Descriptions:
CacheDit is a new inference acceleration extension for ComfyUI’s DiT-based models, and this Veteran AI video provides both a conceptual explanation of how it works and hands-on benchmarks across several real workflows. Unlike TeaCache, which simply reuses the previous step’s output, CacheDit applies a Taylor series expansion to predict the next diffusion step — similar to using velocity and acceleration to forecast future position. This predictive approach allows it to correct errors before they accumulate, resulting in better quality at equivalent speedups.
The extension supports a range of popular models including Z-Image, Z-Image Turbo, Qwen Image, LTX Video, and Wan 2.2, with model-specific accelerator nodes for each. Two key parameters — warm-up steps (full calculations before caching begins) and skip interval (how many steps are predicted rather than computed) — are demonstrated with worked examples. Benchmarks show 1.4–1.6x speedup on single-model workflows: a Z-Image Turbo generation dropped from roughly 10 seconds to 8.36 seconds, and further to 6.89 seconds depending on inter-image similarity. A cache hit rate of 38.9% is demonstrated with 7 cached steps out of 18 total.
The video also covers an important edge case: in two-stage sampling pipelines (like the Z-Image Turbo + Base hybrid), CacheDit provides minimal benefit because the second model cannot access cache data from the first, reducing the effective hit rate. This nuance makes the video useful for practitioners deciding where to apply the optimization.
📺 Source: Veteran AI · Published February 04, 2026
🏷️ Format: Benchmark Test
