self.pipe.scheduler.config)
        self.pipe = self.pipe.to(self.device)

        # Initialize guide pipeline
        self.guide_pipe = AutoPipelineForText2Image.from_pretrained(
            "segmind/SSD-1B",
            torch_dtype=self.dtype,
            use_safetensors=True,
            variant="fp16"
        ).to(self.device)

        # Initialize segmentation and depth models
        self.seg_processor, self.seg_model = self._init_segmentation()
        self.depth_processor, self.depth_model = self._init_depth()
        self.depth_model = self.depth_model.to(self.device)

    def _init_segmentation(self):
        """Initialize segmentation models"""
        processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
        model = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")
        return processor, model

    def _init_depth(self):
        """Initialize depth estimation models"""
        processor = AutoImageProcessor.from_pretrained(
            "LiheYoung/depth-anything-large-hf",
            torch_dtype=self.dtype
        )
        model = AutoModelForDepthEstimation.from_pretrained(
            "LiheYoung/depth-anything-large-hf",
            torch_dtype=self.dtype
        )
        return processor, model

    def _get_depth_map(self, image: Image.Image) -> Image.Image:
        """Generate depth map for input image"""
        image_to_depth = self.depth_processor(images=image, return_tensors="pt").to(self.device)
        with torch.inference_mode():
            depth_map = self.depth_model(**image_to_depth).predicted_depth

        width, height = image.size
        depth_map = torch.nn.functional.interpolate(
            depth_map.unsqueeze(1).float(),
            size=(height, width),
            mode="bicubic",
            align_corners=False,
        )
        depth_min = torch.amin(depth_map, dim=[1, 2, 3], keepdim=True)
        depth_max = torch.amax(depth_map, dim=[1, 2, 3], keepdim=True)
        depth_map = (depth_map - depth_min) / (depth_max - depth_min)
        image = torch.cat([depth_map] * 3, dim=1)

        image = image.permute(0, 2, 3, 1).cpu().numpy()[0]
        return Image.fromarray((image * 255.0).clip(0, 255).astype(np.uint8))

    def _segment_image(self, image: Image.Image) -> Image.Image:
        """Generate segmentation map for input image"""
        pixel_values = self.seg_processor(image, return_tensors="pt").pixel_values
        with torch.inference_mode():
            outputs = self.seg_model(pixel_values)

        seg = self.seg_processor.post_process_semantic_segmentation(
            outputs, target_sizes=[image.size[::-1]])[0]
        color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
        
        # You'll need to implement the palette mapping here
        # This is a placeholder - you should implement proper color mapping
        for label in range(seg.max() + 1):
            color_seg[seg == label, :] = [label * 30 % 255] * 3
            
        return Image.fromarray(color_seg).convert('RGB')

    def _resize_image(self, image: Image.Image, target_size: int) -> Image.Image:
        """Resize image while maintaining aspect ratio"""
        width, height = image.size
        if width > height:
            new_width = target_size
            new_height = int(height * (target_size / width))
        else:
            new_height = target_size
            new_width = int(width * (target_size / height))
        return image.resize((new_width, new_height), Image.LANCZOS)

    def _flush(self):
        """Clear CUDA cache"""
        gc.collect()
        if torch.cuda.is_available():
            torch.cuda.empty_cache()

    def generate_design(self, image, num_variations=1, **kwargs):
        """Generate design variations using the model.
        
        Args:
            image: Input image (PIL Image, numpy array, or torch tensor)
            num_variations: Number of variations to generate
            **kwargs: Additional parameters like prompt, num_steps, guidance_scale, strength
        
        Returns:
            List of generated images
        """
        try:
            # Convert image to PIL Image if needed
            if isinstance(image, np.ndarray):
                image = Image.fromarray(image)
            elif isinstance(image, torch.Tensor):
                # Convert tensor to numpy then PIL
                image = Image.fromarray((image.cpu().numpy() * 255).astype(np.uint8))
            
            if not isinstance(image, Image.Image):
                raise ValueError(f"Unsupported image type: {type(image)}")
            
            # Ensure image is RGB
            if image.mode != "RGB":
                image = image.convert("RGB")
            
            # Get parameters
            prompt = kwargs.get('prompt', '')
            num_steps = int(kwargs.get('num_steps', 50))
            guidance_scale = float(kwargs.get('guidance_scale', 10.0))
            strength = float(kwargs.get('strength', 0.9))
            seed_param = kwargs.get('seed')
            
            # Handle seed
            base_seed = int(time.time()) if seed_param is None else int(seed_param)
            logging.info(f"Using base seed: {base_seed}")
            
            variations = []
            for i in range(num_variations):
                try:
                    # Generate distinct seed for each variation
                    seed = base_seed + i
                    generator = torch.Generator(device=self.device).manual_seed(seed)
                    
                    # Generate variation
                    output = self.pipe(
                        prompt=prompt,
                        image=image,
                        num_inference_steps=num_steps,
                        guidance_scale=guidance_scale,
                        strength=strength,
                        generator=generator,
                        negative_prompt=self.neg_prompt
                    ).images[0]
                    
                    variations.append(output)
                    logging.info(f"Successfully generated variation {i} with seed {seed}")
                    
                except Exception as e:
                    logging.error(f"Error generating variation {i}: {str(e)}")
                    continue
                
                finally:
                    # Clear CUDA cache after each variation
                    if torch.cuda.is_available():
                        torch.cuda.empty_cache()
            
            if not variations:
                logging.warning("No variations were generated successfully")
                return [image]  # Return original image if no variations generated
            
            return variations
            
        except Exception as e:
            logging.error(f"Error in generate_design: {str(e)}")
            return [image]  # Return original image on error

    def __del__(self):
        """Cleanup when the model is deleted"""
        self._flush()