Fix the dependency errors that block custom nodes from loading
#How to tell it's a dependency problem
If ComfyUI Manager's "Install Missing Custom Nodes" doesn't fix a red/broken node, or the node loads but throws an error the first time you queue a prompt, the custom node's code is installed but one of its Python or system dependencies isn't. Check the ComfyUI console window — the real error is almost always a few lines above the line ComfyUI prints in the UI.
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Cause: The custom node's requirements.txt never ran, or ran against the wrong Python environment.
Fix:
If that still fails, you likely have two Pythons on your system and installed the node while the wrong one was active. Confirm with:
The path printed should point inside your ComfyUI venv folder. If it doesn't, re-activate the venv and reinstall.
#FFmpeg not found (video export, VideoHelperSuite)
Cause: VideoHelperSuite, AnimateDiff exports, and most LTX Video workflows call the ffmpeg binary directly — it's a system dependency, not a Python package, so pip install can't fix it.
Fix (Windows):
Close and reopen your terminal, then verify:
If winget isn't available, download a build from ffmpeg.org, unzip it, and add the bin folder to your system PATH.
Fix (Linux):
#Face/detection nodes fail on import (insightface, onnxruntime, mediapipe)
Cause: These packages need compiled binaries that match your exact Python version and OS. A pip install that "succeeds" can still fail to import if the wheel doesn't match.
Fix:
On Windows, insightface sometimes needs the Microsoft C++ Build Tools if no precompiled wheel exists for your Python version — install "Desktop development with C++" from the Visual Studio Installer, then retry.
If you have a CUDA GPU, make sure you installed onnxruntime-gpu, not plain onnxruntime — the CPU-only package installs silently and just makes face nodes very slow instead of erroring.
#"CUDA driver version is insufficient" or torch/CUDA mismatch
Cause: A custom node pinned a torch version that doesn't match the CUDA build you installed ComfyUI with, and reinstalling it silently swapped your PyTorch build.
Fix: Reinstall the CUDA-matched PyTorch build explicitly, after any custom node installs:
Confirm it stuck:
torch.cuda.is_available() must print True. If it prints False after this, your NVIDIA driver is older than the CUDA build requires — update the driver first.
#Two custom nodes conflict on the same package version
Cause: Node A needs numpy<2.0, Node B needs numpy>=2.0. Whichever installed last wins, and the other silently breaks.
Fix: There's no universal fix here — check each node's GitHub issues for the specific version pin, and prefer forks/nodes that are actively maintained. As a last resort, keep the two nodes in separate ComfyUI installs rather than fighting version pins in one environment.
#Quick diagnostic checklist
| Symptom | Likely cause | Guide |
|---|---|---|
| Red node, "Install Missing Nodes" doesn't fix it | requirements.txt didn't run | This guide, above |
| Video export nodes error, images work fine | FFmpeg not on PATH | This guide, above |
| Face/detailer nodes crash on first run | onnxruntime/insightface wheel mismatch | This guide, above |
Everything worked yesterday, now torch.cuda.is_available() is False | A node install swapped your torch build | This guide, above |
| Error happens loading the workflow, not a specific node | Wrong model type, not a dependency | Workflow Errors |
| "CUDA out of memory" during generation | VRAM, not a missing package | GPU Errors |
#Related Guides
Hardware Partner
Running these workflows? ComputeAtlas.ai helps you find the right GPU
Optimization is only half the battle. Get precise VRAM benchmarks and hardware recommendations tailored for ComfyUI.
Check GPU Prices →