Facial analysis is an important domain in computer vision and has received extensive research attention. For numerous downstream tasks with different input/output formats and modalities, existing methods usually design task-specific architectures and train them using face datasets collected in the particular task domain. In this work, we proposed a single model, Talk2Face, to simultaneously tackle a large number of face generation and analysis tasks, e.g. text guided face synthesis, face captioning and age estimation. Specifically, we cast different tasks into a sequence-to-sequence format with the same architecture, parameters and objectives. While text and facial images are tokenized to sequences, the annotation labels of faces for different tasks are also converted to natural languages for unified representation. We collect a set of 2.3M face-text pairs from available datasets across different tasks, to train the proposed model. Uniform templates are then designed to enable the model to perform different downstream tasks, according to the task context and target. Experiments on different tasks show that our model achieves better face generation and caption performances than SOTA approaches. On age estimation and multi-attribute classification, our model reaches competitive performance with those models specially designed and trained for these particular tasks. In practice, our model is much easier to be deployed to different facial analysis related tasks. Code and dataset will be available at https://github.com/ydli-ai/Talk2Face.