""" ColorizeNet model wrapper for image colorization """ import logging import os import torch import numpy as np from PIL import Image from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, StableDiffusionXLControlNetPipeline, StableDiffusionImg2ImgPipeline from diffusers.utils import load_image from transformers import pipeline from huggingface_hub import hf_hub_download from app.config import settings logger = logging.getLogger(__name__) class ColorizeModel: """Wrapper for ColorizeNet model""" def __init__(self, model_id: str | None = None): """ Initialize the ColorizeNet model Args: model_id: Hugging Face model ID for ColorizeNet """ if model_id is None: model_id = settings.MODEL_ID self.model_id = model_id self.device = "cuda" if torch.cuda.is_available() else "cpu" logger.info("Using device: %s", self.device) self.dtype = torch.float16 if self.device == "cuda" else torch.float32 self.hf_token = os.getenv("HF_TOKEN") or None # Configure writable cache to avoid permission issues on Spaces hf_cache_dir = os.getenv("HF_HOME", "./hf_cache") os.environ.setdefault("HF_HOME", hf_cache_dir) os.environ.setdefault("HUGGINGFACE_HUB_CACHE", hf_cache_dir) os.environ.setdefault("TRANSFORMERS_CACHE", hf_cache_dir) os.makedirs(hf_cache_dir, exist_ok=True) # Avoid libgomp warning by setting a valid integer os.environ.setdefault("OMP_NUM_THREADS", "1") try: # Decide whether to use ControlNet based on model_id wants_controlnet = "control" in self.model_id.lower() if wants_controlnet: # Try loading as ControlNet with Stable Diffusion logger.info("Attempting to load model as ControlNet: %s", self.model_id) try: # Load ControlNet model self.controlnet = ControlNetModel.from_pretrained( self.model_id, torch_dtype=self.dtype, token=self.hf_token, cache_dir=hf_cache_dir ) # Try SDXL first, fallback to SD 1.5 try: self.pipe = StableDiffusionXLControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", controlnet=self.controlnet, torch_dtype=self.dtype, safety_checker=None, requires_safety_checker=False, token=self.hf_token, cache_dir=hf_cache_dir ) logger.info("Loaded with SDXL base model") except Exception: self.pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, torch_dtype=self.dtype, safety_checker=None, requires_safety_checker=False, token=self.hf_token, cache_dir=hf_cache_dir ) logger.info("Loaded with SD 1.5 base model") self.pipe.to(self.device) # Enable memory efficient attention if available if hasattr(self.pipe, "enable_xformers_memory_efficient_attention"): try: self.pipe.enable_xformers_memory_efficient_attention() logger.info("XFormers memory efficient attention enabled") except Exception as e: logger.warning("Could not enable XFormers: %s", str(e)) logger.info("ColorizeNet model loaded successfully as ControlNet") self.model_type = "controlnet" except Exception as e: logger.warning("Failed to load as ControlNet: %s", str(e)) wants_controlnet = False # fall through to pipeline if not wants_controlnet: # Load as image-to-image pipeline logger.info("Trying to load as image-to-image pipeline...") self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained( self.model_id, torch_dtype=self.dtype, safety_checker=None, requires_safety_checker=False, use_safetensors=True, cache_dir=hf_cache_dir, token=self.hf_token ).to(self.device) logger.info("ColorizeNet model loaded using image-to-image pipeline") self.model_type = "pipeline" except Exception as e: logger.error("Failed to load ColorizeNet model: %s", str(e)) raise RuntimeError(f"Could not load ColorizeNet model: {str(e)}") def preprocess_image(self, image: Image.Image) -> Image.Image: """ Preprocess image for colorization Args: image: PIL Image Returns: Preprocessed PIL Image """ # Convert to grayscale if needed if image.mode != "L": # Convert to grayscale image = image.convert("L") # Convert back to RGB (grayscale image with 3 channels) image = image.convert("RGB") # Resize to standard size (512x512 for SD models) image = image.resize((512, 512), Image.Resampling.LANCZOS) return image def colorize(self, image: Image.Image, num_inference_steps: int = None) -> Image.Image: """ Colorize a grayscale image Args: image: PIL Image (grayscale or color) num_inference_steps: Number of inference steps (auto-adjusted for CPU/GPU) Returns: Colorized PIL Image """ try: # Optimize inference steps based on device if num_inference_steps is None: # Use fewer steps on CPU for faster processing num_inference_steps = 8 if self.device == "cpu" else 20 # Preprocess image control_image = self.preprocess_image(image) original_size = image.size # Prepare prompt for colorization prompt = "colorize this black and white image, high quality, detailed, vibrant colors, natural colors" negative_prompt = "black and white, grayscale, monochrome, low quality, blurry, desaturated" # Adjust guidance scale for CPU (lower = faster) guidance_scale = 5.0 if self.device == "cpu" else 7.5 # Generate colorized image based on model type if self.model_type == "controlnet": # Use ControlNet pipeline result = self.pipe( prompt=prompt, image=control_image, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, controlnet_conditioning_scale=1.0, generator=torch.Generator(device=self.device).manual_seed(42) ) if isinstance(result, dict) and "images" in result: colorized = result["images"][0] elif isinstance(result, list) and len(result) > 0: colorized = result[0] else: colorized = result else: # Use pipeline directly result = self.pipe( control_image, prompt=prompt, num_inference_steps=num_inference_steps ) if isinstance(result, dict) and "images" in result: colorized = result["images"][0] elif isinstance(result, list) and len(result) > 0: colorized = result[0] else: colorized = result # Ensure we have a PIL Image if not isinstance(colorized, Image.Image): if isinstance(colorized, np.ndarray): # Handle numpy array if colorized.dtype != np.uint8: colorized = (colorized * 255).astype(np.uint8) if len(colorized.shape) == 3 and colorized.shape[2] == 3: colorized = Image.fromarray(colorized, 'RGB') else: colorized = Image.fromarray(colorized) elif torch.is_tensor(colorized): # Handle torch tensor colorized = colorized.cpu().permute(1, 2, 0).numpy() colorized = (colorized * 255).astype(np.uint8) colorized = Image.fromarray(colorized, 'RGB') else: raise ValueError(f"Unexpected output type: {type(colorized)}") # Resize back to original size if original_size != (512, 512): colorized = colorized.resize(original_size, Image.Resampling.LANCZOS) return colorized except Exception as e: logger.error("Error during colorization: %s", str(e)) raise