Iris recognition is one of the most accurate and reliable biometric technologies. However, due to the high collection costs and privacy of the iris, it is difficult to build a large-scale iris image database for training iris recognition models. This paper proposes a novel iris image generation algorithm which can produce numerous intra- and inter-class iris images. By using contrastive learning, we disentangle identity-related features (e.g., iris texture, left or right eye) and condition-variant features (e.g., pupil size, iris exposure ratio) in the generated images. This disentanglement facilitates identity control over synthetic iris images. Since the iris has the multi-degree-of-freedom (MDOF) topology and high-entropy texture, we specially design the dual-channel input protocol to separate the topology and texture of the iris, so that the generator can infer multi-condition iris images while maintaining the unique texture details. Extensive experiments demonstrate that the proposed approach achieves impressive performance in both image quality and identity representation.