Description

I would like to propose adding a new community notebook that demonstrates how to generate photorealistic biological textures (specifically human irises) on standard CPUs using Latent Consistency Models (LCM) optimized with OpenVINO.

This workflow showcases how to achieve ~4x faster inference speeds compared to standard Stable Diffusion pipelines on edge hardware, making it a strong example for medical/biological generative use cases.

Scope of the Notebook

The notebook will demonstrate:

1. Pipeline Setup: Loading the SimianLuo/LCM_Dreamshaper_v7 model using OVStableDiffusionPipeline.
2. Optimization: leveraging optimum-intel to run the diffusion process purely on CPU.
3. Domain Application: Generating high-frequency textures (macro photography of eyes) which is often a stress test for image generation models.
4. Reproducibility: A self-contained workflow that runs seamlessly in Google Colab.

Motivation

While there are existing Stable Diffusion examples, there are fewer examples focusing on LCM (Latent Consistency Models) for high-speed CPU inference, particularly applied to specific scientific/biological domains. This notebook bridges that gap.

Existing Work

I have already implemented a working prototype of this pipeline in my repository:

• Repo: https://github.com/humbeaniket2006-max/Ocular_Core_Lite
• Live Demo: Colab Link

I would love to adapt this into the standard openvino_notebooks format and contribute it to the community. Please let me know if this aligns with the roadmap!