This repository provides a PyTorch implementation of OpenAI's PixelCNN++ generative model. It is targeted at researchers and practitioners interested in state-of-the-art image generation models, offering a comparable performance to the official TensorFlow implementation.

How It Works

The implementation follows the architecture of PixelCNN++, a powerful autoregressive model that generates images pixel by pixel. It utilizes convolutional neural networks with masked convolutions to ensure causality, predicting each pixel's value based on previously generated pixels. This approach allows for high-quality image synthesis.

Quick Start & Requirements

• Install via pip install -r requirements.txt.
• Requires PyTorch, NumPy, Pillow, and tqdm.
• Pre-trained models are available for download.

Highlighted Details

• Achieves 2.95 BPD on the test set, closely matching the official TensorFlow implementation's 2.92 BPD.
• Code structure is maintained for easy comparison with the original OpenAI implementation.

Maintenance & Community

This repository is no longer actively maintained, though issues can still be filed with the expectation of slow responses.

Licensing & Compatibility

The license is not specified in the README. Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The implementation omits data-dependent weight initialization and exponential moving average of past models for evaluation, which are present in the official TensorFlow version. The project is explicitly stated as no longer maintained.