OneFormer is the first multi-task universal image segmentation framework based on transformers. OneFormer needs to be trained only once with a single universal architecture, a single model, and on a single dataset , to outperform existing frameworks across semantic, instance, and panoptic segmentation tasks! OneFormer is the first multi-task universal image segmentation framework based on transformers. OneFormer needs to be trained only once with a single universal architecture, a single model, and on a single dataset , to outperform existing frameworks across semantic, instance, and panoptic segmentation tasks. OneFormer uses a task-conditioned joint training strategy, uniformly sampling different ground truth domains (semantic instance, or panoptic) by deriving all labels from panoptic annotations to train its multi-task model. OneFormer uses a task token to condition the model on the task in focus, making our architecture task-guided for training, and task-dynamic for inference, all with a single model.