docs; readme
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RyanOnTheInside
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# ComfyUI ProfilerX
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> **v2.0 — Fully rewritten for modern ComfyUI!** The old version broke when ComfyUI moved to async execution. This rewrite uses ComfyUI's official `ProgressHandler` API instead of monkey-patching, so it's stable and forward-compatible. If you tried ProfilerX before and hit crashes, update and try again — it works now.
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A performance profiling suite for ComfyUI that automatically tracks execution time, memory usage, and cache performance of your workflows.
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## Features
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- 📊 Memory usage tracking (VRAM and RAM)
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- ⚡ Node execution time breakdown
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- 💾 Cache hit/miss statistics
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- 📈 Beautiful, interactive charts and tables
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- 📈 Historical data tracking and analysis with standard deviation
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- 🎯 Zero configuration required
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- 📱 Responsive UI that integrates with ComfyUI's interface
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- 📊 Historical data tracking and analysis
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- ⚙️ Time-range filtering for analytics
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- 🔍 Sortable performance tables
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- 🍎 Apple Silicon (MPS) support
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## Requirements
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- ComfyUI (latest version)
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- Python 3.8+
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- CUDA-capable GPU (for VRAM monitoring)
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- GPU for VRAM monitoring (CUDA or Apple Silicon MPS; CPU-only systems track RAM only)
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- Modern web browser
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## Installation
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The profiler integrates directly into ComfyUI's interface:
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1. After installation, you'll see a new performance monitoring panel in the top-right corner
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1. After installation, you'll see a chart icon button in the top menu bar
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2. Run your workflows as normal
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3. The dashboard will automatically update with:
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- Total execution time
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3. Click the icon to open the dashboard, which shows:
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- Total execution time with average and standard deviation
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- Peak memory usage (VRAM and RAM)
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- Cache performance metrics
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- Cache performance metrics (hits/misses)
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- Per-node execution time breakdown
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- Historical performance trends
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- Node analytics with stddev across runs
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<img src="https://github.com/user-attachments/assets/4dd514a4-8d52-4047-9f2c-3588a669e2c9" alt="ProfilerX Interface" width="600">
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## Features in Detail
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### Real-time Monitoring
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- Live execution time tracking
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- Memory usage graphs (VRAM and RAM)
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- Cache hit/miss counters
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- Node-by-node progress tracking
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### Latest Run
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- Per-node execution time, VRAM delta, RAM delta
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- Cache hit/miss status per node
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- Workflow totals with averages and ±stddev
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### Analytics Dashboard
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- Historical performance trends
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- Node execution time breakdown
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- Memory usage patterns
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- Cache efficiency analysis
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### Node Analytics
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- Average execution time and VRAM per node type
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- Standard deviation columns for time and VRAM
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- Cache hit rate per node type
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- Time-range filtering
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- Sortable performance tables
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- Sortable columns
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### Historical Trends
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- Recent workflow runs with duration, VRAM peak, cache rate
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- Time-range filtering (1h, 6h, 24h, 7d, all)
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- Overall workflow averages
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### Data Management
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- Automatic history tracking
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- Automatic history tracking (up to 10,000 runs)
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- Data persistence between sessions
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- Archive management
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- Export capabilities
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- Archive create/load/delete from the Settings tab
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- Auto-archive when history limit is reached
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## How it Works
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ProfilerX integrates directly with ComfyUI's execution system to collect performance metrics:
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ProfilerX uses ComfyUI's official `ProgressHandler` API to collect performance metrics with minimal overhead:
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- Execution time is measured for both individual nodes and the entire workflow
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- Memory usage is tracked using `torch.cuda` for VRAM and `psutil` for RAM
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- Cache performance is monitored by intercepting ComfyUI's caching system
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- All data is collected automatically with minimal performance impact
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- Historical data is stored locally for trend analysis
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- A single small patch re-injects the profiler handler when ComfyUI resets progress state for each execution
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- `start_handler` / `finish_handler` callbacks measure per-node timing and memory
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- Cache hits are detected automatically (nodes that receive `finish` without `start`)
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- VRAM is tracked via `torch.cuda` (NVIDIA) or `torch.mps` (Apple Silicon), RAM via `psutil`
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- Standard deviation is computed incrementally using Welford's online algorithm
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- Historical data is stored locally in `data/profiling_history.json`
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## Execution Tracking
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In addition to workflow profiling, this extension includes a detailed execution tracking system that monitors ComfyUI's internal method calls. This can be useful for:
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- Understanding the execution flow of your workflows
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- Identifying bottlenecks in specific operations
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- Debugging performance issues
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- Analyzing method call patterns and timing
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### Enabling Execution Tracking
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By default, execution tracking is disabled. To enable it:
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1. Open `ComfyUI_ProfilerX/execution_core.py`
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2. Find the `ENABLED` flag at the top of the `ExecutionTracker` class:
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```python
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class ExecutionTracker:
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_instance = None
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_lock = threading.Lock()
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ENABLED = False # Change this to True to enable tracking
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```
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3. Change `ENABLED = False` to `ENABLED = True`
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4. Restart ComfyUI
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When enabled, the tracker will record detailed timing information for internal ComfyUI operations in `ComfyUI_ProfilerX/data/method_traces.json`.
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## REST API
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ProfilerX exposes endpoints for the frontend (also usable by scripts):
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- `GET /profilerx/stats` — latest run, history, node averages, workflow averages
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- `GET /profilerx/archives` — list archive files
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- `POST /profilerx/archive` — archive current history
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- `POST /profilerx/archive/{filename}/load` — load an archive
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- `DELETE /profilerx/archive/{filename}` — delete an archive
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## Other Projects by RyanOnTheInside
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@@ -134,4 +124,3 @@ Contributions are welcome! Please feel free to submit a Pull Request. For major
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## License
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MIT License - feel free to use this in your own projects!
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