ggf-ltp-zimage

By gjnaveView on GitHub →

ComfyUI custom nodes for running L2P Z-Image 6B pixel-space generation, wrapping the public pipeline to enable direct no-VAE model inference.

How much VRAM does ggf-ltp-zimage require?

Direct Answer: The ComfyUI node ggf-ltp-zimage 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!

Deploy on High-Performance GPUs

Need more VRAM to run ComfyUI with this node? Rent low-cost, high-performance GPUs on Vast.ai instantly.

🚀 Deploy on Vast.ai

Deploy on Cloud GPUs

Need more VRAM to run ComfyUI with this node? Rent low-cost, high-performance cloud GPUs on RunPod instantly.

🚀 Deploy on RunPod

What Python packages are required for ggf-ltp-zimage?

Direct Answer: Running ggf-ltp-zimage requires installing the following Python package dependencies: accelerate, einops, ftfy, huggingface_hub, imageio, imageio-ffmpeg, peft, protobuf, safetensors, sentencepiece, torchvision, transformers. Ensure your ComfyUI environment has these packages active before launching.

requirements.txt
accelerate
einops
ftfy
huggingface_hub
imageio
imageio-ffmpeg
peft
protobuf
safetensors
sentencepiece
torchvision
transformers

Interactive Setup & Dependency Resolver

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

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Frequently Asked Questions

How much VRAM does ggf-ltp-zimage require?

ggf-ltp-zimage 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 ggf-ltp-zimage 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 ggf-ltp-zimage need?

ggf-ltp-zimage requires the following PyTorch-related packages: torchvision. 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 ggf-ltp-zimage?

To run ggf-ltp-zimage, you need to install: accelerate, einops, ftfy, huggingface_hub, imageio, imageio-ffmpeg, peft, protobuf, safetensors, sentencepiece, torchvision, transformers. You can install these using pip or add them to your requirements.txt file.

How can I reduce VRAM usage when running ggf-ltp-zimage?

ggf-ltp-zimage 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 ggf-ltp-zimage in ComfyUI?

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