DDColor
DDColor is a free image-colorization model for turning historical black-and-white photos into color and recoloring anime-game landscapes.
What DDColor is
DDColor is a free image-colorization model for turning historical black-and-white photos into color and recoloring anime-game landscapes. Its method uses multi-scale visual features to optimize learnable color tokens for automatic colorization. The repository provides paper, ModelScope, artistic, and tiny pretrained variants. Its maker recommends ModelScope for images outside ImageNet and describes tiny as the lightest option. Users can run local inference scripts or a Gradio demo; instructions also cover Hugging Face Hub and ModelScope, and the repository links to Replicate demos and an API. Training is supported with ImageNet or a custom dataset, and the repository documents ONNX export with an example using the tiny model. Listed requirements are Python 3.7 or later and PyTorch 1.7 or later. The repository includes an Apache License, Version 2.0. The maker cautions that colorfulness loss can produce unpleasant blocks of color, including red artifacts. No dedicated support service is stated.
Good
- Offers four pretrained model variants.
- Includes local scripts and a runnable Gradio demo.
- Supports custom-dataset training and ONNX export.
- API access is listed.
Watch out
- Colorfulness loss can create unpleasant color blocks.
- Requirements include Python 3.7+ and PyTorch 1.7+.
Who it suits
DDColor suits people who want to colorize monochrome images locally, through a demo, or through an API. It also suits users who want to train or export a model and can work with the listed Python and PyTorch requirements.
Verdict
DDColor offers several ways to run colorization, along with training and ONNX export options. Be aware that its maker warns of possible color-block artifacts, including red ones.
Plans and prices
From DDColor's own pricing page, checked 4 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| DDColor open-source software | Free | No paid plans or product usage limits stated |
The facts
Each one read from DDColor's own pages, with the date it was checked.
What it does
- Export
- The repository documents ONNX export, with an example using the tiny pretrained model.checked 4 Oct 2026
- Integrations
- Inference instructions cover Hugging Face Hub and ModelScope, and the repository links to Replicate demos and an API.checked 4 Oct 2026
- License
- The repository includes an Apache License, Version 2.0.checked 4 Oct 2026
- Local use
- The README documents local inference scripts and a runnable Gradio demo.checked 4 Oct 2026
- Method
- The model uses multi-scale visual features to optimize learnable color tokens for automatic image colorization.checked 4 Oct 2026
- Model guidance
- The maker recommends the ModelScope model by default for images outside ImageNet and describes the tiny model as the most lightweight version.checked 4 Oct 2026
- Model options
- The repository provides paper, ModelScope, artistic, and tiny pretrained model variants.checked 4 Oct 2026
- Notable limitation
- The maker cautions that colorfulness loss can produce unpleasant color blocks, including red artifacts.checked 4 Oct 2026
- Purpose
- DDColor colorizes historical black-and-white photos and can recolor anime-game landscapes.checked 4 Oct 2026
- Requirements
- The listed requirements are Python 3.7 or later and PyTorch 1.7 or later.checked 4 Oct 2026
- Security
- The repository page does not state security controls or compliance certifications.checked 4 Oct 2026
- Support
- The repository directs users to online demos and documentation but does not state a dedicated support service.checked 4 Oct 2026
- Training
- Users can train the model using ImageNet or a custom dataset, with training dependencies and pretrained ConvNeXt and InceptionV3 weights.checked 4 Oct 2026
The company
- Maker attribution
- The paper authors are listed as affiliated with DAMO Academy, Alibaba Group.checked 4 Oct 2026
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