Save Checkpoint with Metadata

By alexds9View on GitHub →

Save checkpoint .safetensors with custom header JSON, optional prompt override, merge of EXTRA_PNGINFO, and smart/no-counter filename modes. Includes text outputs for path and final metadata.

How much VRAM does Save Checkpoint with Metadata require?

Direct Answer: The ComfyUI node Save Checkpoint with Metadata 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.

High (2-4GB)
Base VRAM:
256MB (0.3GB)
Recommended GPU:
4GB+ VRAM
Low VRAM Mode:
✓ Supported
Estimation Confidence:
HIGH

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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Related Custom Nodes

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

AITocha Vision
by aitocha128MB VRAM
AI_Text_Comfyui
by Zch6111256MB VRAM
AK XZ Axis (XY for any KSampler)
by akawana256MB VRAM
ArtyClaw Comfy Nodes
by artyclaw256MB VRAM

Frequently Asked Questions

How much VRAM does Save Checkpoint with Metadata require?

Save Checkpoint with Metadata 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 Save Checkpoint with Metadata 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 can I reduce VRAM usage when running Save Checkpoint with Metadata?

Save Checkpoint with Metadata 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 Save Checkpoint with Metadata in ComfyUI?

To install Save Checkpoint with Metadata: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/a-l-e-x-d-s-9/ComfyUI-SaveCheckpointWithMetadata, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.