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.
- All-in-one node - Select model, enter prompt, generate
- 20+ 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
Search for "comfyui-sdnq" → Install → Restart ComfyUI
cd ComfyUI/custom_nodes/
git clone https://github.com/EnragedAntelope/comfyui-sdnq.git
cd comfyui-sdnq && pip install -r requirements.txt- Add SDNQ Sampler node (under
sampling/SDNQ) - Select a model from dropdown (auto-downloads on first use)
- Enter your prompt → Queue Prompt → Done!
Hover over inputs for tooltips - all parameters are documented in the UI.
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
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.
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.
Model loading errors → Update libraries:
pip install --upgrade transformers diffusersNewest models (FLUX.2-klein, GLM-Image, Qwen-Image-2512, LTX-2) → Build diffusers from source:
pip install git+https://github.com/huggingface/diffusers.gitThis 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.
SDNQ by Disty0 - All quantization technology is developed and maintained by Disty0.