This repository provides a Python3/TensorFlow implementation of PixelCNN++, a powerful generative model for images. It addresses the challenge of generating high-fidelity images with tractable likelihoods, targeting researchers and practitioners in deep learning and computer vision. The benefit is the ability to generate realistic image samples and achieve state-of-the-art results on benchmark datasets.

How It Works

PixelCNN++ utilizes a convolutional neural network architecture where each pixel's probability distribution is conditioned on the values of pixels to its left and above. This autoregressive approach ensures tractability of likelihoods and efficient sampling. The implementation incorporates modifications like Discretized Logistic Mixture Likelihood, improving upon the original PixelCNN design for enhanced sample quality.

Quick Start & Requirements

• Install via pip: pip install numpy tensorflow-gpu imageio
• Requires Python 3, NumPy, TensorFlow (GPU version), and imageio.
• Multi-GPU training is supported and recommended for performance.
• Official pretrained model available for CIFAR-10 (2.92 bpd).
• Documentation and examples are available within the repository structure.

Highlighted Details

• Achieves 2.92 bits per dimension on CIFAR-10, outperforming prior PixelCNN models.
• Supports multi-GPU training for faster convergence.
• Can be adapted for custom datasets by implementing analogous data iterators.
• Offers conditional generation based on class labels.

Maintenance & Community

The project is archived and no longer actively maintained or updated. It originates from OpenAI and includes authors from leading research institutions.

Licensing & Compatibility

The repository does not explicitly state a license. Given its origin and the lack of a specified license, users should exercise caution regarding commercial use or integration into closed-source projects.

Limitations & Caveats

The code is provided as-is with no expected updates. It relies on TensorFlow 1.x (implied by tensorflow-gpu and the era of the paper) which may have compatibility issues with newer TensorFlow versions. The setup requires a multi-GPU environment for optimal training performance.