Images generation using local LLM tools

Category: ARTIFICIAL INTELLIGENCE

 

Lets talk about how to set up a local AI server for graphics creation (think image generation, editing, and visual AI tasks) and need clear instructions for Windows and Linux. Let’s break it down step by step.

 

🎨 Best Tools for Local AI Graphics Creation

  • Stable Diffusion → The most popular open-source image generation model.

  • ComfyUI → Node-based workflow interface for Stable Diffusion (great for advanced users).

  • Automatic1111 WebUI → User-friendly web interface for Stable Diffusion with tons of extensions.

  • InvokeAI → Streamlined, production-ready toolkit for image generation.

  • Ollama + Diffusion models → Easy deployment if you want a unified AI server for text + graphics.

 

⚙️ Hardware Requirements

  • GPU strongly recommended (NVIDIA preferred for CUDA support).

  • VRAM:

    • 6–8 GB → Small models (SD 1.5, lightweight variants).

    • 12–24 GB → Larger models (SDXL, fine-tuned checkpoints).

  • RAM: 16 GB minimum.

  • Storage: 20–50 GB free (models + outputs).

 

🖥️ Setup on Windows

  1. Install Python & Git

    • Download Python 3.10+

    • Install Git

  2. Install GPU Drivers

    • NVIDIA: Install latest CUDA + cuDNN toolkit.

    • AMD: ROCm support (limited for Stable Diffusion).

  3. Clone Stable Diffusion WebUI (Automatic1111)

    bash
     
    git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui

  4. Run the WebUI

    bash
     
    webui-user.bat

    • First run will download dependencies.

    • Access via browser at http://127.0.0.1:7860.

  5. Add Models

    • Download .safetensors or .ckpt models (e.g., SDXL) from CivitAI or HuggingFace.

    • Place them in stable-diffusion-webui/models/Stable-diffusion.

 

🐧 Setup on Linux

  1. Install Dependencies

    bash
     
    sudo apt update && sudo apt install git python3 python3-venv

  2. Install GPU Drivers

    • NVIDIA: Install CUDA toolkit (sudo apt install nvidia-driver-535).

    • AMD: ROCm support (experimental).

  3. Clone Stable Diffusion WebUI

    bash
     
    git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
cd stable-diffusion-webui

  4. Run the WebUI

    bash
     
    ./webui.sh

    • Access via browser at http://127.0.0.1:7860.

  5. Add Models

    • Same as Windows: place .safetensors files in models/Stable-diffusion.

 

🔧 Optional Enhancements

  • ComfyUI → Install via GitHub for advanced workflows.

  • Extensions → Add ControlNet, LoRA, and upscale tools for better results.

  • Docker Setup → Run Stable Diffusion in containers for easier management.

 

✅ Summary

  • Windows → Install Python + Git → Clone WebUI → Run .bat → Add models.

  • Linux → Install dependencies → Clone WebUI → Run .sh → Add models.

  • Use Stable Diffusion WebUI for simplicity, ComfyUI if you want modular control.