In this paper, we introduce a denoising diffusion model that benefits from quantum compute techniques for generative tasks within high energy physics. The fully quantum diffusion model leverages random unitary matrices in the forward process and incorporates a variational quantum circuit within the U-Net architecture. This approach aims to address the computational challenges and resource demands typically associated with generative models, particularly when dealing with extensive datasets. We evaluate our model on the quark and gluon jets dataset from the Large Hadron Collider. The results demonstrate that the fully quantum and hybrid models are competitive with a similar classical model for jet generation, highlighting the potential of using quantum techniques for machine learning problems.