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-<!--
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-    <h2>📚 LeetCUDA: Modern CUDA Learn Notes with PyTorch for Beginners 🐑</h2>
-    <img src='https://github.com/user-attachments/assets/b2578723-b7a7-4d8f-bcd1-5008947b808a' width="700">
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-📚 **LeetCUDA**: It includes **Tensor/CUDA Cores, TF32/F16/BF16/F8**, [📖200+ CUDA Kernels🔥](#cuda-kernel) with PyTorch, [📖100+ LLM/CUDA🔥](#my-blogs-part-1) blogs, [📖HGEMM⚡️](./kernels/hgemm) which can achieve `98%~100%` TFLOPS of **cuBLAS**, and [📖flash-attn⚡️](./kernels/flash-attn) using Tensor Cores with pure MMA PTX. ♥️ Please consider to leave a ⭐️ Star to support me, my bro ~ ♥️
-
-<div align="center">
-  <p align="center">
-    <a href="#contribute">🔥🔥 PR Welcome: Add Your Kernel to LeetCUDA! Let's make it Awesome together! 🎉🎉</a> <br>
-    <a href=https://github.com/xlite-dev/LeetCUDA/graphs/contributors > <img src=https://opencollective.com/leetcuda/contributors.svg height=40px > </a>
-  </p>
-  <a href="https://hellogithub.com/repository/98348655a96640ca8ddcbc298edc901d" target="_blank"><img src="https://api.hellogithub.com/v1/widgets/recommend.svg?rid=98348655a96640ca8ddcbc298edc901d&claim_uid=ofSCbzTmdeQk3FD&theme=dark" alt="Featured｜HelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a>
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     <h2>📚 LeetCUDA: Modern CUDA Learn Notes with PyTorch for Beginners 🐑</h2>
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 ## 📖 News 🔥🔥
 <div id="news"></div>
 
-- [2025-08-18]: **[🤗cache-dit](https://github.com/vipshop/cache-dit)** is released! 🤗An Unified and Training-free Cache Acceleration Toolbox for DiTs: Cache Acceleration with One-line Code ~ ♥️. Feel free to take a try!
+- [2025-08-18]: **[🤗cache-dit](https://github.com/vipshop/cache-dit)** is released! A Unified and Training-free Cache Acceleration Framework for 🤗Diffusers: Cache Acceleration with One-line Code ~ ♥️. Feel free to take a try!
 
 <div align='center'>
   <img src=https://github.com/vipshop/cache-dit/raw/main/assets/cache-dit-v1.png height="320px">
@@ -78,14 +52,6 @@
 
 - [2024-12-02]: **[⚡️HGEMM](https://github.com/xlite-dev/HGEMM.git)** is released! Write HGEMM from scratch using Tensor Cores with **WMMA, MMA and CuTe** API, achieve peak🎉 performance.
 
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 ## 📖 Contents
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@@ -510,6 +476,7 @@ The kernels listed here will guide you through a step-by-step progression, rangi
 
 |📖 类型-标题|📖 作者| 📖 推荐 |
 |:---|:---|:---|
+| [[Diffusion推理]📖Cache加速-FoCa公式理解记录](https://zhuanlan.zhihu.com/p/1952056591068144338)|@DefTruth|⭐️⭐️⭐|
 | [[Diffusion推理]📖cache-dit: BlockAdapter支持HunyuanImage-2.1 Cache加速!](https://zhuanlan.zhihu.com/p/1950849526400263083)|@DefTruth|⭐️⭐️⭐|
 | [[Diffusion推理]📖cache-dit + Qwen-Image-Lightning 实现 3.5 steps 推理!](https://zhuanlan.zhihu.com/p/1948696529180295613)|@DefTruth|⭐️⭐️⭐|
 | [[Diffusion推理]📖cache-dit: Wan2.2-MoE 2.4x 推理加速!](https://zhuanlan.zhihu.com/p/1943976514321380955)|@DefTruth|⭐️⭐️⭐|