DDPM Model for Pixel Art Generation

Introduction

Pixel art generation presents unique challenges due to its inherently low resolution, sharp edges, and simple, discrete color palettes. Most generative models, including diffusion models, are designed for high-resolution, continuous-tone images, and thus often struggle to replicate these specific traits of pixel art.
project seeks to investigate the application of diffusion models for generating high-quality pixel art under low-resolution constraints. The primary aim is to adapt diffusion models to produce pixel art while maintaining critical stylistic features such as sharp edges, discrete color transitions, and minimal pixel blending.

Source codes and Implementation

• colorQ

Data Directory

data_dir: Directory containing the dataset.

Unet Model

Unet.py: Implementation of Unet model class.

Custom Dataset

Custom_dataset.py: Implementation of class to load dataset in standard torch format.

Training Notebook

training-notebook: Notebook for building, compiling, and training the model.

Requirements

Requirements

• Python 3.8 or higher
• PyTorch 1.9.0
• torchvision 0.10.0
• numpy 1.21.0
• pandas 1.3.0
• matplotlib 3.4.2