ColorFlow is a model designed for coloring image sequences, with a particular focus on preserving the identity of characters and objects during the coloring process. Utilizing contextual information, it accurately generates colors for different elements (such as hair and clothing) in black-and-white image sequences based on a pool of reference images, ensuring consistency with the color references. Through a three-phase diffusion model framework, ColorFlow introduces a novel retrieval-augmented coloring workflow that achieves relevant image coloring without the need for fine-tuning each identity or extracting explicit identity embeddings. Its main advantages include high-quality coloring effects while retaining identity information, which is of significant market value for coloring cartoon or comic series.