LinCa predictors (ECCV 2026)
Learnable decomposed feature-caching predictors from LinCa: Accelerating Diffusion Models via Learnable Decomposed Feature Caching, accepted to ECCV 2026.
Code: github.com/QHR69/LinCa
Model repo: huggingface.co/QHRQQQ/LinCa
LinCa decomposes a cached DiT feature into sub-components with different continuity, predicts each with a matching order, then reconstructs it through a strictly invertible mapping. Under 0.2% extra parameters, up to 7.08× fewer FLOPs with near-lossless quality.
All speedups below are FLOPs speedups relative to the original 50-step model.
| Model | Setting | FLOPs speedup | Quality |
|---|---|---|---|
| FLUX.1-dev | N=6 |
4.52× | ImageReward 1.0228 (orig. 0.9930) |
| Qwen-Image | N=6 |
4.90× | ImageReward 1.2163 (orig. 1.2532) |
| Qwen-Image-Edit | N=7 |
5.52× | GEdit-EN OS 7.56 (orig. 7.54) |
| HunyuanVideo | N=6 |
5.50× | VBench 80.16 (orig. 80.66) |
Files
These files are predictor weights only. Download FLUX / Qwen backbones from their official sources.
| File | Backbone |
|---|---|
qwen-image_checkpoint.pt |
Qwen-Image |
qwen-image-edit_checkpoint.pt |
Qwen-Image-Edit |
flux/best_predictor_stage{0,1,2}.pt |
FLUX.1-dev (3 stages, splits 17/17/16) |
Use the script defaults in the GitHub repo when loading these checkpoints.
We do not release HunyuanVideo or Wan2.1 predictors.
Citation
@inproceedings{liu2026linca,
title={LinCa: Accelerating Diffusion Models via Learnable Decomposed Feature Caching},
author={Liu, Jinshan and Qin, Haoran and Tu, Xiaobing and Liu, Jiacheng and Hu, Jiahui and Yan, Zhengan and Xie, Yukun and Shen, Kerui and Ren, Jinkui and Lin, Yuqi and Zhang, Xiantao and Zhang, Linfeng},
booktitle={European Conference on Computer Vision (ECCV)},
year={2026}
}
Contact: qinhaoran68@gmail.com