Skip to content

Latest commit

 

History

History
528 lines (433 loc) · 18.3 KB

MISSING_DOCS.md

File metadata and controls

528 lines (433 loc) · 18.3 KB

Missing Documentation - Generative Models

Overview

This document tracks the completion status of all documentation files referenced in the README files. Each missing file should be a comprehensive 600-1000 line document following the established 10-section template.

Last Updated: 2026-02-07

Completion Status Summary

Completed Files (7/24)

  • ✅ README.md (main overview)
  • ✅ DOCUMENTATION_INDEX.md
  • ✅ MODELS_IMPLEMENTED.md
  • ✅ diffusion/README.md
  • ✅ diffusion/base_diffusion.md (900+ lines)
  • ✅ diffusion/dit.md (1000+ lines) [NEW]
  • ✅ diffusion/flow_matching.md (900+ lines) [NEW]
  • ✅ diffusion/rectified_flow.md (900+ lines) [NEW]
  • ✅ audio_video/README.md
  • ✅ gans.md (1000+ lines)
  • ✅ vae.md (1100+ lines)

Missing Diffusion Documentation (7 files)

1. diffusion/conditional_diffusion.md

Status: ❌ Missing Referenced in: diffusion/README.md (line 19) Implementation: Nexus/nexus/models/diffusion/conditional_diffusion.py

Expected Sections:

  1. Overview: Conditioning mechanisms for diffusion models
  2. Theoretical Background: Class conditioning, text conditioning, image conditioning
  3. Mathematical Formulation: Conditional score functions, classifier-free guidance derivation
  4. High-Level Intuition: Why conditioning works, guidance scale effects
  5. Implementation: TimestepEmbedder, ConditioningEncoder, CrossAttention modules
  6. Code Walkthrough: Training with conditioning, null conditioning dropout
  7. Optimization: Guidance scale selection, conditioning dropout schedules
  8. Experiments: FID vs guidance scale, class vs text conditioning results
  9. Common Pitfalls: Forgetting null conditioning, wrong guidance formula
  10. References: Ho & Salimans 2022, Nichol et al. 2022

2. diffusion/stable_diffusion.md

Status: ❌ Missing Referenced in: diffusion/README.md (line 18), main README.md (line 18) Implementation: Nexus/nexus/models/diffusion/stable_diffusion.py

Expected Sections:

  1. Overview: Latent diffusion models, why latent space matters
  2. Theoretical Background: VAE compression, latent space properties
  3. Mathematical Formulation: Latent diffusion loss, VAE encoder/decoder
  4. High-Level Intuition: Operating in compressed space, memory benefits
  5. Implementation: VAE integration, CLIP text encoder, U-Net backbone
  6. Code Walkthrough: Full text-to-image pipeline, encoding/decoding
  7. Optimization: Latent space normalization, aspect ratio buckets
  8. Experiments: SD 1.5 vs 2.1 vs XL results, resolution comparison
  9. Common Pitfalls: VAE artifacts, latent scaling issues
  10. References: Rombach et al. 2022, Stable Diffusion papers

Priority: HIGH (widely used model)

3. diffusion/unet.md

Status: ❌ Missing Referenced in: diffusion/README.md (line 31) Implementation: Nexus/nexus/models/diffusion/unet.py

Expected Sections:

  1. Overview: U-Net architecture for diffusion models
  2. Theoretical Background: Skip connections, multi-scale processing
  3. Mathematical Formulation: ResNet blocks, attention layers, downsampling/upsampling
  4. High-Level Intuition: Why U-Nets work for diffusion, spatial hierarchies
  5. Implementation: ResidualBlock, AttentionBlock, DownBlock, UpBlock
  6. Code Walkthrough: Forward pass with skip connections
  7. Optimization: Efficient attention (flash attention), gradient checkpointing
  8. Experiments: U-Net vs transformer comparison, depth analysis
  9. Common Pitfalls: Skip connection mistakes, attention memory issues
  10. References: Ronneberger et al. 2015, DDPM paper

4. diffusion/mmdit.md

Status: ❌ Missing Referenced in: diffusion/README.md (line 23), main README.md (line 23) Implementation: Nexus/nexus/models/diffusion/mmdit.py

Expected Sections:

  1. Overview: Multimodal Diffusion Transformer (SD3, FLUX)
  2. Theoretical Background: Dual-stream architecture, joint attention
  3. Mathematical Formulation: Image and text stream processing
  4. High-Level Intuition: Why separate streams, when to merge
  5. Implementation: DualStreamTransformer, joint attention blocks
  6. Code Walkthrough: Two-stream forward pass, modality fusion
  7. Optimization: Memory efficiency for dual streams
  8. Experiments: SD3 results, FLUX benchmarks
  9. Common Pitfalls: Stream synchronization, attention masking
  10. References: SD3 paper, FLUX documentation

Priority: HIGH (powers SD3 and FLUX)

5. diffusion/pixart_alpha.md

Status: ❌ Missing Referenced in: diffusion/README.md (line 24), main README.md (line 24) Implementation: Nexus/nexus/models/diffusion/pixart_alpha.py

Expected Sections:

  1. Overview: Efficient high-resolution text-to-image
  2. Theoretical Background: Efficient training strategies, T5 conditioning
  3. Mathematical Formulation: Cross-attention decomposition
  4. High-Level Intuition: Training efficiency tricks
  5. Implementation: EfficientAttention, T5Integration
  6. Code Walkthrough: Training pipeline, sampling
  7. Optimization: Fast training techniques
  8. Experiments: PixArt-α vs SD comparison, efficiency analysis
  9. Common Pitfalls: T5 memory issues
  10. References: Chen et al. 2023

6. diffusion/consistency_models.md

Status: ❌ Missing Referenced in: diffusion/README.md (line 59), main README.md (line 28) Implementation: Nexus/nexus/models/diffusion/consistency_model.py

Expected Sections:

  1. Overview: Single-step and few-step generation
  2. Theoretical Background: Self-consistency property, consistency training
  3. Mathematical Formulation: Consistency loss, boundary conditions
  4. High-Level Intuition: Why consistency enables few-step generation
  5. Implementation: ConsistencyModel, consistency_loss
  6. Code Walkthrough: Training procedure, 1-step sampling
  7. Optimization: Distillation from pre-trained diffusion
  8. Experiments: 1-step FID, 2-step FID, comparison to diffusion
  9. Common Pitfalls: Boundary condition issues, training instability
  10. References: Song et al. 2023

Priority: HIGH (important for fast sampling)

7. diffusion/lcm.md

Status: ❌ Missing Referenced in: diffusion/README.md (line 66), main README.md (line 29) Implementation: Not yet implemented (mentioned in README)

Expected Sections:

  1. Overview: Latent Consistency Models for fast sampling
  2. Theoretical Background: Distilling latent diffusion models
  3. Mathematical Formulation: Consistency distillation in latent space
  4. High-Level Intuition: Combining latent space + consistency
  5. Implementation: LCM training, guidance distillation
  6. Code Walkthrough: 2-4 step generation pipeline
  7. Optimization: Efficient distillation
  8. Experiments: LCM-LoRA results, speed comparisons
  9. Common Pitfalls: Distillation hyperparameters
  10. References: Luo et al. 2023

Note: Implementation file doesn't exist yet; may need to create both code and docs.

Missing Audio/Video Documentation (7 files)

8. audio_video/cogvideox.md

Status: ❌ Missing Referenced in: audio_video/README.md (line 11), main README.md (line 37) Implementation: Nexus/nexus/models/video/cogvideox.py

Expected Sections:

  1. Overview: Expert transformer for text-to-video
  2. Theoretical Background: 3D causal attention, temporal modeling
  3. Mathematical Formulation: Spatiotemporal attention, expert routing
  4. High-Level Intuition: Video generation challenges
  5. Implementation: 3DCausalAttention, ExpertTransformer
  6. Code Walkthrough: Progressive training (image → short video → long video)
  7. Optimization: Memory efficiency for video, gradient checkpointing
  8. Experiments: CogVideoX benchmarks, FVD scores
  9. Common Pitfalls: Temporal consistency, memory issues
  10. References: Hong et al. 2024, ICLR 2025

Priority: HIGH (SOTA video generation)

9. audio_video/videopoet.md

Status: ❌ Missing Referenced in: audio_video/README.md (line 17), main README.md (line 38) Implementation: Nexus/nexus/models/video/videopoet.py

Expected Sections:

  1. Overview: LLM approach to video generation
  2. Theoretical Background: Unified tokenization, multi-task pre-training
  3. Mathematical Formulation: Video tokenizer, autoregressive generation
  4. High-Level Intuition: Video as language
  5. Implementation: VideoTokenizer, MultiTaskTrainer
  6. Code Walkthrough: Multi-task training, zero-shot transfer
  7. Optimization: Efficient tokenization
  8. Experiments: Zero-shot capabilities
  9. Common Pitfalls: Tokenization bottlenecks
  10. References: Kondratyuk et al. 2023

10. audio_video/valle.md

Status: ❌ Missing Referenced in: audio_video/README.md (line 25), main README.md (line 41) Implementation: Nexus/nexus/models/audio/valle.py

Expected Sections:

  1. Overview: Neural codec language modeling for TTS
  2. Theoretical Background: EnCodec integration, two-stage training
  3. Mathematical Formulation: Autoregressive + non-autoregressive
  4. High-Level Intuition: Zero-shot voice cloning
  5. Implementation: ARStage, NARStage, EnCodecIntegration
  6. Code Walkthrough: Two-stage training, 3-second prompting
  7. Optimization: Efficient codec generation
  8. Experiments: MOS scores, zero-shot results
  9. Common Pitfalls: Codec artifacts, prompt length
  10. References: Wang et al. 2023

Priority: HIGH (zero-shot TTS)

11. audio_video/voicebox.md

Status: ❌ Missing Referenced in: audio_video/README.md (line 31), main README.md (line 42) Implementation: Nexus/nexus/models/audio/voicebox.py

Expected Sections:

  1. Overview: Non-autoregressive speech generation
  2. Theoretical Background: Flow matching for speech
  3. Mathematical Formulation: Speech flow matching objective
  4. High-Level Intuition: In-context learning for voices
  5. Implementation: FlowMatchingSpeech, AcousticPrompting
  6. Code Walkthrough: Fast generation pipeline
  7. Optimization: Efficient flow matching
  8. Experiments: Voicebox vs VALL-E comparison
  9. Common Pitfalls: Prompt conditioning
  10. References: Le et al. 2023

12. audio_video/soundstorm.md

Status: ❌ Missing Referenced in: audio_video/README.md (line 37), main README.md (line 43) Implementation: Nexus/nexus/models/audio/soundstorm.py

Expected Sections:

  1. Overview: Parallel audio generation
  2. Theoretical Background: Confidence-based iterative decoding
  3. Mathematical Formulation: MaskGIT-style generation
  4. High-Level Intuition: Parallel vs autoregressive
  5. Implementation: ConfidenceDecoding, ParallelGeneration
  6. Code Walkthrough: 2-second audio in 0.5 seconds
  7. Optimization: Efficient parallel decoding
  8. Experiments: Speed vs quality trade-off
  9. Common Pitfalls: Confidence thresholds
  10. References: Borsos et al. 2023

13. audio_video/musicgen.md

Status: ❌ Missing Referenced in: audio_video/README.md (line 43), main README.md (line 44) Implementation: Nexus/nexus/models/audio/musicgen.py

Expected Sections:

  1. Overview: Text-to-music generation
  2. Theoretical Background: Multi-stream transformer, melody conditioning
  3. Mathematical Formulation: Chroma features, music generation objective
  4. High-Level Intuition: Controllable music synthesis
  5. Implementation: MultiStreamTransformer, MelodyConditioning
  6. Code Walkthrough: Text + melody to music
  7. Optimization: Efficient music generation
  8. Experiments: MusicGen benchmarks, user studies
  9. Common Pitfalls: Melody alignment
  10. References: Copet et al. 2023

14. audio_video/naturalspeech3.md

Status: ❌ Missing Referenced in: audio_video/README.md (line 49), main README.md (line 45) Implementation: Nexus/nexus/models/audio/naturalspeech3.py

Expected Sections:

  1. Overview: Factorized diffusion for speech
  2. Theoretical Background: Disentangled prosody and content
  3. Mathematical Formulation: Factorized diffusion objective
  4. High-Level Intuition: Why factorization matters
  5. Implementation: FactorizedDiffusion, ProsodyEncoder, ContentEncoder
  6. Code Walkthrough: Disentangled training and generation
  7. Optimization: Efficient factorized generation
  8. Experiments: SOTA quality results, ablations
  9. Common Pitfalls: Factor alignment
  10. References: Ju et al. 2024

Documentation Template

All missing documentation should follow this 10-section template:

Template Structure

# [Model Name]

## 1. Overview and Motivation
- Problem being solved
- Key innovations
- Why it matters
- Architecture at a glance (ASCII diagram)

## 2. Theoretical Background
- Underlying theory
- Connection to related work
- Mathematical foundations
- Key concepts

## 3. Mathematical Formulation
- Precise equations
- Training objective
- Loss functions
- Algorithm pseudocode

## 4. High-Level Intuition
- Non-technical explanations
- Analogies
- Visual intuition
- When to use this model

## 5. Implementation Details
- Configuration parameters
- Key components
- Architecture decisions
- Design patterns

## 6. Code Walkthrough
- Training loop
- Forward pass
- Sampling procedure
- Complete examples with 30-50 lines per example

## 7. Optimization Tricks
- Training tips
- Hyperparameter selection
- Speed optimizations
- Memory optimizations

## 8. Experiments and Results
- Benchmark results
- Ablation studies
- Comparison tables
- FID/MOS/other metrics

## 9. Common Pitfalls
- Typical mistakes
- How to avoid them
- Debugging tips
- Error messages and solutions

## 10. References
- Original papers (with arxiv links)
- Related work
- Code repositories
- Additional resources

Documentation Length

  • Target: 600-1000 lines per file
  • Minimum: 500 lines with comprehensive coverage
  • Examples:
    • base_diffusion.md: 900 lines ✅
    • dit.md: 1000+ lines ✅
    • gans.md: 1000 lines ✅
    • vae.md: 1100 lines ✅

Implementation Files Status

All implementation files exist and are complete:

Diffusion Models ✅

  • Nexus/nexus/models/diffusion/base_diffusion.py
  • Nexus/nexus/models/diffusion/conditional_diffusion.py
  • Nexus/nexus/models/diffusion/stable_diffusion.py
  • Nexus/nexus/models/diffusion/unet.py
  • Nexus/nexus/models/diffusion/dit.py
  • Nexus/nexus/models/diffusion/mmdit.py
  • Nexus/nexus/models/diffusion/consistency_model.py
  • Nexus/nexus/models/diffusion/flow_matching.py
  • Nexus/nexus/models/diffusion/rectified_flow.py
  • Nexus/nexus/models/diffusion/pixart_alpha.py

Audio Models ✅

  • Nexus/nexus/models/audio/valle.py
  • Nexus/nexus/models/audio/voicebox.py
  • Nexus/nexus/models/audio/soundstorm.py
  • Nexus/nexus/models/audio/musicgen.py
  • Nexus/nexus/models/audio/naturalspeech3.py

Video Models ✅

  • Nexus/nexus/models/video/cogvideox.py
  • Nexus/nexus/models/video/videopoet.py

GAN & VAE Models ✅

  • Nexus/nexus/models/gan/base_gan.py
  • Nexus/nexus/models/gan/conditional_gan.py
  • Nexus/nexus/models/gan/cycle_gan.py
  • Nexus/nexus/models/gan/wgan.py
  • Nexus/nexus/models/cv/vae/vae.py

Priority Ranking

High Priority (Core Models)

  1. diffusion/stable_diffusion.md - Most widely used
  2. diffusion/mmdit.md - Powers SD3 and FLUX
  3. diffusion/consistency_models.md - Fast sampling
  4. audio_video/cogvideox.md - SOTA video generation
  5. audio_video/valle.md - Zero-shot TTS

Medium Priority (Important Architectures)

  1. diffusion/unet.md - Foundational architecture
  2. diffusion/conditional_diffusion.md - Essential technique
  3. diffusion/pixart_alpha.md - Efficient training
  4. audio_video/voicebox.md - Fast speech synthesis

Lower Priority (Specialized Models)

  1. diffusion/lcm.md - Specialized distillation
  2. audio_video/videopoet.md - Research model
  3. audio_video/soundstorm.md - Specialized audio
  4. audio_video/musicgen.md - Music-specific
  5. audio_video/naturalspeech3.md - Advanced TTS

Progress Tracking

Completed This Session

  • ✅ diffusion/dit.md (1000+ lines)
  • ✅ diffusion/flow_matching.md (900+ lines)
  • ✅ diffusion/rectified_flow.md (900+ lines)
  • ✅ MISSING_DOCS.md (this file)

Remaining: 14 files

  • 7 diffusion model docs
  • 7 audio/video model docs

Estimated Effort

  • Time per doc: 20-30 minutes for comprehensive 700+ line file
  • Total remaining: ~5-7 hours for all 14 files
  • Could be distributed across multiple sessions

How to Create Missing Documentation

Step-by-Step Process

  1. Read Implementation File

    cat Nexus/nexus/models/[category]/[model].py

  2. Study README References

    • Check what README says about the model
    • Note any specific details mentioned

  3. Follow Template

    • Use the 10-section structure
    • Aim for 600-1000 lines
    • Include code examples

  4. Include Code Examples

    • Training loop (30-50 lines)
    • Sampling code (30-50 lines)
    • Configuration (10-20 lines)
    • Optimization tricks (with code)

  5. Add References

    • Original paper with arxiv link
    • Related papers
    • Code repositories
    • Benchmarks

Example Starter

# For diffusion/conditional_diffusion.md

# 1. Read implementation
import nexus.models.diffusion.conditional_diffusion as cd

# 2. Extract key components
# - TimestepEmbedder
# - ConditioningEncoder
# - ClassifierFreeGuidance

# 3. Write sections
# - Overview: What is conditioning?
# - Theory: Math of conditional diffusion
# - Implementation: Code walkthrough
# - Experiments: CFG scale impact
# etc.

Next Steps

Immediate (High Priority)

  1. Create diffusion/stable_diffusion.md
  2. Create diffusion/mmdit.md
  3. Create diffusion/consistency_models.md

Short Term (Medium Priority)

  1. Create diffusion/conditional_diffusion.md
  2. Create diffusion/unet.md
  3. Create audio_video/cogvideox.md

Long Term (Complete Coverage)

  1. Create remaining 8 documentation files

Contributing

When creating new documentation:

  1. Follow the template - All 10 sections required
  2. Include code - Runnable examples from implementation
  3. Be comprehensive - 600-1000 lines minimum
  4. Add visuals - ASCII diagrams, tables, equations
  5. Reference papers - Include arxiv links
  6. Test examples - Ensure code snippets are correct

Verification Checklist

For each completed documentation file:

  • All 10 sections present
  • 600+ lines of content
  • 3+ code examples with 30+ lines each
  • ASCII architecture diagram
  • Comparison tables
  • References with links
  • Common pitfalls section
  • Experiments and results
  • Mathematical formulations
  • References implementation file

Total Documentation Progress: 10/24 files (42%) Documentation Lines: ~8500 lines completed Target Lines: ~20000 lines for full coverage Completion: High-quality, comprehensive documentation following established template