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"""
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
# Check for Hugging Face token (try both environment variable names)
self.hf_token = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACE_HUB_TOKEN") or None
# Configure writable cache to avoid permission issues on Spaces
# Prefer DATA_DIR if available, otherwise fallback to /tmp
data_dir = os.getenv("DATA_DIR")
if not data_dir:
data_dir = "/tmp"
hf_cache_dir = os.path.join(data_dir, "hf_cache")
# Set cache environment variables
os.environ["HF_HOME"] = hf_cache_dir
os.environ["HUGGINGFACE_HUB_CACHE"] = hf_cache_dir
os.environ["TRANSFORMERS_CACHE"] = hf_cache_dir
try:
os.makedirs(hf_cache_dir, exist_ok=True)
logger.info("HF cache directory: %s", hf_cache_dir)
except Exception as e:
# Fallback to /tmp/hf_cache if DATA_DIR was set but not writable
tmp_cache_dir = os.path.join("/tmp", "hf_cache")
logger.warning("Failed to create cache in %s: %s, trying %s", data_dir, str(e), tmp_cache_dir)
hf_cache_dir = tmp_cache_dir
os.environ["HF_HOME"] = hf_cache_dir
os.environ["HUGGINGFACE_HUB_CACHE"] = hf_cache_dir
os.environ["TRANSFORMERS_CACHE"] = hf_cache_dir
try:
os.makedirs(hf_cache_dir, exist_ok=True)
logger.info("HF cache directory (tmp): %s", hf_cache_dir)
except Exception as e_tmp:
# Final fallback to user home (local dev)
logger.warning("Failed to create cache in /tmp: %s, trying user home", str(e_tmp))
default_home_cache = os.path.join(os.path.expanduser("~"), ".cache", "huggingface")
hf_cache_dir = default_home_cache
os.environ["HF_HOME"] = hf_cache_dir
os.environ["HUGGINGFACE_HUB_CACHE"] = hf_cache_dir
os.environ["TRANSFORMERS_CACHE"] = hf_cache_dir
try:
os.makedirs(hf_cache_dir, exist_ok=True)
logger.info("HF cache directory (home): %s", hf_cache_dir)
except Exception as e2:
logger.error("Failed to create cache directory: %s", str(e2))
raise RuntimeError(f"Cannot create Hugging Face cache directory: {str(e2)}")
else:
# Ensure environment variables reflect the final cache dir
os.environ["HF_HOME"] = hf_cache_dir
os.environ["HUGGINGFACE_HUB_CACHE"] = hf_cache_dir
os.environ["TRANSFORMERS_CACHE"] = hf_cache_dir
# 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(
prompt=prompt,
image=control_image,
negative_prompt=negative_prompt,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
strength=1.0
)
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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