PyTorch Generative AI Model Implementation Collection: From Character-Level GPT to 3D Neural Radiance Fields

This GitHub repository is a collection of generative AI models implemented using PyTorch, covering various architectures such as character-level GPT, GAN, VAE, WDCGAN, and Plenoxels. It provides a complete code base for learning and experimenting with generative models. The project uses a modular structure for easy understanding and extension.

• Original Author/Maintainer: JuliMuehl
• Source Platform: GitHub
• Original Title: generative_models
• Original Link: https://github.com/JuliMuehl/generative_models
• Release Date: 2026-06-10

Character-Level GPT

A character-level language model implemented from scratch, demonstrating core Transformer principles. It predicts directly at the character level, includes a complete training process and progress check tools, helping to understand concepts like self-attention and positional encoding.

GAN

Standard GAN architecture, trained adversarially by generator and discriminator to learn data distribution, demonstrating the process of generating realistic synthetic data.

VAE (MNIST Version)

A generative architecture based on probabilistic graphical models, learning latent representations of data. It demonstrates the core ideas of encoding into probability distributions and sampling decoding on the MNIST dataset, suitable for tasks like image generation and anomaly detection.

WDCGAN (CIFAR-10 Version)

An improved GAN that introduces Wasserstein distance and gradient penalty to solve training instability issues. Its deep convolutional structure captures hierarchical image features, generating 32x32 color images.

Plenoxels

A neural radiance field method for 3D scene representation and rendering, using voxel grids to store radiance fields, improving training and inference speed. It represents the cutting-edge direction of generative AI expansion into 3D.

The project uses the following tech stack:

• PyTorch: Core deep learning framework
• PyTorch Geometric: Graph neural network extension
• Jupyter Notebook: Interactive development and visualization
• NumPy/Pandas: Data processing and analysis Additionally, it provides configuration files for different hardware (standard GPU, Intel XPU acceleration) to support hardware diversity.

Teaching Value

1. High code readability with no over-encapsulation;
2. Progressive difficulty from simple character-level models to complex 3D rendering;
3. Includes complete workflows for data loading, model definition, training loops, and evaluation.

Research Value

1. Serves as a baseline implementation to quickly validate new ideas;
2. Extract specific modules for use in complex projects;
3. Used as teaching material to train teams or students.

Potential application scenarios of generative models:

• Content creation: Text generation, image synthesis, 3D asset generation;
• Data augmentation: Generate synthetic training data for supervised learning;
• Anomaly detection: Identify abnormal samples using VAE reconstruction errors;
• Drug discovery: Molecular structure generation and optimization;
• Art and design: AI-assisted creative workflows.

Generative AI is developing rapidly, with fast technological iterations from simple autoencoders to diffusion models. This repository covers basic architectures and is the cornerstone of modern generative AI. Recommendations: Beginners should start with character-level GPT or VAE to understand the combination of probabilistic modeling and neural networks; experienced users can extend Plenoxels to complex scenes or combine GAN and diffusion model technologies. Future directions: Multimodal fusion and controllable generation—basic implementations are the necessary path to the future.