kanibus-kanibus/examples

🎬 KANIBUS EXAMPLE WORKFLOWS

📋 Available Workflows

🚀 simple_eye_tracking.json

Basic eye tracking for testing installation

• Nodes: VideoFrameLoader → NeuralPupilTracker → PreviewImage
• Purpose: Test if Kanibus is working properly
• Performance: Fast processing, minimal GPU usage
• Use Case: First-time testing, debugging

⚡ wan21_basic_tracking.json

WAN 2.1 compatible workflow (480p)

• Resolution: 854x480 (WAN 2.1 optimized)
• FPS: 24 FPS target
• Features: Eye tracking + ControlNet scribble
• Use Case: Standard video generation with eye control
• Performance: Balanced speed and quality

🎯 wan22_advanced_full.json

WAN 2.2 full pipeline (720p)

• Resolution: 1280x720 (WAN 2.2 optimized)
• FPS: 30 FPS target
• Features: Full multi-modal pipeline
  • Eye tracking
  • Facial landmarks (468 points)
  • Depth estimation
  • Emotion analysis
  • Multi-ControlNet application
• Use Case: Professional content creation
• Performance: High quality, requires powerful GPU

🌟 advanced_integrated_workflow.json ⭐ NEW

Advanced integrated pipeline with dual WAN compatibility

• Resolution: Adaptive (720p default, 480p for WAN 2.1)
• FPS: 30 FPS target with temporal consistency
• Features: Complete integrated pipeline
  • Dual WAN 2.1/2.2 compatibility with auto-detection
  • High-precision eye tracking with 3D gaze estimation
  • 468-point facial landmarks
  • Real-time emotion analysis (7 basic + 15 micro-expressions)
  • Temporal frame consistency optimization
  • Multi-modal ControlNet integration
  • Performance monitoring and analytics
• Use Case: Enterprise-grade content creation and research
• Performance: Ultimate quality, requires 8GB+ GPU
• Documentation: See ADVANCED_WORKFLOW_GUIDE.md

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🛠️ How to Use Workflows

Method 1: Load in ComfyUI

1. Open ComfyUI
2. Click "Load" button
3. Navigate to ComfyUI/custom_nodes/kanibus/examples/
4. Select desired workflow JSON file
5. Click "Load"

Method 2: Drag & Drop

1. Open ComfyUI in browser
2. Drag the JSON file directly into the ComfyUI interface
3. Workflow will load automatically

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📋 Workflow Requirements

✅ All Workflows Require:

• Kanibus nodes installed and working
• 4 ControlNet models downloaded (~5.6GB)
  • control_v11p_sd15_scribble.pth
  • control_v11f1p_sd15_depth.pth
  • control_v11p_sd15_normalbae.pth
  • control_v11p_sd15_openpose.pth
• Base model (Stable Diffusion 1.5 or WAN compatible)
• VAE model (recommended: vae-ft-mse-840000-ema-pruned.safetensors)

🎯 Per Workflow:

simple_eye_tracking.json

• GPU Memory: 4GB+ recommended
• Models: Only Kanibus (MediaPipe auto-downloads)
• Input: Video file or webcam

wan21_basic_tracking.json

• GPU Memory: 6GB+ recommended
• Models: Base model + 2 ControlNet models minimum
• Input: Video file (will be resized to 480p)

wan22_advanced_full.json

• GPU Memory: 8GB+ recommended
• Models: Base model + all 4 ControlNet models
• Input: Video file (will be resized to 720p)
• Processing: Most demanding, highest quality

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🔧 Customization

Common Parameters to Adjust:

VideoFrameLoader Node:

"widgets_values": [
  "your_video.mp4",  // Video file path
  0,                 // Start frame  
  -1,                // Frame count (-1 = all)
  1,                 // Step (1 = every frame)
  30.0,              // Target FPS
  1280,              // Width (-1 = original)
  720,               // Height (-1 = original)
  "high",            // Quality: low/medium/high/original
  true,              // Enable caching
  8,                 // Batch size
  32,                // Preload frames
  4.0,               // Memory limit GB
  "RGB"              // Color space
]

NeuralPupilTracker Node:

"widgets_values": [
  1.0,     // Sensitivity (0.1-3.0)
  0.7,     // Smoothing (0.0-1.0)  
  0.25,    // Blink threshold (0.1-0.5)
  300.0,   // Saccade threshold (degrees/second)
  true,    // Enable 3D gaze
  true,    // Enable saccade detection
  true,    // Enable pupil dilation
  true     // Cache results
]

KanibusMaster Node:

"widgets_values": [
  "video",        // Input source: image/video/webcam
  "streaming",    // Pipeline mode: real_time/batch/streaming/analysis
  "auto_detect",  // WAN version: wan_2.1/wan_2.2/auto_detect
  30.0,           // Target FPS
  true,           // Enable GPU optimization
  // ... feature enables and weights
]

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🧪 Testing Workflows

Verification Steps:

1. Load workflow in ComfyUI
2. Check for red nodes (missing models/nodes)
3. Set input video path in VideoFrameLoader
4. Click "Queue Prompt"
5. Monitor progress in ComfyUI console

Troubleshooting:

• Red nodes: Missing models or nodes not installed
• Slow processing: Reduce batch size or resolution
• Out of memory: Lower quality settings or batch size
• No output: Check video file path and format

Performance Tips:

• Start with simple_eye_tracking.json to test installation
• Use wan21_basic_tracking.json for standard use
• Only use wan22_advanced_full.json with powerful GPUs
• Monitor GPU memory usage during processing

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📞 Support

If workflows don't work:

1. Run diagnostic: python test_installation.py
2. Check models: python download_models.py --check-only
3. Verify installation: Follow README.md installation guide
4. Get help: GitHub Issues

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Example workflows for Kanibus v1.0.0 - Eye Tracking ControlNet System