ComfyUI LoRA Block Weight Loader

By bhvbhushanView on GitHub →

Advanced LoRA loader with per-block weight control for fine-grained influence over different model layers in ComfyUI

How much VRAM does ComfyUI LoRA Block Weight Loader require?

Direct Answer: The ComfyUI node ComfyUI LoRA Block Weight Loader 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.

High (2-4GB)
Base VRAM:
128MB (0.1GB)
Recommended GPU:
4GB+ VRAM
Low VRAM Mode:
✓ Supported
Estimation Confidence:
MEDIUM

Interactive VRAM Compatibility Estimator

Estimated Total VRAM: 3.00 GBTarget: 8 GB
✅ Comfortable Fit

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 ComfyUI LoRA Block Weight Loader?

Direct Answer: Running ComfyUI LoRA Block Weight Loader requires installing the following Python package dependencies: numpy>=1.19.0, torch>=2.0.0. This node specifically requires PyTorch version 2.0.0 or newer.

requirements.txt
numpy>=1.19.0
torch>=2.0.0

Interactive Setup & Dependency Resolver

Operating System:
Environment Type:
Run this terminal command in your ComfyUI root folder:
# Loading command...

Special Environment Requirements:

Requires PyTorch version 2.0.0+.

To update PyTorch for your selected setup, run:
# Loading PyTorch command...

Related Custom Nodes

The following custom nodes run in the same PyTorch / CUDA environment and share similar categories or workflows:

ComfyUI-Light-N-Color
by KAVVATARE128MB VRAM
ComfyUI Latent Transform Sampler
by pizurny128MB VRAM
ComfyUI-11labs
by gabe-init256MB VRAM
ComfyUI-DreamCube
by cedarconnor128MB VRAM

Frequently Asked Questions

How much VRAM does ComfyUI LoRA Block Weight Loader require?

ComfyUI LoRA Block Weight Loader 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 ComfyUI LoRA Block Weight Loader 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

What PyTorch version does ComfyUI LoRA Block Weight Loader need?

ComfyUI LoRA Block Weight Loader requires the following PyTorch-related packages: torch>=2.0.0. 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 LoRA Block Weight Loader?

To run ComfyUI LoRA Block Weight Loader, you need to install: numpy>=1.19.0, torch>=2.0.0. You can install these using pip or add them to your requirements.txt file.

How can I reduce VRAM usage when running ComfyUI LoRA Block Weight Loader?

ComfyUI LoRA Block Weight Loader 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 LoRA Block Weight Loader in ComfyUI?

To install ComfyUI LoRA Block Weight Loader: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/bhvbhushan/ComfyUI-LoRABlockWeight, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.