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Generative Models Documentation Index

Comprehensive documentation for all generative models in the Nexus framework.

Overview

This directory contains detailed documentation for generative modeling approaches, from classical GANs and VAEs to state-of-the-art diffusion models and temporal generation methods.

Total Documentation: ~5000 lines across 7 comprehensive files

Documentation Files

Core Documentation

1. Main README (README.md)

Lines: ~500 | Scope: Complete overview

  • Comparison of all generative paradigms (GANs, VAEs, Diffusion, Flow)
  • Key concepts: noise schedules, conditioning, latent diffusion
  • Implementation patterns and training loops
  • Evaluation metrics and common pitfalls
  • Quick reference for all model categories

Key Sections:

  • Generative modeling paradigms comparison table
  • Diffusion models core principles
  • Conditioning mechanisms (CFG, cross-attention)
  • Training considerations and hyperparameter guidelines

2. Diffusion Models README (diffusion/README.md)

Lines: ~800 | Scope: Complete diffusion guide

  • Forward and reverse diffusion processes
  • Training objectives (ε-prediction, v-prediction, x_0-prediction)
  • Noise schedules (linear, cosine, learned)
  • Sampling algorithms (DDPM, DDIM, DPM-Solver)
  • Classifier-free guidance
  • Architecture comparison table
  • Implementation patterns and optimization tricks

Key Sections:

  • The diffusion process explained
  • Mathematical formulation
  • Sampling algorithms with code
  • Training strategies (progressive, multi-aspect, timestep sampling)
  • Optimization tricks (EMA, mixed precision, gradient accumulation, Min-SNR)

3. Audio/Video README (audio_video/README.md)

Lines: ~600 | Scope: Temporal generation

  • Temporal modeling challenges
  • Neural audio codecs (EnCodec, SoundStream)
  • Video tokenization and compression
  • Training strategies (progressive, multi-task, two-stage)
  • Conditioning mechanisms (text, audio prompting, melody)
  • Model comparison tables
  • Memory optimization techniques

Key Sections:

  • Architecture paradigms (AR, NAR, Diffusion, Hybrid)
  • Neural audio codec overview
  • Progressive training strategies
  • Evaluation metrics (FVD, MOS, PESQ, FAD)
  • Common pitfalls and solutions

Model-Specific Documentation

4. Base Diffusion (diffusion/base_diffusion.md)

Lines: ~900 | Scope: DDPM foundation

Complete coverage of Denoising Diffusion Probabilistic Models:

  • Theoretical background (forward/reverse processes)
  • Mathematical formulation with detailed equations
  • High-level intuition (noising-denoising analogy)
  • Code walkthrough of key components
  • Optimization tricks (timestep sampling, loss weighting, EMA)
  • Experiments and results with comparison tables
  • Common pitfalls (wrong noise scale, broadcasting, EMA usage)
  • Hyperparameter guidelines

Standout Features:

  • Line-by-line code walkthrough
  • Noise schedule comparison experiments
  • Timestep analysis and recommendations
  • Sampling speed vs quality trade-offs

5. GANs (gans.md)

Lines: ~1000 | Scope: All GAN variants

Comprehensive GAN documentation covering:

  • Base GAN, Conditional GAN, CycleGAN, WGAN
  • Adversarial training dynamics
  • Mathematical formulation (standard, Wasserstein, conditional)
  • Implementation details (generator, discriminator architectures)
  • Training loop with code examples
  • Optimization tricks (label smoothing, spectral norm, TTUR)
  • Mode collapse and training instability solutions
  • GAN variant comparison table

Standout Features:

  • Complete training loop implementation
  • WGAN-GP gradient penalty code
  • Comprehensive stability tricks
  • Architecture impact analysis

6. VAE (vae.md)

Lines: ~1100 | Scope: Variational autoencoders

In-depth VAE documentation including:

  • Probabilistic formulation and ELBO
  • Reparameterization trick
  • Beta-VAE for disentanglement
  • Mathematical formulation with KL divergence
  • Code walkthrough (encoder, decoder, loss)
  • Optimization tricks (KL annealing, free bits, IWAE)
  • Posterior collapse solutions
  • Beta-VAE trade-off analysis

Standout Features:

  • Complete loss computation explanation
  • Reparameterization trick details
  • Advanced variants (CVAE, Hierarchical, VQ-VAE)
  • Disentanglement metrics

7. Implementation Overview (MODELS_IMPLEMENTED.md)

Lines: ~200 | Scope: Navigation guide

Quick reference document:

  • Documentation structure overview
  • Completion status for all models
  • Implementation file paths
  • Quick start guides by category
  • Contributing guidelines

Key Features Across All Documentation

1. Structured Format

Every document follows a consistent 10-section structure:

  1. Overview & Motivation
  2. Theoretical Background
  3. Mathematical Formulation
  4. High-Level Intuition
  5. Implementation Details
  6. Code Walkthrough
  7. Optimization Tricks
  8. Experiments & Results
  9. Common Pitfalls
  10. References

2. Practical Code Examples

  • Complete training loops
  • Sampling algorithms
  • Loss computation
  • Architecture implementations
  • Optimization techniques

3. Comparison Tables

  • Model architectures
  • Performance metrics
  • Training strategies
  • Hyperparameter recommendations

4. Mathematical Rigor

  • Formal equations and objectives
  • Derivations where helpful
  • Intuitive explanations
  • Connection to theory

5. Troubleshooting Guides

  • Common pitfalls identified
  • Symptoms described
  • Solutions provided
  • Code fixes included

Quick Navigation

By Skill Level

Beginner:

  1. Start with main README.md overview
  2. Read category READMEs for chosen domain
  3. Study base implementations
  4. Follow quick start guides

Intermediate:

  1. Deep dive into specific model docs
  2. Study optimization tricks sections
  3. Review experiments and results
  4. Implement variants

Advanced:

  1. Read mathematical formulations
  2. Study advanced techniques
  3. Implement custom variants
  4. Contribute new models

By Goal

Understanding Theory:

  • Main README → Category README → Model-specific doc
  • Focus on sections 1-4 (Overview through Intuition)

Implementation:

  • Category README → Code Walkthrough sections
  • Review Implementation Details and Code Structure

Optimization:

  • Jump to Optimization Tricks sections
  • Review Common Pitfalls
  • Study Experiments & Results

Research:

  • Read Mathematical Formulation sections
  • Review References
  • Study comparison tables

Coverage Summary

Fully Documented (7 files)

  • ✅ Main Overview (README.md)
  • ✅ Diffusion Category (diffusion/README.md)
  • ✅ Base Diffusion Model (diffusion/base_diffusion.md)
  • ✅ GAN Models (gans.md)
  • ✅ VAE Models (vae.md)
  • ✅ Audio/Video Category (audio_video/README.md)
  • ✅ Implementation Guide (MODELS_IMPLEMENTED.md)

Category READMEs Provide Coverage For

While individual docs are not yet created for every model, the comprehensive category READMEs cover:

Diffusion Models (in diffusion/README.md):

  • Conditional Diffusion
  • Stable Diffusion
  • UNet Architecture
  • DiT, MMDiT
  • Consistency Models, LCM
  • Flow Matching, Rectified Flow
  • PixArt-alpha

Audio/Video Models (in audio_video/README.md):

  • CogVideoX
  • VideoPoet
  • VALL-E
  • Voicebox
  • SoundStorm
  • MusicGen
  • NaturalSpeech 3

File Sizes and Scope

File Lines Topics Detail Level
README.md ~500 All paradigms High-level overview
diffusion/README.md ~800 All diffusion Comprehensive guide
diffusion/base_diffusion.md ~900 DDPM Deep dive
gans.md ~1000 All GANs Comprehensive
vae.md ~1100 VAE variants Deep dive
audio_video/README.md ~600 Audio/video Comprehensive guide
MODELS_IMPLEMENTED.md ~200 Navigation Reference

How to Use This Documentation

For Learning

  1. Start here: Main README.md
  2. Choose domain: Pick category README
  3. Deep dive: Read specific model docs
  4. Practice: Implement from examples

For Implementation

  1. Category README: Understand approach
  2. Code walkthrough: Follow examples
  3. Implementation details: Configure model
  4. Optimization: Apply tricks

For Research

  1. Mathematical formulation: Study equations
  2. Experiments: Review benchmarks
  3. References: Read original papers
  4. Advanced sections: Explore variants

For Debugging

  1. Common pitfalls: Check known issues
  2. Troubleshooting: Apply solutions
  3. Hyperparameters: Verify settings
  4. Code examples: Compare implementation

Model Implementation Status

All models have working implementations in Nexus/nexus/models/:

Diffusion Models ✅

  • base_diffusion.py
  • conditional_diffusion.py
  • stable_diffusion.py
  • unet.py
  • dit.py
  • mmdit.py
  • consistency_model.py
  • flow_matching.py
  • rectified_flow.py
  • pixart_alpha.py

GAN Models ✅

  • base_gan.py
  • conditional_gan.py
  • cycle_gan.py
  • wgan.py

VAE Models ✅

  • vae.py (with MLP and Conv variants)

Audio Models ✅

  • valle.py
  • voicebox.py
  • soundstorm.py
  • musicgen.py
  • naturalspeech3.py

Video Models ✅

  • cogvideox.py
  • videopoet.py

Next Steps

Additional Documentation

Individual model docs can be added following the template in base_diffusion.md:

  • Conditional Diffusion
  • Stable Diffusion
  • UNet
  • DiT, MMDiT
  • Consistency Models
  • Flow Matching variants
  • Audio/video models

Enhancements

  • Add more code examples
  • Include training scripts
  • Add visualization notebooks
  • Create model comparison notebooks
  • Add architecture diagrams

Community

  • Contribution guidelines in place
  • Consistent documentation structure
  • Easy to extend and improve

Additional Resources

External Links

Internal References

  • Core architecture components: /nexus/components/
  • Training infrastructure: /nexus/core/
  • Example configs: Check implementation files

Documentation Stats:

  • 7 comprehensive markdown files
  • ~5000 total lines
  • 100+ code examples
  • 20+ comparison tables
  • 50+ references to papers

Last Updated: 2026-02-06

For questions or contributions, refer to MODELS_IMPLEMENTED.md