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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
The official implementation of paper "ColorFlow: Retrieval-Augmented Image Sequence Colorization". ColorFlow：基于检索增强的图像序列上色
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# 🎨 ColorFlow *Retrieval-Augmented Image Sequence Colorization* **Authors:** Junhao Zhuang, Xuan Ju, Zhaoyang Zhang, Yong Liu, Shiyi Zhang, Chun Yuan, Ying Shan <a href='https://zhuang2002.github.io/ColorFlow/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> <a href='https://huggingface.co/spaces/TencentARC/ColorFlow'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue'></a> <a href="https://arxiv.org/abs/2412.11815"><img src="https://img.shields.io/badge/arXiv-2412.11815-b31b1b.svg"></a> <a href="https://huggingface.co/TencentARC/ColorFlow"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue"></a> **Your star means a lot for us to develop this project!** :star: <img src='https://zhuang2002.github.io/ColorFlow/fig/teaser.png'/> ### 🌟 Abstract Automatic black-and-white image sequence colorization while preserving character and object identity (ID) is a complex task with significant market demand, such as in cartoon or comic series colorization. Despite advancements in visual colorization using large-scale generative models like diffusion models, challenges with controllability and identity consistency persist, making current solutions unsuitable for industrial application. To address this, we propose **ColorFlow**, a three-stage diffusion-based framework tailored for image sequence colorization in industrial applications. Unlike existing methods that require per-ID finetuning or explicit ID embedding extraction, we propose a novel robust and generalizable **Retrieval Augmented Colorization** pipeline for colorizing images with relevant color references. Our pipeline also features a dual-branch design: one branch for color identity extraction and the other for colorization, leveraging the strengths of diffusion models. We utilize the self-attention mechanism in diffusion models for strong in-context learning and color identity matching. To evaluate our model, we introduce **ColorFlow-Bench**, a comprehensive benchmark for reference-based colorization. Results show that ColorFlow outperforms existing models across multiple metrics, setting a new standard in sequential image colorization and potentially benefiting the art industry. ### 📰 News - **Update Date:** December 23, 2024 - We have released the weights for the Sketch_Shading model, along with updates to the related code and demo. You can access the model weights in our [Hugging Face model repository](https://huggingface.co/TencentARC/ColorFlow) and explore the updated demo [here](https://huggingface.co/spaces/TencentARC/ColorFlow). 🎉🔥 - **Release Date:** December 17, 2024 - The inference code and model weights have also been released! 🎉 ### 📋 TODO - ✅ Release inference code and model weights - ⬜️ Release training code ### 🚀 Getting Started Follow these steps to set up and run ColorFlow on your local machine: - **Clone the Repository** Download the code from our GitHub repository: ```bash git clone https://github.com/TencentARC/ColorFlow cd ColorFlow ``` - **Set Up the Python Environment** Ensure you have Anaconda or Miniconda installed, then create and activate a Python environment and install required dependencies: ```bash conda create -n colorflow python=3.8.5 conda activate colorflow pip install -r requirements.txt ``` - **Run the Application** You can launch the Gradio interface for ColorFlow by running the following command: ```bash python app.py ``` - **Access ColorFlow in Your Browser** Open your browser and go to `http://localhost:7860`. If you're running the app on a remote server, replace `localhost` with your server's IP address or domain name. To use a custom port, update the `server_port` parameter in the `demo.launch()` function of app.py. ### 🎉 Demo You can [try the demo](https://huggingface.co/spaces/TencentARC/ColorFlow) of ColorFlow on Hugging Face Space. ### 🛠️ Method The overview of ColorFlo
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f2054e6d6f24d6a6, topic:computer-vision