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Colourise vs DDColor

Colourise

colourise.com

DDColor

github.com

Side by side

FigureColouriseDDColor
Price from$5/moFree
Free plan—Yes
Free trialYes—
Open source——
WebYes—
Linux—Yes
API—Yes
Self-hosted—Yes
Plans published31
Spec score · AI Photo Colorization Software8.0 (#12)7.9 (#13)
SupportThe company says it usually replies to support inquiries within one business day.The repository directs users to online demos and documentation but does not state a dedicated support service.

Plans

Colourise

  • 1-Month Plan$39.99 a month
  • 1-Year Plan$59.99 a year
  • Lifetime Plan$74.99, paid once

Colourise pricing →

DDColor

  • DDColor open-source softwareFree

DDColor pricing →

In their own words

Colourise

Colourise is a browser-based AI photo studio for colorizing monochrome images and removing image backgrounds. Its colorization tool analyzes a black-and-white photo and applies lifelike, historically accurate color with one click. Background removal isolates subjects for product shots, profile pictures, and composites. Batch processing can apply enhancements, colorization, or background removal to entire folders at once. The processing tools accept JPG and PNG uploads up to 10MB. A free tier for occasional personal use has a daily quota; paid plans include unlimited processing, all AI tools, high-resolution output, and lifetime free updates. Listed prices are $39.99 a month, $59.99 a year, or $74.99, paid once. The Lifetime plan includes commercial use for client work, advertising, merchandise, and other revenue-generating activities. Colourise says uploads are encrypted in transit, are not used to train AI models, and are deleted after processing; its terms state they are kept only long enough to process and return results, then removed. Support usually replies within one business day. The service does not promise continuous availability, and maintenance, capacity limits, or outages may interrupt access.

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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.

Visit github.com