200 lines
6.0 KiB
Markdown
200 lines
6.0 KiB
Markdown
# ComfyUI-Distributed
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> **Supercharge your ComfyUI workflows with distributed GPU processing**
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A powerful extension for [ComfyUI](https://github.com/comfyanonymous/ComfyUI) that enables distributed processing across multiple GPUs and machines. Speed up your image generation and upscaling workflows by leveraging all available GPU resources in your network.
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---
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## Features
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- **Distributed Processing** - Split workloads across multiple GPUs automatically
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- **Distributed Upscaling** - Tile-based upscaling with intelligent work distribution
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- **Automatic Worker Management** - Launch and monitor workers from the UI
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- **Network Support** - Use GPUs across different machines on your network
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- **Real-time Monitoring** - Track worker status and performance from the UI
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- **Easy Configuration** - JSON-based configuration with UI controls
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- **Memory Management** - Built-in VRAM clearing and optimization
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---
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## Requirements
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- ComfyUI installation
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- PyTorch with CUDA support
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- Python 3.8+
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## Installation
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1. **Clone this repository** into your ComfyUI custom nodes directory:
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```bash
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cd ComfyUI/custom_nodes
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git clone https://github.com/yourusername/ComfyUI-Distributed.git
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```
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2. **Restart ComfyUI** - the extension will create a default configuration file on first load
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---
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## Quick Start
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### Adding Local Workers
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1. **Open** the Distributed GPU panel (sidebar on the left)
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2. **Click** "Add Worker" in the UI
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3. **Configure** your worker:
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- **Name**: Descriptive name for the worker
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- **Host**: `localhost` for same machine
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- **Port**: Unique port number (e.g., 8189, 8190...)
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- **CUDA Device**: GPU index from `nvidia-smi`
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- **Extra Args**: Optional ComfyUI arguments
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4. **Save** and optionally launch the worker
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### Adding Remote Workers
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1. **Follow** the same steps as above, but:
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2. **Add** `--listen` to the 'Extra Args' field ⚠️ **Required!**
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3. **Open** the worker port in your firewall
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4. **Launch** ComfyUI on the remote machine
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5. **Add** the worker using the remote machine's IP address
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> **Tip**: If the remote machine has multiple GPUs, create multiple workers with different CUDA devices and ports
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### Configuration Tips
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| Setting | Description | Example |
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|---------|-------------|---------|
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| **CUDA Devices** | Use `nvidia-smi` to see GPU indices | 0, 1, 2... |
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| **Ports** | Each worker needs a unique port | 8189, 8190... |
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| **Extra Args** | Additional ComfyUI arguments | See below |
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**Common Extra Args:**
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- `--lowvram` - For GPUs with less memory
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- `--listen` - **Required** for remote workers
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- `--highvram` - For GPUs with 12GB+ VRAM
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---
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## Nodes
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### Distributed Collector
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> Collects and combines results from distributed processing
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**Usage**: Place after any image generation node to enable distributed processing. Works automatically when workers are enabled.
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### Distributed Seed
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> Ensures unique seeds across distributed workers for varied generation
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**Usage**: Connect to any seed input. Each worker automatically receives an offset seed for variety.
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### Ultimate SD Upscale Distributed
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> Distributed version of Ultimate SD Upscale that processes tiles across multiple GPUs
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**Usage**:
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1. Upscale your image with a regular upscale model (ESRGAN, etc.)
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2. Feed the upscaled image into this node
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3. Configure tile settings based on your VRAM
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4. Enable workers for faster processing
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---
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## UI Features
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### Distributed GPU Panel
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The control center for your distributed setup:
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| Feature | Description |
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|---------|-------------|
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| **Worker Status** | Real-time status indicators |
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| **Launch/Stop** | Control individual workers |
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| **Clear Memory** | Free VRAM on all workers |
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| **Interrupt** | Stop current processing |
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| **Auto-detect IP** | Find best network interface |
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| **Worker Logs** | View real-time logs |
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---
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## Workflow Examples
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### Basic Distributed Generation
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1. Create your normal ComfyUI workflow
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2. Add **Distributed Seed** → connect to sampler's seed
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3. Add **Distributed Collector** → after VAE Decode
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4. Enable workers in the UI
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5. Run the workflow!
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### Distributed Upscaling
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1. Load your image
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2. Upscale with ESRGAN or similar
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3. Connect to **Ultimate SD Upscale Distributed**
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4. Configure tile settings
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5. Enable workers for faster processing
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---
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## Troubleshooting
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### Common Issues
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<details>
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<summary><b>Workers won't start</b></summary>
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- Check ports are not in use: `netstat -an | grep 8189`
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- Verify CUDA device exists: `nvidia-smi`
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- Check ComfyUI path in worker logs
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</details>
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<details>
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<summary><b>"Worker not managed by UI" message</b></summary>
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- Worker was started outside the UI
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- Stop the worker manually and use the UI to relaunch
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</details>
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<details>
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<summary><b>Images not combining properly</b></summary>
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- Ensure all workers have the same models loaded
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- Check that custom nodes are installed on all workers
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</details>
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<details>
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<summary><b>Network connection issues</b></summary>
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- Check firewall settings for required ports
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- Verify master IP is accessible: `ping 192.168.1.100`
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- Ensure same ComfyUI version on all machines
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- Ensure ComfyUI-Distributed is installed on remote workers
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</details>
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### Debug Mode
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Enable detailed logging for troubleshooting:
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1. **UI Method**: Settings → Debug Mode → Enable
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2. **Check** console output for detailed logs
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---
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## Development
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This project is under active development. Contributions are welcome!
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### Planned Features
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- [ ] Remote worker control via SSH
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- [ ] View remote worker logs in UI
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- [ ] Improve worker timeout logic
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