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Colorize.cc vs DDColor

Colorize.cc

colorize.cc

DDColor

github.com

Side by side

FigureColorize.ccDDColor
Price from—Free
Free plan—Yes
Free trialYes—
Open source——
WebYes—
Linux—Yes
APIYesYes
Self-hosted—Yes
Plans published31
Spec score · AI Photo Colorization Software8.3 (#10)7.9 (#13)
PurposeColorize.cc is an online AI service for colorizing and restoring black-and-white photos.DDColor colorizes historical black-and-white photos and can recolor anime-game landscapes.
SupportThe maker lists [email protected] for questions.The repository directs users to online demos and documentation but does not state a dedicated support service.

Plans

Colorize.cc

  • Basic$12, paid once
  • Standard$49, paid once
  • For studios$199, paid once

Colorize.cc pricing →

DDColor

  • DDColor open-source softwareFree

DDColor pricing →

In their own words

Colorize.cc

Colorize.cc is a cloud-based photo service for adding color to black-and-white images and restoring damaged photographs. It can address scratches, stains, tears, noise and blur while improving detail. Premium colorization supports output up to 4K. In V8 Studio, users can work on selected areas, move colors, add or remove details, and rerun edits. The dashboard supports batch colorization and restoration, result management, and access to API documentation. A free preview is available without signup in simplified quality; the upload preview accepts JPG and PNG files up to 5 MB. The FAQ lists JPG, JPEG and PNG as supported formats. Paid plans are one-time payments: Basic is $12, paid once for 50 photo processing, Standard is $49, paid once for 250, and For studios is $199, paid once for 1,250. Each includes two restoration algorithms, two resolution enhancement algorithms, colorization up to 4K, photo animation and AI Portrait. Colorize.cc says uploaded photos are stored securely in the cloud and accessible only to the user.

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DDColor

DDColor is a free model for adding color to historical black-and-white photos and recoloring anime-game landscapes. It uses multi-scale visual features to optimize learnable color tokens for automatic colorization. The repository offers paper, ModelScope, artistic and tiny pretrained variants; the maker recommends ModelScope for images outside ImageNet and identifies tiny as the lightest option. Local inference scripts and a runnable Gradio demo are documented, alongside inference guidance for Hugging Face Hub and ModelScope. The repository also links to Replicate demos and an API. Users can train with ImageNet or a custom dataset, and the documentation covers ONNX export with an example using the tiny model. Requirements are Python 3.7 or later and PyTorch 1.7 or later. The repository includes an Apache License, Version 2.0, and lists no paid plans or product usage limits. One stated limitation is that colorfulness loss can result in unattractive color blocks, including red artifacts.

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