Colorizer deployment
Use the release's inference wrapper or its ONNX graph to obtain the complete
improvement. The .safetensors file contains the learned colorizer; guided
decoding is implemented by spatial.py and is included in the ONNX graph.
Python / PyTorch
Run from the extracted release directory:
python -m pip install -r requirements.txt
python inference.py --model . --output-dir colorized photo.jpg
The default guided radius is 8 at the model's input resolution. Use
--guided-radius 0 for raw predictions or --guided-radius 16 for stronger
smoothing. Stronger smoothing can remove legitimate small color details.
--flip-tta --guided-radius 4 is an optional two-pass mode. The released
ONNX graph is the single-pass radius-8 mode.
from PIL import Image
from model import load_model
from inference import colorize
model = load_model('.')
result = colorize(model, Image.open('photo.jpg'))
result.save('colorized.png')
The wrapper handles EXIF orientation, preserves aspect ratio, bounds the longest network-input side to 256 pixels, upsamples chroma to the original oriented image dimensions and combines it with the original Lab luminance. Final RGB conversion can clip colors outside the display gamut. Very large inputs still require memory for full-resolution color conversion. There is no video temporal-consistency guarantee.
ONNX without PyTorch
python -m pip install -r requirements-onnx.txt
python colorize_onnx.py --model colorizer.onnx --output-dir colorized photo.jpg
Input name: luminance, float32, shape N x 1 x H x W, values L*/50 - 1.
Output name: chroma, float32, shape N x 2 x H x W, Lab a and b values.
Height and width must each be at least 8. Dynamic shapes and batches are
supported. Prefer a longest input side of 256 to match the evaluated
operating point. Ordinary RGB values are not valid graph inputs.
The graph includes temperature-0.38 decoding and radius-8 guided filtering
with epsilon 0.001. It does not include file loading, EXIF handling, Lab
conversion, aspect-ratio resizing or final chroma upsampling; these are
implemented in colorize_onnx.py. The ONNX wrapper uses Pillow resizing,
whereas the PyTorch wrapper uses PyTorch interpolation. Their image-level
comparison is recorded in the release checks.
Publish to main
Authenticate normally on your own computer with repository write access:
hf auth login
python upload_main.py --folder . --repo User-2468/mini-unet-colorizer
This performs one upload to main with an optimistic-concurrency guard.
It also verifies that stable retains its pre-upload revision. The app can
keep using stable until you choose to switch it to the tested release.
The current chat connection has Jobs/read access but no repository write
scope; the downloadable release is the publication fallback.
Deployment limits
This is a measured app-testing candidate. Semantic color mistakes remain, and smoothing cannot infer an object's unknown original color. Review the included failure examples on your app's real input photos before describing it as generally production-ready. Browser/mobile performance and real-time video have not been validated.