  In this paper, we introduce an innovative NLP model specifically fine-tuned
to determine the minimal number of denoising steps required for any given text
prompt. This advanced model serves as a real-time tool that recommends the
ideal denoise steps for generating high-quality images efficiently. It is
designed to work seamlessly with the Diffusion model, ensuring that images are
produced with superior quality in the shortest possible time. Although our
explanation focuses on the DDIM scheduler, the methodology is adaptable and can
be applied to various other schedulers like Euler, Euler Ancestral, Heun, DPM2
Karras, UniPC, and more. This model allows our customers to conserve costly
computing resources by executing the fewest necessary denoising steps to
achieve optimal quality in the produced images.
