WanVideo Enhance A Video (native)

WanVideoEnhanceAVideoKJ is the native-ComfyUI implementation of Enhance-A-Video (from NUS-HPC-AI-Lab) wired up for Wan. The pitch when the technique dropped was blunt and appealing: better generated video, basically for free. It patches the model's attention so that temporal coherence and detail improve without extra models, without training, and without much added compute. You slot it between your model loader and your sampler, set a strength, and generations tend to come out cleaner and more consistent across frames.

How it works

Enhance-A-Video adjusts how the model attends across frames during generation - it strengthens cross-frame relationships so the video holds together better temporally, sharpening detail and reducing the mushy, drifting quality that plagues weak video generations. This node applies that modification to your Wan model and hands the patched model back. It's a "free lunch"-style trick: no new weights, just a smarter use of the attention that's already there.

The inputs

Three, and only one you'll actually tune:

• model - your Wan video model. Out comes a modified model that you feed to the sampler.
• latent - the video latent it operates against.
• weight - the strength of the enhancement, default 2, range 0 to 10. This is the dial. Higher pushes the effect harder; the default is a sensible middle.

The single output is the enhanced model.

The honest part

"For free" is mostly true, but not "at any setting." The technique is real and well-liked, but crank the weight too high and you don't get more better - you get artifacts. Users trying it out have reported very deformed, noisy output when it's misapplied or pushed too hard. So the practical advice: start at or near the default, generate, and only nudge the weight up in small steps while watching for the point where detail turns into noise. If your video suddenly looks worse and grainier after adding this node, the weight is too high - that's the failure mode to recognize, not a sign the node is broken.

Installing KJNodes

In ComfyUI Manager, search "KJNodes for ComfyUI" in the Custom Nodes Manager, install, restart. Manual: cd ComfyUI/custom_nodes && git clone https://github.com/kijai/ComfyUI-KJNodes, then pip install -r ComfyUI-KJNodes/requirements.txt (portable: python_embeded\python.exe -m pip install -r ComfyUI\custom_nodes\ComfyUI-KJNodes\requirements.txt), restart. The node itself needs nothing extra; it only does something inside a working Wan workflow.

Troubleshooting

If output degrades after adding it, lower the weight - that's the number-one cause of a bad result. If you see no change at all, confirm you actually routed the patched model output into the sampler; it's easy to add the node and forget to reconnect the model wire through it. And because this modifies attention behavior, it stacks with other model patches (speed LoRAs, other enhancers) in ways that aren't always additive - if you're combining several tweaks and things look off, pull them apart and re-add one at a time to find which one is fighting the others.