Skip to content

Image Generation Library

Welcome to the documentation for our image generation library. This library provides tools for training and using diffusion-based generative models for image generation tasks.

Features

  • Implementations of various diffusion processes
  • Multiple sampling algorithms
  • Support for conditional image generation
  • Utilities for evaluating model quality

Getting Started

Installation

Clone the repository:

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

Basic Usage

from image_gen import GenerativeModel

# Initialize a generative model
model = GenerativeModel(diffusion="ve", sampler="euler-maruyama")

# Train the model
model.train(dataset, epochs=100, batch_size=32, lr=1e-3)

# Generate images
generated_images = model.generate(num_samples=10, n_steps=500)

Documentation Structure