Generate human faces with a diffusion model on an Alif Ensemble E8
Introduction
Understand the pico-faces workflow
Set up the development tools and DevKit
Export the pico-faces model for Ethos-U85
Prepare the DevKit for debugging
Generate and validate faces on the DevKit
Next Steps
Generate human faces with a diffusion model on an Alif Ensemble E8
Who is this for?
This topic is for embedded and machine learning developers who want to deploy a Generative AI model on a microcontroller with Arm Ethos-U85 acceleration.
What will you learn?
After completing this Learning Path, you'll be able to perform the following tasks:
- Identify how ExecuTorch divides pico-faces generation between the Cortex-M55 and Ethos-U85 NPU.
- Export and fully delegate the pico-faces diffusion transformer and decoder to the Ethos-U85 NPU.
- Configure the Alif Ensemble E8 DevKit for loading and debugging the generated application.
- Generate human faces on the DevKit and validate NPU execution through the LCD and UART output.
Prerequisites
Before starting, you'll need the following prerequisites:
- Familiarity with Python, machine learning models, and embedded development
- An Alif Ensemble E8 DevKit with its display and a USB-C data cable
- A development computer running Linux, macOS, or Windows, with internet access and several gigabytes of free storage
- Git, plus Python 3.10 through 3.13 or uv to install a compatible Python interpreter
- An Arm account for the Keil MDK Community license and an Alif account for downloading SETOOLS
Summary
This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
You’ll export the pico-faces diffusion model with ExecuTorch and deploy it to the Alif Ensemble E8 DevKit. First, you’ll configure Keil Studio and the CMSIS toolchain, then delegate the diffusion transformer and decoder to the Arm Ethos-U85 NPU. Next, you’ll provision the Cortex-M55 boot and debug flow, build the application, and generate human faces. Finally, you’ll verify NPU execution through the DevKit LCD, UART output, and performance counters.