process to prompt the generator to generate images progressively resembling the distribu- tion of real data [7, 8]. Moreover, the diffusion models stand out as the most revolutionary technologies that SketchyGAN [37] focuses on the sketch-to-image generation task and aims to achieve more diversity and real- ism. Currently, ControlNet [38] can control diffusion mod- els by adding extra conditions. The sketch-to-image mindsets and explore new design spaces. Simul- taneously, diffusion probabilistic models can generate real- istic architectural rendering images suitable for Virtual Re- ality (VR) or Augmented Reality (AR)