We present MagicColor, a diffusion-based framework for multi-instance sketch colorization. The production of multi-instance 2D line art colorization adheres to an industry-standard workflow, which consists of three crucial stages: the design of line art characters, the coloring of individual objects, and the refinement process. The artists are required to repeat the process to color each instance one by one, which is inaccurate and inefficient. Meanwhile, current generative methods fail to solve this task due to the challenge of multi-instance pair data collection. To tackle these challenges, we incorporate three technical designs to ensure precise character detail transcription and achieve multi-instance sketch colorization in a single forward. Specifically, we first propose the self-play training strategy to solve the lack of training data. Then the instance guider is introduced to feed the color of the instance. To achieve accurate color matching, we present the fine-grained color matching with edge loss to enhance visual quality. Equipped with the proposed module, MagicColor enables automatically transforming sketches into vividly-colored animations in accurate consistency with multi-reference characters.Experiments on a self-collected benchmark demonstrate the superiority of our model over current solutions in terms of precise colorization. Our model could even automate the colorization process, such that users can easily create a color consistent image by simply providing reference images as well as the original sketch. Our codes will be available soon.