Added support for Stable Diffusion LCM model

examples/openvino/stable_diffusion/README.md

@@ -0,0 +1,48 @@
+# Stable Diffusion LCM with OpenVINO Backend
+
+This example demonstrates how to run Latent Consistency Models (LCM) for fast text-to-image generation on Intel hardware using ExecuTorch with the OpenVINO backend.
+
+## Overview
+
+Latent Consistency Models (LCMs) are optimized diffusion models that generate high-quality images in just 4-8 steps, compared to 25-50 steps required by traditional Stable Diffusion models. This implementation:
+
+## Environment Setup
+Follow the [instructions](../../../backends/openvino/README.md) of **Prerequisites** and **Setup** in `backends/openvino/README.md` to set up the OpenVINO backend.
+
+### Install dependencies
+```bash
+pip install -r requirements.txt
+```
+
+## Export the Model
+
+Export the LCM model:
+
+```bash
+python export_lcm.py \
+    --model_id SimianLuo/LCM_Dreamshaper_v7 \
+    --output_dir ./lcm_models \
+    --device CPU \
+    --dtype fp16
+```
+This will create three files in `./lcm_models/`:
+- `text_encoder.pte`
+- `unet.pte`
+- `vae_decoder.pte`
+
+### Generate Images
+
+Run inference with the exported model:
+
+```bash
+python openvino_lcm.py \
+    --models_dir ./lcm_models \
+    --prompt "a beautiful sunset over mountains" \
+    --steps 4 \
+    --dtype fp16
+```
+## Supported Models
+
+This implementation supports LCM-based Stable Diffusion models:
+- **SimianLuo/LCM_Dreamshaper_v7**
+- **latent-consistency/lcm-sdxl**