diffusion-image-gen 0.4.1

A package for generating images with diffusion

Diffusion-Based Image Generation

Overview

This project implements diffusion-based generative models for image synthesis. It provides a comprehensive framework for training and using diffusion models, with support for various diffusion processes, noise schedules, and sampling methods.

Features

• Multiple Diffusion Processes: Support for Variance Exploding (VE), Variance Preserving (VP), and other diffusion types
• Customizable Noise Schedules: Linear, Cosine, and custom noise schedules
• Various Samplers: Euler-Maruyama, Exponential Integrator, ODE Probability Flow, and Predictor-Corrector samplers
• Advanced Generation Capabilities:
  • Unconditional image generation
  • Class-conditional image generation
  • Image colorization
  • Image inpainting/imputation
• Interactive Dashboard: Built with Streamlit for easy model interaction, accessible via a simple CLI command.

Installation

Using pip (Recommended)

You can install the package directly from PyPI:

pip install diffusion-image-gen

From Source (for development)

Clone the repository:

git clone https://github.com/HectorTablero/image-gen.git
cd image-gen
pip install -e .

Usage

Basic Generation

from diffusion_image_gen import GenerativeModel

# Load a pre-trained model
# Note: You will need to have a model file (.pt or .pth) available.
# model = GenerativeModel.load("path/to/your/model.pth")

# Generate images
# images = model.generate(num_images=4, n_steps=500, seed=42)

Colorization

# Colorize a grayscale image
# colorized = model.colorize(grayscale_image, n_steps=500)

Image Inpainting

# Perform inpainting with a mask
# inpainted = model.imputation(image, mask, n_steps=500)

Note: The usage examples above assume you have a trained model file. The package provides the framework, but pre-trained models are not included in the base installation.

Interactive Dashboard

Run the dashboard to interact with your models using the command line:

diffusion-image-gen dashboard

This will start the Streamlit web application.

Documentation

A comprehensive version of the documentation is available at https://deepwiki.com/HectorTablero/image-gen

License

This project is licensed under the MIT License - see the LICENSE file for details.

Authors

• Héctor Tablero Díaz - [e-mail]
• Álvaro Martínez Gamo - [e-mail]