Double AI Workflow Speed with Tea Cache Hunyuan, LTX, and FLUX Workflows

I was skeptical at first—another “speed boost” node that promises faster generation times without quality loss. But after trying TeaCache with a few different models, I have to admit it actually works. Here’s what happened when I tested it.

TeaCache sits between your diffusion model and the sampling nodes, quietly cutting out redundant calculations. The first time I plugged it into an LTX-Video workflow, the render time dropped from 10 minutes to just under 6. No visible quality difference, just faster.

For HunyuanVideo, the results were similar. A 720p generation that usually takes 7 minutes finished in about 4. The key is the rel_l1_thresh setting—it determines how aggressively the node skips minor details.

I grabbed TeaCache through ComfyUI-Manager—just searched for it and clicked install. If you prefer manual setup, the GitHub repo has clear instructions. Either way, it’s ready in seconds.

• LTX-Video: Works best with rel_l1_thresh around 0.06. Any higher, and I noticed slight artifacts in motion-heavy scenes.
• HunyuanVideo: Sweet spot was 0.15. Faster renders without losing the model’s sharpness.
• FLUX: Surprisingly flexible. Even at 0.4, image quality held up fine for most use cases.

There’s also a WaveSpeed node included as an alternative, but TeaCache felt more consistent in my tests.

The real win wasn’t just speed—it was how little tweaking the node needed. I expected to fiddle with settings for hours, but the defaults worked right away. For deeper adjustments, the GitHub repo has a technical breakdown of how rel_l1_thresh works under the hood.