HAAR is a text-input based generation model capable of generating realistic 3D hairstyles. It takes text prompts as input and generates 3D hairstyle assets ready for various computer graphics animation applications. Unlike current AI-based generation models, HAAR utilizes 3D hair strands as the foundation representation. It automatically annotates the generated synthetic hairstyle model using a 2D visual question answering system. We propose a text-guided generation method that uses a conditional diffusion model to generate guided hair strands in the latent hairstyle UV space and reconstructs a dense hairstyle containing hundreds of thousands of hair strands using a latent upsampling process given a textual description. The generated hairstyles can be rendered using existing computer graphics techniques.