ComfyUI Extension: comfyui-sdnq

Authored by EnragedAntelope

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Updated

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Run ComfyUI workflows without the setup

No installs, no CUDA version roulette, no GPU sitting idle on your bill. Bring a workflow and run it in the browser.

ComfyUI custom node pack for loading SDNQ quantized models

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    README

    ComfyUI-SDNQ

    Load and run SDNQ quantized models in ComfyUI with 50-75% VRAM savings!

    Run large models like FLUX.2, FLUX.1, SD3.5, Qwen-Image, and more on consumer hardware with significantly reduced VRAM requirements.

    <img width="2315" height="1121" alt="image" src="https://github.com/user-attachments/assets/4011ab36-e197-4543-8d7f-cf90ee4dee82" />

    Features

    • All-in-one node - Select model, enter prompt, generate
    • 30 pre-configured models with auto-download from HuggingFace
    • 50-75% VRAM savings with SDNQ quantization
    • Memory modes: GPU (fastest), balanced (12-16GB), lowvram (8GB)
    • LoRA support, image editing, 14 schedulers
    • Performance options: Triton acceleration, xFormers, VAE tiling

    Installation

    ComfyUI Manager (Recommended)

    Search for "comfyui-sdnq" → Install → Restart ComfyUI

    Manual

    cd ComfyUI/custom_nodes/
git clone https://github.com/EnragedAntelope/comfyui-sdnq.git
cd comfyui-sdnq && pip install -r requirements.txt

    Quick Start

    1. Add SDNQ Sampler node (under sampling/SDNQ)
    2. Select a model from dropdown (auto-downloads on first use)
    3. Enter your prompt → Queue Prompt → Done!

    Hover over inputs for tooltips - all parameters are documented in the UI.

    Models

    30+ pre-quantized models available: FLUX.1, FLUX.2, Qwen-Image (including 2512 Dec update), Z-Image, GLM-Image, LTX-2 video, and more.

    Browse all models: Disty0's SDNQ Collection

    Video Models (Experimental)

    LTX-2 video models are now supported. Set num_frames > 1 for video generation. Output is a batch of images (frames) that can be connected to video export nodes.

    Performance

    For best speed (30-80% faster), install Triton:

    • Linux: pip install triton
    • Windows: pip install triton-windows

    Triton enables optimized quantized matmul operations. Enabled by default when available.

    Scheduler tip: Use FlowMatchEulerDiscreteScheduler for FLUX/SD3/Qwen. Use DPMSolverMultistepScheduler for SDXL/SD1.5.

    Troubleshooting

    Model loading errors → Update libraries:

    pip install --upgrade transformers diffusers

    Newest models (FLUX.2-klein, GLM-Image, Qwen-Image-2512, LTX-2) → Build diffusers from source:

    pip install git+https://github.com/huggingface/diffusers.git

    This ensures you have the latest pipeline support for cutting-edge models.

    Out of memory → Try balanced or lowvram memory mode, or use uint4 models.

    Slow performance → Install Triton (see above), or try use_xformers=True.

    Credits

    SDNQ by Disty0 - All quantization technology is developed and maintained by Disty0.

    Run ComfyUI workflows without the setup

    No installs, no CUDA version roulette, no GPU sitting idle on your bill. Bring a workflow and run it in the browser.

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