MD Nodes
A wild collection of custom nodes for ComfyUI including noise schedulers, samplers, audio preview, latent visualizers, and more — built for maximal creative chaos.
Quick Technical Summary: MD Nodes
- Base VRAM Footprint:
- 1536 MB (6 GB Tier)
- Primary Dependencies:
- bitsandbytes, google-api-python-client, google-auth, h5py, hdf5plugin, huggingface_hub, imageio, librosa, lion_pytorch, lmstudio, loguru, lpips, matplotlib, natsort, ollama, pedalboard, piexif, prodigyopt, pykakasi, pyloudnorm, pynvml, pytorch_lightning, rich, soundfile, stdlib_list, tomli, tomli_w, torchcodec
- Min PyTorch / CUDA:
- PyTorch 2.0+ | CUDA 12.1+
- GitHub Repository:
- https://github.com/MDMAchine/ComfyUI_MD_Nodes
Citation Note: Data sourced from VRAM DB. For complete workflow OOM estimations, use the VRAM DB Workflow Analyzer.
How much VRAM does MD Nodes require?
Direct Answer: The ComfyUI node MD Nodes requires a minimum base VRAM of 1536MB and is optimized for GPUs with at least 6GB of VRAM. Low VRAM mode is fully supported for resource-constrained setups.
- Base VRAM:
- 1536MB (1.5GB)
- Recommended GPU:
- 6GB+ VRAM
- Low VRAM Mode:
- ✓ Supported
- Estimation Confidence:
- MEDIUM
Cheapest VRAM Upgrade Paths (Live Market Prices):
- GeForce RTX 3060 12GB (Ultimate Budget VRAM King)──► Used: $209.62View eBay ↗
- GeForce RTX 4060 8GB (Modern Entry-Level)──► New: $303.50View Amazon ↗
Interactive VRAM Compatibility Estimator
Your GPU has plenty of headroom. You can run this node safely with your active configurations!
Verify Compatibility for Your Specific GPU VRAM
Select your graphics card's VRAM capacity to view optimized batch sizes, suggested resolutions, and custom performance tips for MD Nodes:
Buy NVIDIA GeForce RTX 3060 (12GB VRAM)
Tired of renting cloud rigs? Run ComfyUI locally with absolute zero latency. Best entry-level ComfyUI experience. Avoids immediate VRAM limitations on basic LoRA training.
Are you the author of this node?
Help your users avoid out-of-memory errors by displaying this professional, dynamic VRAM badge on your GitHub README. Copy the markdown below to embed it with a backlink directly to this hardware specification profile.
What Python packages are required for MD Nodes?
Direct Answer: Running MD Nodes requires installing the following Python package dependencies: bitsandbytes, google-api-python-client, google-auth, h5py, hdf5plugin, huggingface_hub, imageio, librosa, lion_pytorch, lmstudio, loguru, lpips, matplotlib, natsort, ollama, pedalboard, piexif, prodigyopt, pykakasi, pyloudnorm, pynvml, pytorch_lightning, rich, soundfile, stdlib_list, tomli, tomli_w, torchcodec. Ensure your ComfyUI environment has these packages active before launching.
bitsandbytes
google-api-python-client
google-auth
h5py
hdf5plugin
huggingface_hub
imageio
librosa
lion_pytorch
lmstudio
loguru
lpips
matplotlib
natsort
ollama
pedalboard
piexif
prodigyopt
pykakasi
pyloudnorm
pynvml
pytorch_lightning
rich
soundfile
stdlib_list
tomli
tomli_w
torchcodecInteractive Setup & Dependency Resolver
# Loading command...Frequently Asked Questions
How much VRAM does MD Nodes require?
MD Nodes requires a minimum of 1536MB (1.5GB) of VRAM for base operation. For optimal performance, a GPU with at least 6GB of VRAM is recommended. This node supports low VRAM mode for resource-constrained setups.
Can I run MD Nodes on an RTX 3060, RTX 4070, or RTX 4090?
✅ RTX 3060 (12GB): Yes, fully compatible with 9.3GB headroom. ✅ RTX 4070 (12GB): Yes, fully compatible with 9.3GB headroom. ✅ RTX 4070 Ti (16GB): Yes, fully compatible with 12.9GB headroom. ✅ RTX 4090 (24GB): Yes, fully compatible with 20.1GB headroom
How much VRAM does MD Nodes take on an RTX 3060 vs RTX 4090?
On an RTX 3060 (12GB VRAM), MD Nodes runs smoothly on an RTX 3060 (12GB) with 9.3GB of headroom. This is sufficient to run the node alongside standard SD 1.5 and SDXL workflows in full precision. On an RTX 4090 (24GB VRAM), the node runs with extreme headroom on an RTX 4090 (24GB) with 20.1GB of dedicated headroom. This allows you to combine the node with massive models (like FLUX.1 Dev, Schnell, or Hunyuan Video) in full precision (FP16) without any offload flags.
What PyTorch version does MD Nodes need?
MD Nodes requires the following PyTorch-related packages: lion_pytorch, pytorch_lightning, torchcodec. 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 MD Nodes?
To run MD Nodes, you need to install: bitsandbytes, google-api-python-client, google-auth, h5py, hdf5plugin, huggingface_hub, imageio, librosa, lion_pytorch, lmstudio, loguru, lpips, matplotlib, natsort, ollama, pedalboard, piexif, prodigyopt, pykakasi, pyloudnorm, pynvml, pytorch_lightning, rich, soundfile, stdlib_list, tomli, tomli_w, torchcodec. You can install these using pip or add them to your requirements.txt file.
How can I reduce VRAM usage when running MD Nodes?
MD Nodes 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 MD Nodes in ComfyUI?
To install MD Nodes: (1) Navigate to your ComfyUI/custom_nodes directory, (2) Clone the repository: git clone https://github.com/MDMAchine/ComfyUI_MD_Nodes, (3) Install dependencies: pip install -r requirements.txt (if present), (4) Restart ComfyUI. Alternatively, use ComfyUI Manager for one-click installation.