ComfyUI-TripleKSampler

Triple-stage sampling nodes for Wan2.2 split models with Lightning LoRA integration.

Features

• Triple-Stage Workflow - Base denoising → Lightning high → Lightning low
• Six Node Variants - Simple/Advanced/Advanced Alt for both native KSampler and WanVideoWrapper workflows
• Intelligent Auto-Calculation - Optimal parameter computation
• Model-Safe Cloning - No mutation of original models
• Sigma Shift Integration - Built-in ModelSamplingSD3 support
• Automatic Sigma Refinement - Theoretical optimization for perfect boundary alignment (refined strategies)

Quick Start

1. Install

   cd ComfyUI/custom_nodes/
   git clone https://github.com/VraethrDalkr/ComfyUI-TripleKSampler.git
   cd ComfyUI-TripleKSampler && pip install -r requirements.txt

2. Optional: WanVideoWrapper Integration - Install ComfyUI-WanVideoWrapper to enable TripleWVSampler nodes

3. Use - Find nodes under TripleKSampler category after ComfyUI restart

   • TripleKSampler/sampling - Native KSampler workflow nodes
   • TripleKSampler/wanvideo - WanVideoWrapper integration nodes
   • TripleKSampler/utilities - Switch Strategy utility nodes

4. Configure - Connect your Wan2.2 models and set basic parameters

Why Use TripleKSampler?

The TripleKSampler node streamlines complex multi-model workflows while respecting base model step resolution. The diagram below compares four different approaches:

Workflow Comparison:

1. Base Models Only - Maximum quality, slowest generation (full base model processing)
2. Lightning Models Only - Minimum quality, fastest generation (full lightning processing)
3. Typical 3 KSamplers - Manual setup with decent quality and improved motion, but doesn't respect base model step resolution
4. TripleKSampler Node - Automated approach with decent quality, improved motion, and proper base model step resolution

The example shown uses lightning_start=2, lightning_steps=8 with the default Base Quality Threshold and the 50% switch strategy. This demonstrates how TripleKSampler automates the complex model switching that would otherwise require manual KSampler coordination.

Node Types

Node	Category	Best For	Key Features
TripleKSampler (Simple)	sampling	Most users	Smart defaults, auto-calculation, streamlined interface
TripleKSampler (Advanced)	sampling	Power users	Full control, 8 switching strategies, dynamic UI, dry-run testing
TripleKSampler (Advanced Alt)	sampling	Power users	Full control, 8 switching strategies, static UI, dry-run testing - use if dynamic UI causes issues
TripleWVSampler (Simple)	wanvideo	WanVideoWrapper users	Smart defaults for TripleWVSampler workflows
TripleWVSampler (Advanced)	wanvideo	WanVideoWrapper power users	Full control for TripleWVSampler workflows, dynamic UI, dry-run testing
TripleWVSampler (Advanced Alt)	wanvideo	WanVideoWrapper power users	Full control for TripleWVSampler workflows, static UI, dry-run testing
Switch Strategy (Simple)	utilities	Simple node users	External strategy control, 5 strategies
Switch Strategy (Advanced)	utilities	Advanced node users	External strategy control, 8 strategies

Essential Parameters

• sigma_shift - Sigma shift value (default: 5.0)
• base_cfg - CFG for base denoising (default: 3.5)
• lightning_start - Starting step in lightning schedule (default: 1)
• lightning_steps - Total lightning steps (default: 8)

Documentation

• 📖 Complete Documentation - Comprehensive guides and reference
• ⚙️ Installation Guide - Detailed setup instructions
• 📋 Parameter Reference - Full parameter documentation
• 🔧 Configuration Guide - TOML configuration setup
• 🎯 Model Switching Strategies - Strategy explanations
• 🚀 Advanced Features - Edge cases and special modes
• 🛠️ Troubleshooting - Common issues and solutions

Example Workflows

Example workflows are included in the example_workflows/ directory.

Text-to-Video (T2V):

• t2v_simple.json - Simple node with smart defaults
• t2v_advanced.json - Advanced node with full parameter control
• t2v_simple_custom_lora.json - Demonstrates layering custom LoRAs with Lightning LoRAs

Image-to-Video (I2V):

• i2v_simple.json - Simple node with smart defaults
• i2v_advanced.json - Advanced node with full parameter control

WanVideoWrapper Workflows (requires ComfyUI-WanVideoWrapper):

• t2v_wanvideo_advanced.json - Text-to-Video with TripleWVSampler Advanced
• i2v_wanvideo_advanced.json - Image-to-Video with TripleWVSampler Advanced

Hybrid Workflow: hybrid_workflow.json showcases the Switch Strategy utility nodes for external strategy control. Demonstrates using different switching strategies for T2V and I2V branches in a single workflow.

• Requires: rgthree-comfy custom nodes

Math Node Comparison: tripleksampler_vs_math.json demonstrates how to replicate TripleKSampler (Simple) behavior using manual math node calculations. This workflow provides a side-by-side comparison to help understand the internal calculations and validate the node's behavior.

• Requires: ComfyUI-Easy-Use and ComfyUI-Custom-Scripts

Known Limitations

WanVideoWrapper Integration

ComfyUI-WanVideoWrapper is explicitly a work-in-progress project that receives frequent updates and integrates new features regularly. TripleWVSampler nodes:

• Cannot be comprehensively tested with all WanVideoWrapper features
• Some advanced features may not behave correctly with cascaded sampling
• Some features may conflict with Lightning LoRA workflows
• Some features may require specific denoising schedules incompatible with triple-stage sampling
• May break with WanVideoWrapper updates that change the sampler interface

Before reporting issues with TripleWVSampler nodes: Always test with the original WanVideoSampler node first to confirm the issue is specific to TripleWVSampler and not an upstream WanVideoWrapper issue.

Support

• Issues - GitHub Issues
• Documentation - Project Wiki
• Updates - Changelog

License

Apache 2.0 License - see LICENSE file for details.

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Author: VraethrDalkr