DDColor vs ImageColorizer

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.

ImageColorizer provides AI tools to colorize, restore, enhance, retouch and repair old photos. Its colorizer adds color to black-and-white images, including skin tones, clothing, scenery and backgrounds. Restoration tools target contrast, clarity and facial detail in faded or blurry photographs, while repair tools reduce visible damage such as scratches, tears, stains, creases and water spots. Portrait retouching can improve facial clarity, reduce noise and smooth minor imperfections. The service supports JPG, PNG and WEBP, and is listed for web, Windows, Mac, iOS, iPad and Android. The free plan includes 50 credits per month and ads. Starter costs $6.90 a month, Premium costs $9.90 a month and Business costs $19.90 a month. Processing or downloading an image costs one credit, and subscription credits can roll over. The privacy policy says cloud-processed source and result images are deleted automatically within two hours. Paid users' photos should be under 100MB and 4000px on one side.