fix readme typos for hunyuanvideo (#390)

README.md

@@ -20,6 +20,7 @@
 - [📢 Updates](#updates)
 - [🎯 Supported DiTs](#support-dits)
 - [📈 Performance](#perf)
+  - [HunyuanVideo](#perf_hunyuanvideo)
   - [Mochi-1](#perf_mochi1)
   - [CogVideoX](#perf_cogvideox)
   - [Flux.1](#perf_flux)
@@ -54,7 +55,7 @@ Consequently, multi-GPU and multi-machine deployments are essential to meet the
 <h3 id="meet-xdit-parallel">Parallel Inference</h3>
 
 To meet real-time demand for DiTs applications, parallel inference is a must.
-xDiT is an inference engine designed for the parallel deployment of DiTs on large scale. 
+xDiT is an inference engine designed for the parallel deployment of DiTs on a large scale. 
 xDiT provides a suite of efficient parallel approaches for Diffusion Models, as well as computation accelerations.
 
 The overview of xDiT is shown as follows.
@@ -77,23 +78,23 @@ The four parallel methods in xDiT can be configured in a hybrid manner, optimizi
 As shown in the following picture, xDiT offers a set of APIs to adapt DiT models in [huggingface/diffusers](https://github.com/huggingface/diffusers) to hybrid parallel implementation through simple wrappers. 
 If the model you require is not available in the model zoo, developing it yourself is straightforward; please refer to our [Dev Guide](#dev-guide).
 
-We also have implemented the following parallel stategies for reference:
+We also have implemented the following parallel strategies for reference:
 
 1. Tensor Parallelism
 2. [DistriFusion](https://arxiv.org/abs/2402.19481)
 
 
 <h3 id="meet-xdit-perf">Computing Acceleration</h3>
 
-Optimization orthogonal to parallel focuses on accelerating single GPU performance.
+Optimization is orthogonal to parallel focuses on accelerating single GPU performance.
 
 First, xDiT employs a series of kernel acceleration methods. In addition to utilizing well-known Attention optimization libraries, we leverage compilation acceleration technologies such as `torch.compile` and `onediff`.
 
 Furthermore, xDiT incorporates optimization techniques from [DiTFastAttn](https://github.com/thu-nics/DiTFastAttn), which exploits computational redundancies between different steps of the Diffusion Model to accelerate inference on a single GPU.
 
 <h2 id="updates">📢 Updates</h2>
 
-* 🎉**December 7, 2024**: xDiT is the official parallel inference engine for [HunyuanVideo](https://github.com/Tencent-Hunyuan/HunyuanVideo), reducing 5-sec video generation latency from 31 minutes to 5 minutes!
+* 🎉**December 7, 2024**: xDiT is the official parallel inference engine for [HunyuanVideo](https://github.com/Tencent/HunyuanVideo), reducing 5-sec video generation latency from 31 minutes to 5 minutes!
 * 🎉**November 28, 2024**: xDiT achieves 1.6 sec end-to-end latency for 28-step [Flux.1-Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) inference on 4xH100!
 * 🎉**November 20, 2024**: xDiT supports [CogVideoX-1.5](https://huggingface.co/THUDM/CogVideoX1.5-5B) and achieved 6.12x speedup compare to the implementation in diffusers!
 * 🎉**November 11, 2024**: xDiT has been applied to [mochi-1](https://github.com/xdit-project/mochi-xdit) and achieved 3.54x speedup compare to the official open source implementation!
@@ -116,6 +117,7 @@ Furthermore, xDiT incorporates optimization techniques from [DiTFastAttn](https:
 
 | Model Name | CFG | SP | PipeFusion |
 | --- | --- | --- | --- |
+| [🎬 HunyuanVideo](https://github.com/Tencent/HunyuanVideo) | NA | ✔️ | ❎ | 
 | [🎬 CogVideoX1.5](https://huggingface.co/THUDM/CogVideoX1.5-5B) | ✔️ | ✔️ | ❎ | 
 | [🎬 Mochi-1](https://github.com/xdit-project/mochi-xdit) | ✔️ | ✔️ | ❎ | 
 | [🎬 CogVideoX](https://huggingface.co/THUDM/CogVideoX-2b) | ✔️ | ✔️ | ❎ | 
@@ -128,7 +130,7 @@ Furthermore, xDiT incorporates optimization techniques from [DiTFastAttn](https:
 
 </div>
 
-### Supported by legacy version only, including DistriFusion and Tensor Parallel as the standalong parallel strategies:
+### Supported by legacy version only, including DistriFusion and Tensor Parallel as the standalone parallel strategies:
 
 <div align="center">
 
@@ -157,11 +159,11 @@ Currently, if you need the parallel version of ComfyUI, please fill in this [app
 
 <h2 id="perf">📈 Performance</h2>
 
-<h3 id="perf_mochi1">Mochi1</h3>
+<h3 id="perf_hunyuanvideo">HunyuanVideo</h3>
 
 1. [HunyuanVideo Performance Report](./docs/performance/hunyuanvideo.md)
 
-<h3 id="perf_cogvideox">CogVideo</h3>
+<h3 id="perf_cogvideox">Mochi1</h3>
 
 2. [mochi1-xdit: Reducing the Inference Latency by 3.54x Compare to the Official Open Souce Implementation!](https://github.com/xdit-project/mochi-xdit)