MD Nodes

MD_NODES

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█▓▒░  │  CRACKER..: MDMAchine           PACKER....: PYTHON 3.10+    │  ░▒▓█
█▓▒░  │  STATUS...: EUPHORIC            NODES.....: 80+ UNLOCKED    │  ░▒▓█
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📟 What is MD_Nodes?

MD_Nodes is a collection of 80+ advanced ComfyUI custom nodes built by MDMAchine (A&E Concepts). Originally focused on audio generation workflows with ACE-Step, the suite has expanded to cover advanced sampling and scheduling, guidance systems, professional audio mastering, workflow automation, GGUF model loading, and system safety tools.

Everything in this repository is free to use. All code is open source GPL v3.

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🏗️ About This Repository

License: GPL v3 — pure Python source, no compiled binaries in this release.

All .py files in this repo are complete, readable source code under the GNU General Public License v3. There are no hidden .pyd or .so binary cores in this initial public release — what you see is what you get.

Why GPL v3?

• ✅ Transparency — Audit exactly how every node works.
• ✅ Community protection — Improvements to the wrappers must be shared back.
• ✅ Commercial freedom — Use these nodes in personal or commercial workflows, no restrictions.

Future Compiled Cores

The architecture is designed to support optional .pyd/.so compiled cores for select high-value algorithms in future releases. When that happens, the compiled cores will be free to use in workflows but protected against reverse engineering. The GPL v3 Python source layer will always remain fully open. This release ships source-only.

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🔥 Node Categories

🎨 Samplers & Schedulers

The engines that drive the diffusion process.

PingPong Sampler FBG

Bidirectional ancestral sampler with feedback guidance.

• Classes: PingPongSamplerNodeBasic, PingPongSamplerNodeFBG, PingPongSamplerNodeLite
• Features: Feedback guidance logic, restart modes, look-back SNR smoothing, NaN recovery
• Use Case: Resolves hyper-detailed textures in images or complex harmonics in audio that standard samplers turn to mush. The FBG variant is the primary production sampler for ACE-Step audio workflows.

AMED Sampler

Adaptive multi-engine diffusion sampler.

• Class: MD_AMED_Sampler
• Features: Multi-mode ancestral/predictor-corrector hybrid
• Use Case: Finding the middle ground between fast render speed and high-quality output.

F-Sampler

Fast sampler using Richardson extrapolation to skip steps.

• Class: FSampler
• Features: Conservative/aggressive skip strategies, quality-preserving step reduction
• Use Case: Cuts rendering time significantly while maintaining comparable output quality.

Hybrid Sigma Scheduler

Universal precision noise scheduling with 14 curve algorithms.

• Classes: HybridAdaptiveSigmas_Advanced, _Basic, _Lite
• Modes: Karras, Poly, AYS, Fibonacci, Exponential, Tangent, LINA, and more
• Use Case: Replaces the default ComfyUI scheduler with fine-grained control over when the model focuses on broad structure vs. fine detail.

GITS Scheduler

Gradient-informed timestep scaling.

• Class: MD_GITS_Scheduler
• Use Case: Allocates steps where the latent is changing most rapidly, skipping over stable regions. Efficient for both audio and image generation.

Noise Decay Scheduler

Custom decay curves for sigma scheduling.

• Class: NoiseDecayScheduler_Custom
• Use Case: Manual control over the exact noise decay profile.

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🧭 Guidance & Optimization

Tools that steer generation without destroying output quality.

APG Guider (Forked)

Adaptive Projected Gradient guidance.

• Class: APGGuiderForked
• Features: Scheduled APG scale, CFG, and momentum by sigma
• Use Case: Projects guidance math safely, allowing high prompt adherence without burning or oversaturating output.

Apply TPG

Token Perturbation Guidance — breaks repetitive patterns.

• Class: MD_ApplyTPG
• Use Case: Shuffles prompt tokens at the attention level to prevent repetitive structural artifacts.

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🎵 Audio Processing

Professional audio tools built directly into the ComfyUI node graph.

Auto Master Pro

Iterative mastering to hit a target LUFS and spectral profile.

• Class: MD_AutoMasterNode
• Features: 3-band compression, stereo widening, automatic gain staging
• Use Case: Makes AI-generated audio sound broadcast-ready in a single node.

Audio Auto EQ

One-click adaptive EQ with 18+ target profiles.

• Class: MD_AudioAutoEQ_Adaptive
• Profiles: Vocal Clarity, Podcast, Cinematic, Warm Analog, and more
• Use Case: Instantly corrects muddy, muffled, or harsh AI audio.

Mastering Chain (Modular)

Individual mastering components for custom pipelines.

• Classes: MasteringChainNode, MD_Mastering_Gain, MD_Mastering_EQ, MD_Mastering_Compressor, MD_Mastering_Limiter
• Use Case: Manual control over every stage of the mastering signal chain.

Broadcast Tools

Loudness normalization for platform and broadcast standards.

• Classes: MD_LUFS_Normalizer, MD_Stereo_Width_Controller
• Use Case: Hit exact LUFS targets for Spotify, YouTube, EBU R128.

Advanced Audio Preview & Save (AAPS)

Professional audio export with metadata embedding.

• Class: AdvancedAudioPreviewAndSave
• Features: MP3/FLAC/OPUS export, LUFS normalization presets, waveform visualization, workflow JSON embedding
• Use Case: The final output node for audio workflows. Normalizes, exports, and embeds generation metadata in a single step.

Audio Simple Editor

Sample-accurate trimming and fading.

• Class: MD_AudioSimpleEditor
• Use Case: Slice, trim, and apply linear/exponential fades to audio tensors.

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📦 ACE Engine

Nodes specifically for ACE-Step audio generation models.

ACE-Step XL Loader

Model loader for ACE-Step 1.5 XL variants.

• Class: MD_ACE_XLLoader
• Features: Adapter support, tunable AuraFlow shift
• Use Case: Loads ACE-Step base, SFT, and turbo UNet variants with correct architecture detection.

ACE Sigma Denoise Patcher

Slices an existing sigma schedule for audio-to-audio workflows.

• Class: MD_ACE_SigmaDenoisePatcher
• Use Case: Feed it an existing audio file and re-generate with altered style or instrumentation while preserving the original structure.

AceStep Inpaint (Generative Fill)

Time-based generative fill mask for audio latents.

• Class: MD_ACE_LatentInpaintMask
• Use Case: Mask a specific time region in an audio latent and regenerate just that section.

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🛡️ Guardian Suite

Crash prevention and output quality protection.

NaN / Image / Audio Guardians

Multi-modal protection against math errors and artifacts.

• Classes: MD_NaN_Guardian, MD_Image_Guardian, MD_Audio_Guardian
• Use Case: Detects and repairs NaN/Inf values in tensors before they crash ComfyUI or produce garbage output.

Universal Latent Sanitizer

Deep-level latent repair.

• Class: MD_LatentSanitizer
• Use Case: Clamps wild outlier values before VAE decode, eliminating static pops and decoding artifacts.

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🛠️ Workflow Automation & Utilities

Prompting & Wildcards

Wildcard expansion, LLM integration, and scene automation.

• Classes: WildcardPromptBuilder, UniversalWildcardOrchestrator, SceneGeniusAutocreator
• Use Case: Automate prompt generation with nested wildcard logic, local LLM routing (Ollama/LM Studio), and genre/style preset libraries.

YAML Configuration

YAML-driven parameter systems for complex nodes.

• Classes: MD_YAML_Generator, MD_YAML_Utils
• Use Case: Load and manage complex node configurations from human-readable YAML files. Used extensively by the PingPong sampler nodes.

Smart Filenames & Saving

Intelligent filename generation with metadata embedding.

• Classes: SmartFilenameBuilder, AdvancedMediaSave
• Use Case: Auto-generate organized filenames and embed generation metadata.

Hardware & System Management

Real-time VRAM/GPU monitoring and protection.

• Classes: MD_VRAMCanary, LLMVRAMManager, GPUTemperatureProtectionEnhanced
• Use Case: Monitor GPU temperature and VRAM headroom, pause the queue if limits are hit.

Seeds & Conditioning

Seed management and conditioning cache.

• Classes: EnhancedSeedSaver, MD_AdvancedSeedGenerator, MD_LoadConditioning, MD_SaveConditioning
• Use Case: Save favorite seeds to disk, cache expensive text encoding to speed up subsequent runs.

Maintenance Tools

Node-based repo management.

• Classes: MD_RepoMaintenance, MD_GlobalUpdateManager, MD_ModelStateReset
• Use Case: Update custom nodes, clear VRAM caches, roll back broken updates.

Math, Logic & Modulators

Conditional routing and signal modulation.

• Classes: MD_Math_Add, MD_Math_Subtract, MD_MultiSwitch, MD_LFO_Generator, MD_CustomNoiseGenerator
• Use Case: Build decision-branching workflows, modulate parameters over time with LFOs, generate custom noise patterns.

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🔗 Coming Soon

These algorithms are being released as standalone repositories and ComfyUI node packages. Each pairs directly with MD_Nodes workflows.

| Repo | Description | |------|-------------| | MDMAchine/STORM-Sampler | STORM adaptive hybrid ODE solver (STORK4 + DPM++3M) with LookBack SNR smoother | | MDMAchine/MD-Causal-Scheduler | 14-mode causal sigma scheduler with LINA time-axis warp | | MDMAchine/MD-HAP-Scheduler | Hamiltonian potential well sigma scheduler | | MDMAchine/MD-Audio-VAE-Tiled | Tiled VAE decoder for long-form audio with LSS, HPC, SCE |

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🌐 HOT-Step-CPP

MDMAchine has contributed several plugins to HOT-Step-CPP, a C++ inference runtime with a Lua plugin system maintained by scragnog.

Contributions merged upstream include the PingPong solver, Causal and HAP schedulers, STORM Guidance V2, the STORM sampler core, Seed Manager UI, and DSP improvements to the tiled decoder. More in progress including negative prompt support.

If you're running HOT-Step-CPP, these plugins ship with it — no separate install needed. Check the HOT-Step-CPP repo for the full list and release notes.

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🧰 Installation

cd path/to/ComfyUI/custom_nodes
git clone https://github.com/MDMAchine/ComfyUI_MD_Nodes.git
cd ComfyUI_MD_Nodes
pip install -r requirements.txt

Or via ComfyUI Manager: search for MD_Nodes and click Install.

Restart ComfyUI after installation.

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📋 Requirements

Python: 3.10+ ComfyUI: Latest (Nodes 2.0 compatible) PyTorch: 2.0+ with CUDA (2.11+cu130 recommended for full feature set)

Audio nodes: soundfile, librosa, pyloudnorm, pedalboard Visualization: matplotlib

See requirements.txt for the full list.

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🔐 License

GPL v3 — full source, no compiled binaries in this release.

You are free to:

• ✅ Use in personal or commercial projects
• ✅ Read, modify, and learn from the source code
• ✅ Redistribute with attribution
• ✅ Fork and create derivative works (GPL v3 terms apply)

Your generated content (audio, images, video) is always 100% yours.

See LICENSE.md for full details.

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🚀 Roadmap

Current: June 2026 Public Release

• [x] Full audit — GPL headers, VERSION constants, unit tests
• [x] torchaudio fully migrated to soundfile
• [x] IP firewall verified — no proprietary code in public repo
• [x] HOT-Step-CPP contributions documented
• [ ] Tooltip coverage pass (currently 66%, targeting 100%)
• [ ] Full node documentation

Next Phase

• [ ] Compiled core releases for select algorithms (.pyd/.so)
• [ ] API service endpoints (Captain Quantum, SCT Analyzer, FidelityX)
• [ ] Additional ACE-Step nodes
• [ ] Video generation utilities

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💾 Credits

| Handle | Contribution | |--------|-------------| | MDMAchine (Alex) | Core architecture, all nodes, HOT-Step-CPP plugins | | scragnog | HOT-Step-CPP maintainer, upstream collaboration | | blepping | Original PingPong/APG concepts | | c0ffymachyne | Audio I/O and signal processing research | | Community | Bug reports, testing, feedback |

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🐛 Support

Issues: GitHub Issues Discussions: GitHub Discussions Consulting / custom development: [email protected]

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█▓▒░                                                                   ░▒▓█
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█▓▒░                                                                   ░▒▓█
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