Easy Stable Diffusion Image Generation

sevsd is a Python package specifically designed to make the process of generating images using Stable Diffusion models as simple as possible. The package enables image generation with just a single function call, greatly simplifying the integration of Stable Diffusion into various applications. Utilizing Hugging Face's diffusers library, sevsd provides an intuitive and flexible interface for generating images based on textual prompts. This makes it an ideal choice for building HTTP APIs, high-level services, or any application requiring AI-driven image generation.

Features

• Simplified interface for Stable Diffusion image generation, enabling the creation of images with just a single function call.
• Easy integration of Stable Diffusion model into Python applications.
• Customizable image generation based on user-defined tasks and configurations.
• Batch processing capabilities for handling multiple tasks efficiently.
• Compatibility with CUDA-enabled GPUs and MPS (Apple's Metal Performance Shaders) for enhanced performance.

Requirements

• Python 3.11+
• PyTorch
• Hugging Face diffusers library
• CUDA-compatible GPU (recommended for better performance)

Installation

Install from PyPI

You can install sevsd directly from PyPI. This is the recommended way to install the package as it will always provide you with the latest stable version:

pip install sevsd

Install from Source

If you prefer to install sevsd from the source, for example, to get the latest changes that may not be released on PyPI yet, you can clone the repository and install it manually:

git clone https://github.com/leopedroso45/Stable-Diffusion-ImageGen
cd Stable-Diffusion-ImageGen
pip install .

Note: When installing from source, make sure you have the necessary build tools and dependencies installed on your system.

Usage

Import and use sevsd in your Python project:

from sevsd import do_work

# Define your models and jobs
models = [
    {
        "name": './model_cache/model1.safetensors',
        "executor": {
            "labels": [1],
            "num_of_exec": 1,
            "cfg_scale": 7,
            "inference_steps": 100,
        }
    },
    {
        "name": './model_cache/model2.safetensors',
        "executor": {
            "labels": [2],
            "num_of_exec": 2,
            "cfg_scale": 6,
            "inference_steps": 50,
        }
    },
]

jobs = [
    {
        "label": 1,
        "prompt": 'A scenic landscape',
        "negative_prompt": "blurred image, black and white, watermarked image",
    },
    {
        "label": 2,
        "prompt": 'A person wearing a mask',
        "negative_prompt": 'deformed anatomy, hand-drawn image, blurred image',
    },
]

do_work(models, jobs, './generated-images')

This example demonstrates a basic usage scenario. Customize the models and jobs as needed for your application.

Components

• setup_pipeline: Prepares the Stable Diffusion pipeline with the specified model configuration.
• process_task: Processes individual tasks, generating and saving images based on job specifications.
• generate_image: Handles the image generation process for each job.
• setup_device: Sets up the computation device (GPU or CPU) for image generation.
• check_os_path: Ensures the output path exists or creates it.
• check_cuda_and_clear_cache: Manages GPU memory and cache for efficient processing.
• do_work: Central function to orchestrate the processing of jobs with corresponding models.

Customization

You can customize the image generation process by adjusting the models and jobs lists. Define different prompts, model paths, execution parameters, and more to cater to diverse image styles and requirements.

Note

• Ensure sufficient GPU memory if using CUDA.
• The package is optimized for flexible handling of various job and model configurations.
• For detailed examples and advanced usage, refer to the source code documentation.

Contributing

Contributions to sevsd are welcome! Please refer to the repository's issues and pull requests for ongoing development.

License

sevsd is licensed under the MIT License.