ComfyUI Extension: ComfyUI Execution Time Reporter

Authored by njlent

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A comprehensive performance monitoring and analysis tool for ComfyUI workflows that tracks execution time, memory usage, and system resource utilization.

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    README

    ComfyUI Execution Time Reporter

    A comprehensive performance monitoring and analysis tool for ComfyUI workflows that tracks execution time, memory usage, and system resource utilization.

    🚀 Features

    ⏱️ Execution Time Tracking

    • Precise timing for each node in your workflow
    • Execution order preservation with chronological numbering
    • Performance analysis with nodes ranked by execution time
    • Overhead calculation showing ComfyUI framework overhead

    🧠 Memory Monitoring

    • GPU Memory (VRAM) tracking - allocation changes and peak usage per node
    • System Memory (RAM) monitoring - process and system-wide memory usage
    • Memory leak detection - identify nodes that don't release memory properly
    • Peak usage identification - find memory bottlenecks in your workflow

    💻 System Information

    • Complete hardware profile - CPU, RAM, GPU specifications
    • Software environment - Python, PyTorch, CUDA, ComfyUI versions
    • Real-time resource usage - CPU usage, memory availability
    • GPU details - VRAM capacity, compute capability, current usage

    📊 Detailed Reports

    • Execution timeline with memory annotations
    • Performance rankings sorted by duration
    • Memory analysis with resource-intensive nodes highlighted
    • System overview for reproducibility and debugging

    📦 Installation

    1. Clone or download this repository into your ComfyUI custom nodes directory:

      cd ComfyUI/custom_nodes/
git clone https://github.com/njlent/ComfyUI_performance-report

    2. Restart ComfyUI - the node will be automatically loaded

    3. Dependencies (usually already available in ComfyUI):

      • psutil - for system monitoring
      • torch - for GPU memory tracking (optional)
      • comfy.model_management - for ComfyUI integration (optional)

    🎯 Usage

    Basic Usage

    1. Add the ExecutionTimeReporter node to any workflow
    2. Run your workflow - timing and memory tracking happens automatically
    3. Check the report in ComfyUI/output/reports/ after completion

    Advanced Usage

    • No connections required - the node works independently
    • Multiple workflows - each gets its own timestamped report
    • Memory optimization - use reports to identify resource bottlenecks
    • Performance tuning - compare different workflow configurations

    📋 Report Format

    System Information Section

    SYSTEM INFORMATION:
--------------------------------------------------------------------------------
Operating System: Windows 10
Architecture: AMD64
Processor: Intel64 Family 6 Model 142 Stepping 10, GenuineIntel
Python Version: 3.11.5 (CPython)
ComfyUI Version: 0.3.52
CPU Cores: 8 physical, 16 logical
CPU Usage: 45.2%
CPU Frequency: 2800 MHz (max: 4200 MHz)
System RAM: 32.0 GB total, 18.5 GB available (42.2% used)
PyTorch Version: 2.1.0
CUDA Version: 12.1
cuDNN Version: 8902
GPU Devices: 1
  GPU 0: NVIDIA GeForce RTX 4090
    VRAM: 24.0 GB
    Compute Capability: 8.9
    Multiprocessors: 128
    Current Usage: 2.34 GB allocated, 2.50 GB reserved

    Execution Timeline

    EXECUTION TIMES (in execution order):
--------------------------------------------------------------------------------
# 1 Node   4:    1.726s - VHS_LoadVideo [GPU: +0.125GB, Peak: 2.341GB]
# 2 Node  14:    0.001s - Get resolution [Crystools]
# 3 Node   2:   16.430s - YoloSmartTracker_Video [GPU: +1.847GB, Peak: 4.188GB, RAM: +0.234GB]
# 4 Node  12:    0.003s - PreviewImage
# 5 Node   6:    3.972s - VideoCombineTiles_Advanced [GPU: -0.512GB, Peak: 3.676GB]

    Performance Analysis

    PERFORMANCE ANALYSIS (sorted by duration, slowest first):
--------------------------------------------------------------------------------
 1. Node   2:   16.430s (65.2%) - YoloSmartTracker_Video
 2. Node   6:    3.972s (15.8%) - VideoCombineTiles_Advanced
 3. Node   8:    3.167s (12.6%) - VHS_VideoCombine

    Memory Analysis

    MEMORY ANALYSIS:
--------------------------------------------------------------------------------
GPU Memory Changes (nodes with significant changes):
  Node   2:   +1.847GB - YoloSmartTracker_Video
  Node   4:   +0.125GB - VHS_LoadVideo
  Node   6:   -0.512GB - VideoCombineTiles_Advanced

Peak GPU Memory Usage (top nodes):
  Node   2:    4.188GB - YoloSmartTracker_Video
  Node   6:    3.676GB - VideoCombineTiles_Advanced

    🔧 Technical Details

    Modular Architecture

    • __init__.py: Main module initialization, imports nodes and monitoring system
    • nodes.py: Contains the ExecutionTimeReporter node class and ComfyUI mappings
    • monitoring.py: Core monitoring system with execution patching and report generation
    • Clean separation: Easy to maintain, extend, and debug

    Memory Tracking

    • GPU Memory: Uses torch.cuda.memory_allocated() and torch.cuda.memory_reserved()
    • Peak Detection: torch.cuda.reset_peak_memory_stats() for per-node peak tracking
    • System Memory: psutil.Process().memory_info() for process-specific tracking
    • Smart Filtering: Only shows significant changes (>1MB GPU, >10MB RAM)

    Performance Impact

    • Minimal overhead - typically <0.1% of total execution time
    • Non-intrusive - doesn't modify your workflow logic
    • Memory efficient - cleans up tracking data after report generation

    Compatibility

    • ComfyUI versions: All recent versions
    • Operating Systems: Windows, Linux, macOS
    • GPU Support: NVIDIA CUDA, AMD ROCm (limited), CPU-only
    • Python versions: 3.8+

    📁 Output Location

    Reports are saved to: ComfyUI/output/reports/execution_report_YYYYMMDD-HHMMSS.txt

    💡 Best Practices

    Workflow Optimization

    1. Identify bottlenecks - Focus on nodes taking >10% of total time
    2. Memory management - Watch for nodes with large positive memory deltas
    3. Batch processing - Compare single vs batch processing performance
    4. Model loading - Track VRAM usage for different model combinations

    Performance Analysis

    • Compare configurations - Test different settings and compare reports
    • Monitor trends - Track performance changes over time
    • Resource planning - Use memory stats to plan hardware requirements
    • Debugging - Identify problematic nodes causing slowdowns or memory issues

    📊 Example Use Cases

    1. Video Processing Workflow

    Typical bottlenecks found:
- Video loading: High I/O time
- AI processing: High GPU memory usage
- Encoding: High CPU usage

    2. Image Generation Pipeline

    Common patterns:
- Model loading: Initial VRAM spike
- Sampling: Consistent GPU usage
- Post-processing: CPU-intensive

    3. Batch Processing Analysis

    Scaling insights:
- Memory usage per batch size
- Processing time vs quality trade-offs
- Optimal batch sizes for your hardware

    🐛 Troubleshooting

    Common Issues

    1. "No timing data found" - Ensure the ExecutionTimeReporter node is in your workflow
    2. Missing GPU stats - PyTorch with CUDA support required for GPU memory tracking
    3. Permission errors - Check write permissions for the output/reports directory
    4. Incomplete reports - Workflow must complete successfully for full report generation

    Debug Information

    The node provides console output for debugging:

    [ExecutionTimeReporter] Started tracking workflow abc123...
[ExecutionTimeReporter] Workflow completed - generating final report for 8 nodes...
[ExecutionTimeReporter] Report saved to: .../execution_report_20251008-231542.txt

    Performance Impact

    • Overhead: Typically <0.1% of total execution time
    • Memory: ~1-5MB additional RAM usage during tracking
    • Storage: Report files are typically 5-50KB depending on workflow size