Face image generation requires high realism and controllability. This study proposes an algorithm for face image generation that is jointly controlled by text and facial key points. The text constrains the generation of faces at a semantic level, while facial key points enable the model to control the generation of facial features, expressions, and details based on given facial information. The proposed algorithm improves the existing diffusion model and introduces additional components: text processing models (CM), keypoint control networks (KCN), and autoencoder networks (ACN). Specifically, the diffusion model is a noise inference algorithm based on the diffusion theory; CM is designed based on an attention mechanism to encode and store text information; KCN receives the location information of key points, enhancing the controllability of face generation; ACN alleviates the generation pressure of the diffusion model and reduces the time required to generate samples. In addition, to adapt to generating face images, this research constructs a dataset containing 30000 face images. In the proposed algorithm, given prerequisite text and a facial keypoint image, the model extracts feature information and keypoint information from the text, generating a highly realistic and controllable target face image. Compared with mainstream methods, the proposed algorithm improves the FID index by about 5%–23% and the IS index by about 3%–14%, which proves its superiority.