5. Multimodal Text-Based Image Application

Note: Due to memory limitations, Raspberry Pi 1GB cannot run this case.

Since Ollama doesn't support text-to-image, we need to use other tools to implement local text-to-image functionality. Voice-controlled text-to-image functionality is currently unavailable, so the content of this article is the same as for the offline text-based multimodal text-to-image application.

1. Concept Introduction

1.1 What is Text-to-Image?

Text-to-Image is an AI technology that automatically generates images based on text descriptions. Simply enter a text (e.g., "A Shiba Inu wearing sunglasses surfing on the beach"), and the AI ​​model will generate an image that matches the description based on semantic understanding, without any prior art or design experience.

Core Principles

1. Language Understanding: The AI ​​first parses your text and identifies keywords (e.g., "Shiba Inu," "sunglasses," "beach").

2. Image Generation: Based on training from massive amounts of image data, the AI ​​converts text into visual elements and combines them into a reasonable image.

3. Style Adaptation: You can specify a style (realistic, cartoon, watercolor, etc.), and the AI ​​will adjust the generated image.

1.2 What is FastSDCPU?

FastSDCPU is an open-source Stable Diffusion project optimized for CPU devices. Through algorithmic and engineering optimizations, it enables fast generation of high-quality images on standard computers without a GPU, significantly lowering the hardware barrier to entry for AI painting.

Core Features

1. Pure CPU Operation: No dedicated graphics card required, running directly on laptops, desktops, or edge devices.

2. High-Speed ​​Inference: Incorporating technologies such as Latent Consistency Models (LCM), image generation takes only 4–8 steps, significantly reducing latency.

3. Lightweight Deployment: Supports model quantization (such as INT8) and OpenVINO acceleration, reducing resource usage and improving responsiveness.

Applicable Scenarios

• Localized AI Creation Tool

• Intranet Image Generation Service

• Teaching Demonstrations and Prototyping

• Lightweight Deployment in Resource-Constrained Environments

2. Project Deployment

2.1 Deployment Environment

Note: If using our factory image, there is no need to deploy an environment and you can skip the deployment steps. Simply refer to <2.4 How to Start After Deployment Successfully> at the bottom to start the project. Then, return to <2.3 Using the Wenshengtu Function> to learn how to use it.

Open a terminal and execute the following code:

​x
# If git is not available on your system, run
sudo apt update
sudo apt install git -y
sudo apt install python3.10-venv -y

# Add environment variables
echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc
source ~/.bashrc

# Clone the project code
git clone https://github.com/rupeshs/fastsdcpu.git
cd fastsdcpu

# Create a virtual environment and install dependencies
python -m venv venv
source venv/bin/activate

# Install uv
curl -Ls https://astral.sh/uv/install.sh | sh(This step may not be successful if there is no proxy in China. If not, you can skip this step and execute the next command)

# If the curl command failed to install uv in the previous step, run these three instructions
wget https://mirrors.huaweicloud.com/astral/uv/0.8.4/uv-aarch64-unknown-linux-gnu -O ~/.local/bin/uv
chmod +x ~/.local/bin/uv
~/.local/bin/uv --version

chmod +x install.sh start-webui.sh
./install.sh --disable-gui

The installation is successful, press any key to exit:

2.2 LAN Access

Before starting, you need to modify a file to support LAN access. Otherwise, the webUI can only be accessed locally:

 Multimodal visual understanding and voice interaction
Multimodal Video Analysis Application