ComfyUI workflows

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  1. ComfyUI Workflows: Server Hardware Configuration

This document details a high-performance server configuration specifically designed to run ComfyUI workflows efficiently. ComfyUI is a powerful, modular, and extensible GUI for Stable Diffusion and other generative AI models, and its node-based approach demands significant computational resources. This configuration aims to provide a balance between performance, stability, and cost-effectiveness for both individual researchers and small-to-medium-sized teams. We will cover hardware specifications, performance characteristics, recommended use cases, comparisons to alternative configurations, and essential maintenance considerations. This document assumes a reasonable level of technical understanding of server hardware and AI model operation. Refer to GPU Acceleration for more background on the importance of GPUs in these workloads.

1. Hardware Specifications

This configuration focuses on maximizing GPU performance while ensuring the other components don’t become bottlenecks. The parts list is current as of October 26, 2023, and prices are estimates. Availability may vary.

Component Specification Estimated Price (USD)
CPU AMD Ryzen Threadripper PRO 5975WX (32-core/64-thread) $2,400
CPU Cooler Noctua NH-U14S TR4-SP3 $110
Motherboard ASUS Pro WS WRX80E-SAGE SE WIFI $800
RAM 128GB (8 x 16GB) DDR4-3200 ECC Registered $600
Primary GPU NVIDIA GeForce RTX 4090 (24GB GDDR6X) $1,600
Secondary GPU (Optional) NVIDIA GeForce RTX 3090 (24GB GDDR6X) $900
Primary Storage (OS/ComfyUI) 2TB NVMe PCIe Gen4 SSD (Samsung 990 Pro) $180
Secondary Storage (Models/Checkpoints) 8TB HDD (Seagate IronWolf Pro) $200
Power Supply 1600W 80+ Titanium (Corsair AX1600i) $600
Case Fractal Design Define 7 XL $300
Network Card Intel X550-T2 10 Gigabit Ethernet $150
Operating System Ubuntu 22.04 LTS $0 (Free)

Detailed Component Breakdown:

  • CPU: The AMD Ryzen Threadripper PRO 5975WX provides a high core count essential for pre- and post-processing tasks within ComfyUI workflows, such as image upscaling, face restoration, and batch processing. While the GPU handles the core diffusion process, the CPU ensures data can be fed to the GPU efficiently. See CPU Architecture for a deeper dive.
  • Motherboard: The ASUS Pro WS WRX80E-SAGE SE WIFI is a workstation-grade motherboard designed for Threadripper PRO processors, offering robust power delivery, multiple PCIe slots for GPUs, and ample storage connectivity. Crucially, it supports ECC Registered RAM for enhanced data integrity.
  • RAM: 128GB of DDR4-3200 ECC Registered RAM provides ample memory for large ComfyUI workflows, especially those utilizing high-resolution images and complex node graphs. ECC RAM is critical for long-running tasks to prevent errors. Refer to Memory Technologies for details on ECC RAM.
  • GPU: The NVIDIA GeForce RTX 4090 is the current consumer-grade king of GPU performance, offering exceptional VRAM and processing power for Stable Diffusion. The 24GB of VRAM allows for larger batch sizes and higher resolution generation. A secondary RTX 3090 can be added for increased throughput and potentially distributing workflows (although ComfyUI's multi-GPU support is still evolving). See GPU Memory (VRAM) for more information.
  • Storage: A fast NVMe SSD is essential for the operating system and ComfyUI installation, significantly reducing load times. A large-capacity HDD provides ample storage for the ever-growing library of Stable Diffusion models and checkpoints.
  • Power Supply: A 1600W 80+ Titanium power supply is required to reliably power the high-wattage components, particularly the GPUs.
  • Networking: A 10 Gigabit Ethernet card facilitates fast data transfer for accessing models stored on a network-attached storage (NAS) device or collaborating with others. See Network Infrastructure for more details.



2. Performance Characteristics

The performance of this configuration is heavily dependent on the specific ComfyUI workflow being executed. However, we can provide some benchmark results and estimated real-world performance metrics.

  • Image Generation Speed (512x512, SDXL): Approximately 4-6 seconds per image with the RTX 4090, depending on the complexity of the workflow. Adding the RTX 3090 can reduce this to 2-4 seconds if properly utilized.
  • Image Generation Speed (768x768, SDXL): Approximately 8-12 seconds per image with the RTX 4090.
  • Image Generation Speed (1024x1024, SDXL): Approximately 15-20 seconds per image with the RTX 4090.
  • Image Upscaling (R-ESRGAN 4x+ Anime6B): Approximately 1-2 seconds per image.
  • Batch Processing (512x512, SDXL, Batch of 16): Approximately 60-90 seconds for the entire batch with the RTX 4090.

Benchmark Details:

These benchmarks were conducted using the following settings:

  • Stable Diffusion Model: SDXL 1.0
  • Sampler: DPM++ 2M Karras
  • Steps: 20
  • CFG Scale: 7
  • Resolution: Varied as indicated above
  • Operating System: Ubuntu 22.04 LTS with NVIDIA drivers 535.104.05
  • ComfyUI Version: Latest stable release (as of October 26, 2023)

Real-World Performance Considerations:

  • Workflow complexity significantly impacts performance. More nodes and complex operations will increase generation times.
  • VRAM usage is a critical factor. Workflows that exceed the GPU's VRAM will result in significantly slower performance due to data swapping to system RAM. See VRAM Management for optimization techniques.
  • CPU performance plays a role in pre- and post-processing tasks.
  • Storage speed affects model loading times and batch processing performance.

3. Recommended Use Cases

This configuration is ideal for the following use cases:

  • Professional Artists and Designers: Generating high-resolution images and complex animations for commercial projects.
  • Researchers: Experimenting with new Stable Diffusion models and workflows.
  • Small Teams: Collaborating on AI-generated content.
  • High-Volume Content Creation: Generating large batches of images for marketing, social media, or other purposes.
  • Complex Workflow Development: Building and testing intricate ComfyUI workflows with numerous nodes and dependencies.
  • Training and Fine-tuning (Limited): While not optimized for full model training, this configuration can handle fine-tuning smaller models or LoRAs. See Model Training Hardware for dedicated training configurations.



4. Comparison with Similar Configurations

Here's a comparison of this configuration with other potential options.

Configuration CPU GPU RAM Storage Estimated Price Performance Level
**ComfyUI Workflows (This Document)** Ryzen Threadripper PRO 5975WX RTX 4090 128GB DDR4 ECC 2TB NVMe + 8TB HDD $4,790 Very High
**High-End Desktop** Intel Core i9-13900K RTX 4090 64GB DDR5 2TB NVMe + 4TB HDD $3,800 High
**Mid-Range Desktop** Intel Core i7-13700K RTX 3090 64GB DDR4 1TB NVMe + 4TB HDD $2,500 Medium
**Workstation (Dual GPUs)** Intel Xeon W-3375 2x RTX 3090 128GB DDR4 ECC 4TB NVMe + 8TB HDD $6,500 Extremely High (potential bottleneck with PCIe bandwidth)

Comparison Notes:

  • The Intel Core i9-13900K offers excellent gaming performance, but the Threadripper PRO provides superior multi-core performance for ComfyUI workflows.
  • The RTX 3090 is a capable GPU, but the RTX 4090 offers significantly better performance, particularly with newer models like SDXL.
  • DDR5 RAM offers slightly higher bandwidth, but the benefits are often outweighed by the lower cost and higher capacity of DDR4 ECC RAM in this specific application.
  • The dual-GPU workstation configuration can offer exceptional performance, but it requires careful consideration of PCIe bandwidth limitations and software support for multi-GPU workflows in ComfyUI. See PCIe Bus Architecture for more information.



5. Maintenance Considerations

Maintaining this configuration requires attention to several key areas.

  • Cooling: The Threadripper PRO and RTX 4090 generate significant heat. Ensure adequate airflow within the case and consider liquid cooling for the CPU if overclocking. Regularly clean dust from the fans and heatsinks. See Thermal Management.
  • Power Requirements: The 1600W power supply provides ample headroom, but it's essential to use high-quality power cables and ensure the electrical circuit can handle the load.
  • Driver Updates: Keep the NVIDIA drivers updated to the latest stable version for optimal performance and compatibility.
  • Software Updates: Regularly update the operating system and ComfyUI software to benefit from bug fixes and performance improvements.
  • Storage Management: Monitor storage space and regularly back up important data, including models and workflows.
  • ECC Memory Checks: Periodically run memory diagnostics to check for errors in the ECC RAM.
  • Airflow and Dust Control: Implement a regular cleaning schedule to prevent dust buildup, which can significantly reduce cooling efficiency. Consider using electrostatic dust cloths.
  • Monitoring Tools: Utilize system monitoring tools (e.g., `htop`, `nvidia-smi`) to track CPU and GPU usage, temperature, and memory utilization. See System Monitoring Tools.
  • Power Surge Protection: Use a high-quality surge protector to protect the system from power fluctuations.



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Intel-Based Server Configurations

Configuration Specifications Benchmark
Core i7-6700K/7700 Server 64 GB DDR4, NVMe SSD 2 x 512 GB CPU Benchmark: 8046
Core i7-8700 Server 64 GB DDR4, NVMe SSD 2x1 TB CPU Benchmark: 13124
Core i9-9900K Server 128 GB DDR4, NVMe SSD 2 x 1 TB CPU Benchmark: 49969
Core i9-13900 Server (64GB) 64 GB RAM, 2x2 TB NVMe SSD
Core i9-13900 Server (128GB) 128 GB RAM, 2x2 TB NVMe SSD
Core i5-13500 Server (64GB) 64 GB RAM, 2x500 GB NVMe SSD
Core i5-13500 Server (128GB) 128 GB RAM, 2x500 GB NVMe SSD
Core i5-13500 Workstation 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000

AMD-Based Server Configurations

Configuration Specifications Benchmark
Ryzen 5 3600 Server 64 GB RAM, 2x480 GB NVMe CPU Benchmark: 17849
Ryzen 7 7700 Server 64 GB DDR5 RAM, 2x1 TB NVMe CPU Benchmark: 35224
Ryzen 9 5950X Server 128 GB RAM, 2x4 TB NVMe CPU Benchmark: 46045
Ryzen 9 7950X Server 128 GB DDR5 ECC, 2x2 TB NVMe CPU Benchmark: 63561
EPYC 7502P Server (128GB/1TB) 128 GB RAM, 1 TB NVMe CPU Benchmark: 48021
EPYC 7502P Server (128GB/2TB) 128 GB RAM, 2 TB NVMe CPU Benchmark: 48021
EPYC 7502P Server (128GB/4TB) 128 GB RAM, 2x2 TB NVMe CPU Benchmark: 48021
EPYC 7502P Server (256GB/1TB) 256 GB RAM, 1 TB NVMe CPU Benchmark: 48021
EPYC 7502P Server (256GB/4TB) 256 GB RAM, 2x2 TB NVMe CPU Benchmark: 48021
EPYC 9454P Server 256 GB RAM, 2x2 TB NVMe

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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️

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