ViBT, or Vision Bridge Transformer, introduces a new approach to image and video translation by focusing on data-to-data trajectories. This method contrasts with traditional noise-to-data diffusion techniques, aiming for more efficient processing. The authors developed two model variants: a 20 billion parameter version and a smaller 1.3 billion parameter model, both designed to handle large-scale tasks in image and video processing. Training these models involves a novel variance-stabilized velocity-matching objective, which enhances the optimization of large models. This technique helps to stabilize the training process, making it more robust. ViBT also boasts improved inference times. By removing conditional tokens from the model, it achieves up to four times faster performance compared to traditional token-heavy baselines. The article details specific applications, including image instruction-based editing and stylization, video stylization, colorization, and frame interpolation. For image tasks, the models are trained on a dataset called Qwen-Image-Editing, while video tasks utilize the Wan2.1 1.3B dataset. Currently, the training code is still in development, and comprehensive instructions will be provided once finalized. The setup process for users involves creating a conda environment and installing necessary requirements, ensuring accessibility for those interested in implementing or experimenting with ViBT.