ComfyUI S4Tool Image
A custom node package for image processing in ComfyUI, for automating image processing within the ComfyUI workflow.
Quick Technical Summary: ComfyUI S4Tool Image
- Base VRAM Footprint:
- 256 MB (4 GB Tier)
- Primary Dependencies:
- addict, huggingface_hub>=0.15.0, numpy>=1.21.0, opencv-contrib-python>=4.5.0, pillow>=8.0.0, pyyaml>=6.0, safetensors>=0.3.0, scikit-image>=0.19.0, scikit-learn>=1.0.0, scipy>=1.8.0, segment_anything, timm, torch>=2.0.0, torchvision>=0.15.0, transformers>=4.20.0
- Min PyTorch / CUDA:
- PyTorch 2.0.0 | CUDA 12.1+
- GitHub Repository:
- https://github.com/S4MUEL-404/ComfyUI-S4Tool-Image
Citation Note: Data sourced from VRAM DB. For complete workflow OOM estimations, use the VRAM DB Workflow Analyzer.
How much VRAM does ComfyUI S4Tool Image require?
Direct Answer: The ComfyUI node ComfyUI S4Tool Image requires a minimum base VRAM of 256MB and is optimized for GPUs with at least 4GB of VRAM. Low VRAM mode is fully supported for resource-constrained setups.
- Base VRAM:
- 256MB (0.3GB)
- Recommended GPU:
- 4GB+ VRAM
- Low VRAM Mode:
- ✓ Supported
- Estimation Confidence:
- HIGH
Interactive VRAM Compatibility Estimator
Your GPU has plenty of headroom. You can run this node safely with your active configurations!
Struggling with VRAM Crashes or Out-of-Memory Errors?
Running heavy models like FLUX or SDXL with ComfyUI S4Tool Image? If you experience torch.cuda.OutOfMemoryError or driver timeouts, read our comprehensive troubleshooting guide to configure launch flags, enable tiled decoding, and avoid crashes.
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 ComfyUI S4Tool Image:
What Python packages are required for ComfyUI S4Tool Image?
Direct Answer: Running ComfyUI S4Tool Image requires installing the following Python package dependencies: addict, huggingface_hub>=0.15.0, numpy>=1.21.0, opencv-contrib-python>=4.5.0, pillow>=8.0.0, pyyaml>=6.0, safetensors>=0.3.0, scikit-image>=0.19.0, scikit-learn>=1.0.0, scipy>=1.8.0, segment_anything, timm, torch>=2.0.0, torchvision>=0.15.0, transformers>=4.20.0. This node specifically requires PyTorch version 2.0.0 or newer.
addict
huggingface_hub>=0.15.0
numpy>=1.21.0
opencv-contrib-python>=4.5.0
pillow>=8.0.0
pyyaml>=6.0
safetensors>=0.3.0
scikit-image>=0.19.0
scikit-learn>=1.0.0
scipy>=1.8.0
segment_anything
timm
torch>=2.0.0
torchvision>=0.15.0
transformers>=4.20.0Interactive Setup & Dependency Resolver
# Loading command...Special Environment Requirements:
Requires PyTorch version 2.0.0+.
# Loading PyTorch command...Universal Architecture Compatibility
ComfyUI S4Tool Image operates as a general-purpose processor without binding directly to a single checkpoint architecture. It can be paired across Stable Diffusion 1.5, SDXL, FLUX, and video diffusion pipelines.
Frequently Asked Questions
How much VRAM does ComfyUI S4Tool Image require?
ComfyUI S4Tool Image requires a minimum of 256MB (0.3GB) 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 ComfyUI S4Tool Image on an RTX 3060, RTX 4070, or RTX 4090?
✅ RTX 3060 (12GB): Yes, fully compatible with 10.6GB headroom. ✅ RTX 4070 (12GB): Yes, fully compatible with 10.6GB headroom. ✅ RTX 4070 Ti (16GB): Yes, fully compatible with 14.2GB headroom. ✅ RTX 4090 (24GB): Yes, fully compatible with 21.4GB headroom
How much VRAM does ComfyUI S4Tool Image take on an RTX 3060 vs RTX 4090?
On an RTX 3060 (12GB VRAM), ComfyUI S4Tool Image runs smoothly on an RTX 3060 (12GB) with 10.6GB 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 21.4GB 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.
What PyTorch version does ComfyUI S4Tool Image need?
ComfyUI S4Tool Image requires the following PyTorch-related packages: torch>=2.0.0, torchvision>=0.15.0. Ensure your ComfyUI environment has these installed. Ensure your PyTorch installation matches your CUDA version (use torch.version.cuda to check).
What Python packages are required for ComfyUI S4Tool Image?
To run ComfyUI S4Tool Image, you need to install: addict, huggingface_hub>=0.15.0, numpy>=1.21.0, opencv-contrib-python>=4.5.0, pillow>=8.0.0, pyyaml>=6.0, safetensors>=0.3.0, scikit-image>=0.19.0, scikit-learn>=1.0.0, scipy>=1.8.0, segment_anything, timm, torch>=2.0.0, torchvision>=0.15.0, transformers>=4.20.0. You can install these using pip or add them to your requirements.txt file.
How can I reduce VRAM usage when running ComfyUI S4Tool Image?
ComfyUI S4Tool Image 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 ComfyUI S4Tool Image in ComfyUI?
To install ComfyUI S4Tool Image: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/S4MUEL-404/ComfyUI-S4Tool-Image, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.
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