Custom Graph Sigma for ComfyUI
Custom Graph Sigma is a ComfyUI custom node that provides an interactive spline-based curve editor for visually creating and exporting custom sigma schedules. This is especially useful for controlling the noise schedule or custom step values in diffusion models and other workflows that use a sequence of values over time or steps.
Quick Technical Summary: Custom Graph Sigma for ComfyUI
- Base VRAM Footprint:
- 128 MB (4 GB Tier)
- Primary Dependencies:
- None (Pure Python/Torch)
- Min PyTorch / CUDA:
- PyTorch 2.0+ | CUDA 12.1+
- GitHub Repository:
- https://github.com/JoeNavark/comfyui_custom_sigma_editor
Citation Note: Data sourced from VRAM DB. For complete workflow OOM estimations, use the VRAM DB Workflow Analyzer.
How much VRAM does Custom Graph Sigma for ComfyUI require?
Direct Answer: The ComfyUI node Custom Graph Sigma for ComfyUI requires a minimum base VRAM of 128MB and is optimized for GPUs with at least 4GB of VRAM. Low VRAM mode is fully supported for resource-constrained setups.
- Base VRAM:
- 128MB (0.1GB)
- Recommended GPU:
- 4GB+ VRAM
- Low VRAM Mode:
- ✓ Supported
- Estimation Confidence:
- MEDIUM
Cheapest VRAM Upgrade Paths (Live Market Prices):
- GeForce RTX 3060 12GB (Ultimate Budget VRAM King)──► Used: $209.62View eBay ↗
- GeForce RTX 4060 8GB (Modern Entry-Level)──► New: $303.50View Amazon ↗
Interactive VRAM Compatibility Estimator
Your GPU has plenty of headroom. You can run this node safely with your active configurations!
Verify Compatibility for Your Specific GPU VRAM
Select your graphics card's VRAM capacity to view optimized batch sizes, suggested resolutions, and custom performance tips for Custom Graph Sigma for ComfyUI:
Buy NVIDIA GeForce RTX 3060 (12GB VRAM)
Tired of renting cloud rigs? Run ComfyUI locally with absolute zero latency. Best entry-level ComfyUI experience. Avoids immediate VRAM limitations on basic LoRA training.
Are you the author of this node?
Help your users avoid out-of-memory errors by displaying this professional, dynamic VRAM badge on your GitHub README. Copy the markdown below to embed it with a backlink directly to this hardware specification profile.
Frequently Asked Questions
How much VRAM does Custom Graph Sigma for ComfyUI require?
Custom Graph Sigma for ComfyUI requires a minimum of 128MB (0.1GB) of VRAM for base operation. For optimal performance, a GPU with at least 4GB of VRAM is recommended. This node supports low VRAM mode for resource-constrained setups.
Can I run Custom Graph Sigma for ComfyUI on an RTX 3060, RTX 4070, or RTX 4090?
✅ RTX 3060 (12GB): Yes, fully compatible with 10.7GB headroom. ✅ RTX 4070 (12GB): Yes, fully compatible with 10.7GB headroom. ✅ RTX 4070 Ti (16GB): Yes, fully compatible with 14.3GB headroom. ✅ RTX 4090 (24GB): Yes, fully compatible with 21.5GB headroom
How much VRAM does Custom Graph Sigma for ComfyUI take on an RTX 3060 vs RTX 4090?
On an RTX 3060 (12GB VRAM), Custom Graph Sigma for ComfyUI runs smoothly on an RTX 3060 (12GB) with 10.7GB of headroom. This is sufficient to run the node alongside standard SD 1.5 and SDXL workflows in full precision. On an RTX 4090 (24GB VRAM), the node runs with extreme headroom on an RTX 4090 (24GB) with 21.5GB of dedicated headroom. This allows you to combine the node with massive models (like FLUX.1 Dev, Schnell, or Hunyuan Video) in full precision (FP16) without any offload flags.
How can I reduce VRAM usage when running Custom Graph Sigma for ComfyUI?
Custom Graph Sigma for ComfyUI supports low VRAM mode. To reduce memory usage: (1) Enable --lowvram or --medvram flags in ComfyUI, (2) Reduce batch size to 1, (3) Use fp16 or fp8 precision if supported, (4) Close other GPU applications.
How do I install Custom Graph Sigma for ComfyUI in ComfyUI?
To install Custom Graph Sigma for ComfyUI: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/JoeNavark/comfyui_custom_sigma_editor, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.