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"""
ColorizeNet model wrapper for image colorization
"""
import logging
import torch
import numpy as np
from PIL import Image
import cv2
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, StableDiffusionXLControlNetPipeline
from diffusers.utils import load_image
from transformers import pipeline
from huggingface_hub import hf_hub_download
logger = logging.getLogger(__name__)
class ColorizeModel:
"""Wrapper for ColorizeNet model"""
def __init__(self, model_id: str = "rsortino/ColorizeNet"):
"""
Initialize the ColorizeNet model
Args:
model_id: Hugging Face model ID for ColorizeNet
"""
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
try:
# Try loading as ControlNet with Stable Diffusion
logger.info("Attempting to load ColorizeNet as ControlNet...")
try:
# Load ControlNet model
self.controlnet = ControlNetModel.from_pretrained(
model_id,
torch_dtype=self.dtype
)
# 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
)
logger.info("Loaded with SDXL base model")
except:
self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
controlnet=self.controlnet,
torch_dtype=self.dtype,
safety_checker=None,
requires_safety_checker=False
)
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))
# Fallback: try as image-to-image pipeline
logger.info("Trying to load as image-to-image pipeline...")
self.pipe = pipeline(
"image-to-image",
model=model_id,
device=0 if self.device == "cuda" else -1,
torch_dtype=self.dtype
)
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 = 20) -> Image.Image:
"""
Colorize a grayscale image
Args:
image: PIL Image (grayscale or color)
num_inference_steps: Number of inference steps
Returns:
Colorized PIL Image
"""
try:
# 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"
# 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=7.5,
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
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