Create README.md

README.md

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+---
+language:
+- en
+base_model:
+- tencent/HunyuanVideo
+- tencent/HunyuanVideo-1.5
+---
+
+<div align=center>
+  
+# *DisCa*: Accelerating Video Diffusion Transformers with *Dis*tillation-Compatible Learnable Feature *Ca*ching
+<h3>CVPR 2026</h3>
+
+**[EPIC Lab@SAI,SJTU](http://zhanglinfeng.tech)** | **[Tencent Hunyuan](https://github.com/Tencent-Hunyuan)** 
+
+[Chang Zou](https://shenyi-z.github.io/), 
+[Changlin Li](https://scholar.google.com/citations?user=wOQjqCMAAAAJ&hl=en), 
+Yang Li,
+Patrol Li,
+Jianbing Wu,
+Xiao He,
+Songtao Liu,
+Zhao Zhong,
+Kailin Huang,
+[Linfeng Zhang](https://zhanglinfeng.tech)<sup>†</sup>
+
+<a href="https://arxiv.org/abs/2602.05449" target="_blank"><img alt="arXiv" src="https://img.shields.io/badge/arXiv-2602.05449-B31B1B?logo=arxiv&logoColor=white" height="25"/></a>
+<a href="#citation"><img alt="Citation" src="https://img.shields.io/badge/Citation-BibTeX-6C63FF?logo=bookstack&logoColor=white" height="25"/></a>
+<a href="https://huggingface.co/Tencent/DisCa" target="_blank"><img alt="HuggingFace Models" src="https://img.shields.io/badge/huggingface-DisCa-f0c542?logo=huggingface&logoColor=white" height="25"/></a>
+</div>
+
+
+## 📑 Overview
+![Heading](assets/imgs/DisCa_Heading.png)
+> **<p align="justify"> Abstract:** *While diffusion models have achieved great success in the field of video generation, this progress is accompanied by a rapidly escalating computational burden.
+Among the existing acceleration methods, Feature Caching is popular due to its training-free property and considerable speedup performance,but it inevitably faces semantic and detail drop with further compression. Another widely adopted method, training-aware step-distillation, though successful in image generation, also faces drastic degradation in video generation with a few steps. Furthermore, the quality loss becomes more severe when simply applying training-free feature caching to the step-distilled models, due to the sparser sampling steps. 
+This paper novelly introduces a **distillation-compatible learnable** feature caching mechanism for the first time. We employ **a lightweight learnable neural predictor** instead of traditional training-free heuristics for diffusion models, enabling a more accurate capture of the high-dimensional feature evolution process. Furthermore, we explore the challenges of highly compressed distillation on large-scale video models and propose a conservative **Restricted MeanFlow** approach to achieve more stable and lossless distillation. By undertaking these initiatives, we further push the acceleration boundaries while preserving generation quality.* </p>
+
+## ✏️ Technical Details
+![Tech Details](assets/imgs/DisCa_Pipeline.png)
+> **<p align="justify"> See more details in the paper.** </p>
+
+## 🛠 Installation
+
+``` bash
+git clone https://github.com/Tencent-Hunyuan/DisCa.git
+cd DisCa
+```
+
+## 🪐 Experiments
+
+### Text-to-Video (T2V) Task on HunyuanVideo-1.0
+The experiments for the Text-to-Video task are conducted on **HunyuanVideo-1.0**. Given that the original project does not provide inference scripts for MeanFlow, we have supplemented them in this project. 
+
+We provide two scripts to implement the proposed methods:
+* `infer_r_meanflow.sh` for **Restricted MeanFlow**
+* `infer_disca.sh` for **DisCa**
+
+The environment setup follows the official HunyuanVideo-1.0 configuration. We have enabled command-line argument passing; you can modify these arguments directly within the bash files or execute them via the command line.
+
+### Image-to-Video (I2V) Task on HunyuanVideo-1.5
+The experiments for the Image-to-Video task are conducted on **HunyuanVideo-1.5**. Because HunyuanVideo-1.5 is already well-adapted for MeanFlow inference, this project provides a lightweight and concise codebase. 
+
+Since the Image-to-Video task inherently possesses stronger control signals and is easier to distill, coupled with the enhanced capabilities of HunyuanVideo-1.5, the I2V implementation in this project achieves a higher compression ratio.
+
+**Getting Started:**
+1. Follow the instructions in [`disca_i2v_hyvideo15/README.md`](disca_i2v_hyvideo15/README.md) to quickly set up the environment and configure the code.
+2. Execute the inference directly using `infer_disca.sh`.
+
+**Restricted MeanFlow Component:**
+For the corresponding Restricted MeanFlow component, you can use the [HunyuanVideo-1.5-480P-I2V-step-distill](https://huggingface.co/tencent/HunyuanVideo-1.5/tree/main/transformer/480p_i2v_step_distilled) model provided in the official HunyuanVideo-1.5 repository. This model is distilled using similar techniques and has undergone further optimization.
+
+
+## 📚 Citation
+
+```bibtex
+@inproceedings{zou2026disca,
+  abbr      = {CVPR},
+  title     = {DisCa: Accelerating Video Diffusion Transformers with Distillation-Compatible Learnable Feature Caching},
+  author    = {Zou, Chang and Li, Changlin and Liu, Songtao and Zhong, Zhao and Huang, Kailin and Zhang, Linfeng},
+  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year      = {2026},
+  url       = {https://arxiv.org/abs/2602.05449},
+  note      = {to appear},
+}
+```
+
+## 🧐 Limitations
+Despite achieving promising performance, DisCa still leaves significant room for optimization, particularly regarding issues like inter-frame jitter and the need for higher compression ratios. How can we train a better Predictor? It might be worth exploring more techniques (like DMD and post-training etc..) to further improve the performance.
+
+## 🎉 Acknowledgements
+Thanks to HunyuanVideo-1.0 and HunyuanVideo-1.5 for open-sourcing their code and models, which make this work possible.