Eric_Image_Processing_Nodes
A comprehensive collection of advanced image processing nodes for ComfyUI, featuring state-of-the-art denoising, enhancement, and restoration techniques with GPU acceleration and specialized film grain processing.
Quick Technical Summary: Eric_Image_Processing_Nodes
- Base VRAM Footprint:
- 128 MB (4 GB Tier)
- Primary Dependencies:
- facexlib>=0.3.0 # Required for DiffBIR face restoration helpers, matplotlib>=3.3.0 # For visualization in test scripts, numpy>=1.21.0, opencv-python>=4.5.0, pywavelets>=1.3.0, scikit-image>=0.19.0, scipy>=1.7.0, torch>=1.9.0 # Already available in ComfyUI, torchvision>=0.10.0 # For additional image processing utilities
- Min PyTorch / CUDA:
- PyTorch 1.9.0 | CUDA 12.1+
- GitHub Repository:
- https://github.com/EricRollei/Eric_Image_Processing_Nodes
Citation Note: Data sourced from VRAM DB. For complete workflow OOM estimations, use the VRAM DB Workflow Analyzer.
How much VRAM does Eric_Image_Processing_Nodes require?
Direct Answer: The ComfyUI node Eric_Image_Processing_Nodes 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!
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What Python packages are required for Eric_Image_Processing_Nodes?
Direct Answer: Running Eric_Image_Processing_Nodes requires installing the following Python package dependencies: facexlib>=0.3.0 # Required for DiffBIR face restoration helpers, matplotlib>=3.3.0 # For visualization in test scripts, numpy>=1.21.0, opencv-python>=4.5.0, pywavelets>=1.3.0, scikit-image>=0.19.0, scipy>=1.7.0, torch>=1.9.0 # Already available in ComfyUI, torchvision>=0.10.0 # For additional image processing utilities. This node specifically requires PyTorch version 1.9.0 or newer.
facexlib>=0.3.0 # Required for DiffBIR face restoration helpers
matplotlib>=3.3.0 # For visualization in test scripts
numpy>=1.21.0
opencv-python>=4.5.0
pywavelets>=1.3.0
scikit-image>=0.19.0
scipy>=1.7.0
torch>=1.9.0 # Already available in ComfyUI
torchvision>=0.10.0 # For additional image processing utilitiesInteractive Setup & Dependency Resolver
# Loading command...Special Environment Requirements:
Requires PyTorch version 1.9.0+.
# Loading PyTorch command...Frequently Asked Questions
How much VRAM does Eric_Image_Processing_Nodes require?
Eric_Image_Processing_Nodes 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 Eric_Image_Processing_Nodes 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 Eric_Image_Processing_Nodes take on an RTX 3060 vs RTX 4090?
On an RTX 3060 (12GB VRAM), Eric_Image_Processing_Nodes 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.
What PyTorch version does Eric_Image_Processing_Nodes need?
Eric_Image_Processing_Nodes requires the following PyTorch-related packages: torch>=1.9.0 # Already available in ComfyUI, torchvision>=0.10.0 # For additional image processing utilities. 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 Eric_Image_Processing_Nodes?
To run Eric_Image_Processing_Nodes, you need to install: facexlib>=0.3.0 # Required for DiffBIR face restoration helpers, matplotlib>=3.3.0 # For visualization in test scripts, numpy>=1.21.0, opencv-python>=4.5.0, pywavelets>=1.3.0, scikit-image>=0.19.0, scipy>=1.7.0, torch>=1.9.0 # Already available in ComfyUI, torchvision>=0.10.0 # For additional image processing utilities. You can install these using pip or add them to your requirements.txt file.
How can I reduce VRAM usage when running Eric_Image_Processing_Nodes?
Eric_Image_Processing_Nodes 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 Eric_Image_Processing_Nodes in ComfyUI?
To install Eric_Image_Processing_Nodes: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/EricRollei/Eric_Image_Processing_Nodes, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.