fix readme typos

README.md

@@ -146,20 +146,18 @@ Furthermore, xDiT incorporates optimization techniques from [DiTFastAttn](https:
 ComfyUI, is the most popular web-based Diffusion Model interface optimized for workflow. 
 It provides users with a UI platform for image generation, supporting plugins like LoRA, ControlNet, and IPAdaptor. Yet, its design for native single-GPU usage leaves it struggling with the demands of today’s large DiTs, resulting in unacceptably high latency for users like Flux.1. 
 
-Using our commercial project **TACO-DiT**, a SaaS build on xDiT, we’ve successfully implemented a multi-GPU parallel processing workflow within ComfyUI, effectively addressing Flux.1’s performance challenges. Below is the example of using TACO-DiT to accelerate a Flux workflow with LoRA:
+Using our commercial project **TACO-DiT**, a close-sourced ComfyUI variant built with xDiT, we’ve successfully implemented a multi-GPU parallel processing workflow within ComfyUI, effectively addressing Flux.1’s performance challenges. Below is an example of using TACO-DiT to accelerate a Flux workflow with LoRA:
 
 ![ComfyUI xDiT Demo](https://raw.githubusercontent.com/xdit-project/xdit_assets/main/comfyui/flux-demo.gif)
 
-By using TACO-DiT, you could significantly reduce your ComfyUI workflow inference latency, and  boosting the throughput with Multi-GPUs. Now it is compatible with multiple Plug-ins, including Controlnet and loras.
+By using TACO-DiT, you could significantly reduce your ComfyUI workflow inference latency, and  boosting the throughput with Multi-GPUs. Now it is compatible with multiple Plug-ins, including ControlNet and LoRAs.
 
 More features and details can be found in our Intro Video: 
 + [[YouTube] TACO-DiT: Accelerating Your ComfyUI Generation Experience](https://www.youtube.com/watch?v=7DXnGrARqys) 
 + [[Bilibili] TACO-DiT: 加速你的ComfyUI生成体验](https://www.bilibili.com/video/BV18tU7YbEra/?vd_source=59c1f990379162c8f596974f34224e4f)
 
 The blog article is also available: [Supercharge Your AIGC Experience: Leverage xDiT for Multiple GPU Parallel in ComfyUI Flux.1 Workflow](https://medium.com/@xditproject/supercharge-your-aigc-experience-leverage-xdit-for-multiple-gpu-parallel-in-comfyui-flux-1-54b34e4bca05).   
 
-Currently, if you need the parallel version of ComfyUI, please fill in this [application form ](https://forms.office.com/r/LjG3xJDF80) or contact [[email protected]](mailto:[email protected]).
-
 <h2 id="perf">📈 Performance</h2>
 
 <h3 id="perf_hunyuanvideo">HunyuanVideo</h3>
@@ -248,7 +246,7 @@ You can easily modify the model type, model directory, and parallel options in t
 bash examples/run.sh
 ```
 
-Hybriding multiple parallelism techniques togather is essential for efficiently scaling. 
+Hybridizing multiple parallelism techniques together is essential for efficiently scaling. 
 It's important that **the product of all parallel degrees matches the number of devices**. 
 Note use_cfg_parallel means cfg_parallel=2. For instance, you can combine CFG, PipeFusion, and sequence parallelism with the command below to generate an image of a cute dog through hybrid parallelism. 
 Here ulysses_degree * pipefusion_parallel_degree * cfg_degree(use_cfg_parallel) == number of devices == 8.
@@ -306,7 +304,7 @@ The (<span style="color: red;">xDiT</span>) highlights the methods first propose
 
 The communication and memory costs associated with the aforementioned intra-image parallelism, except for the CFG and DP (they are inter-image parallel), in DiTs are detailed in the table below. (* denotes that communication can be overlapped with computation.)
 
-As we can see, PipeFusion and Sequence Parallel achieve lowest communication cost on different scales and hardware configurations, making them suitable foundational components for a hybrid approach.
+As we can see, PipeFusion and Sequence Parallel achieve the lowest communication cost on different scales and hardware configurations, making them suitable foundational components for a hybrid approach.
 
 𝒑: Number of pixels;\
 𝒉𝒔: Model hidden size;\
@@ -367,7 +365,7 @@ For usage instructions, refer to the [example/run.sh](./examples/run.sh). Simply
 
 <h4 id="dittfastattn">DiTFastAttn</h4>
 
-xDiT also provides DiTFastAttn for single GPU acceleration. It can reduce computation cost of attention layer by leveraging redundancies between different steps of the Diffusion Model.
+xDiT also provides DiTFastAttn for single GPU acceleration. It can reduce the computation cost of attention layers by leveraging redundancies between different steps of the Diffusion Model.
 
 [DiTFastAttn: Attention Compression for Diffusion Transformer Models](./docs/methods/ditfastattn.md)