Lmcq Load Flux NF4 Checkpoint

If you downloaded an NF4 version of Flux and dropped it into CheckpointLoaderSimple only to get a wall of tensor-shape errors, that's not a bad download - it's the wrong loader. NF4 checkpoints aren't stored the same way a normal safetensors checkpoint is, so ComfyUI's stock loader doesn't know what to do with the packed 4-bit weights. LmcqLoadFluxNF4Checkpoint exists purely to fill that gap: point it at the file, and it hands you back the same MODEL/CLIP/VAE trio you'd get from any other checkpoint loader, just decoded correctly.

What NF4 actually is. Flux Dev is a 12B-parameter model, and full fp16 weights don't fit on most consumer cards. NF4 (4-bit NormalFloat, via bitsandbytes) is the most aggressive of the common compression options - it packs the transformer, text encoders, and VAE into a single all-in-one file and was popularized by lllyasviel's Forge UI as the way to run Flux on genuinely small cards. People got it running on a GTX 1060 6GB, even a GTX 970, at a few minutes an image. That's the whole appeal: it's the format that gets Flux onto hardware nothing else touches.

The catch, and why you might not want it. NF4 is also the lowest-quality of the mainstream quantization options. Community consensus by now is pretty settled: fp8 is near-lossless at half the VRAM of fp16 and is the default recommendation for Flux; GGUF Q8 tests out as basically identical to fp16; NF4 is the one people reach for only when nothing else fits. It's also had its moment - chatter about NF4 Flux spiked hard at launch (over 400 mentions the month Forge shipped it) and has been trickling toward zero ever since as GGUF and fp8 took over the low-VRAM conversation. If you've got 10GB+ of VRAM, a GGUF Q5-Q8 or fp8 checkpoint will look better for a similar footprint. NF4 is the tool for "I have 6-8GB and nothing else works."

Using the node. There's exactly one input - ckpt_name, a dropdown of NF4 checkpoints sitting in your models/checkpoints folder - and three outputs: MODEL, CLIP, VAE, wired the same way you'd wire any checkpoint loader into a KSampler, CLIP Text Encode, and VAE Decode. There's nothing else to configure; it's a drop-in replacement, not a new workflow shape.

Installing it. Grab it through ComfyUI Manager (search "ComfyUI-LMCQ") or clone it manually:

cd ComfyUI/custom_nodes
git clone https://github.com/sebord/ComfyUI-LMCQ

Restart ComfyUI afterward - custom node packs only register at boot.

Where people get stuck. The single most common complaint isn't the node itself, it's not finding it. A recurring pattern on r/comfyui: someone downloads an NF4 Flux checkpoint, ComfyUI has no built-in loader for it, they don't know this pack exists, and they either wire it into the wrong loader (producing exactly the kind of shape-mismatch error the format is prone to - reports of things like "got [16, 56, 56] but expected positional dim 262144") or give up on the model entirely. If that's you: this node is the fix, not a workaround.

The second real landmine is pack-wide, not specific to this node: ComfyUI-LMCQ bundles a compiled runtime-protection module for its encryption nodes, and on some installs that module goes missing, throwing ModuleNotFoundError: No module named '...runtime.model_protection' (or api_model_protection) at startup - this has been reported on Reddit as a live, unresolved issue. Because that import failure happens in the pack's __init__.py, it takes the entire pack down with it, including this loader, even though it has nothing to do with encryption. If none of the Lmcq nodes are showing up in your node list, check your console log for that error first. The README's own fix (added January 2026) is to copy the Python-version-matching files from the pack's runtime folder into the runtime root, delete the mismatched ones, and restart - worth trying before assuming your install is broken.