Project Overview

https://github.com/al-swaiti/SDXL-LOCAL

using https://arxiv.org/abs/2309.05019 SA-Solver Scheduler

This project demonstrates how to generate images using Stable Diffusion XL with optional prompt enhancement via Gemini's API. The code includes configurable settings and the ability to save/load these settings from a JSON file.

Prerequisites

• Python 3.8 or higher (Note: For using torch.compile, Python 3.11 or lower is required)

• venv or conda for creating a virtual environment

Setup

Using venv

• Create a virtual environment:

  python -m venv civitai_env
  

Activate the virtual environment:

• On Windows:

  civitai_env\Scripts\activate
  

On macOS and Linux:

source civitai_env/bin/activate

 source civitai_env/bin/activate.fish

Install the required libraries:

pip install torch diffusers accelerate pillow google-generativeai transformers

Using conda

• Create a conda environment:

  conda create --name civitai_env python=3.11
  

Activate the conda environment:

conda activate civitai_env

Install the required libraries:

conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
pip install diffusers accelerate pillow google-generativeai transformers

Usage

• Clone the repository:

  git clone https://github.com/al-swaiti/SDXL-LOCAL
  cd SDXL-LOCAL
  

Run the script:

python main.py

Configuration

The script uses a configuration file (config.json) "auto created after first run"to store user inputs. If the file doesn't exist, default values are used. The script will prompt you to enter the following details:

• Model location

• Prompt

• Negative prompt

• Width and height of the image

• Number of inference steps

• CFG scale

• Seed

• Gemini API key (optional)

You can also provide these inputs through the command line. If no input is provided, the default values or the values from the configuration file will be used.

Enhancing Prompts with Gemini's API

To get professional prompt results, you can use the Gemini API. If you wish to use this feature, insert your Gemini API key when prompted or include it in the configuration file.

Notes

• If you want to use torch.compile, ensure you are using Python 3.11 or lower. and enable ( pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) ) line.

• The generated image will be saved with a timestamp-based filename.

• for low gpu disable ( pipe.to("cuda")) and enable (pipe.enable_sequential_cpu_offload())

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  try my model /model/130664