Update README.md

README.md

@@ -1,109 +1,109 @@
----
-license: other
-license_name: tencent-hunyuan-community
-license_link: https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/blob/main/LICENSE.txt
-language:
-- en
----
-
-# HunyuanDiT TensorRT Acceleration
-
-Language: **English** | [**中文**](https://huggingface.co/Tencent-Hunyuan/TensorRT-libs/blob/main/README_zh.md)
-
-We provide a TensorRT version of [HunyuanDiT](https://github.com/Tencent/HunyuanDiT) for inference acceleration 
-(faster than flash attention). One can convert the torch model to TensorRT model using the following steps based on
-**TensorRT-9.2.0.5** and **cuda (11.7 or 11.8)**.
-
-> ⚠️ Important Reminder (Suggestion for testing the TensorRT acceleration version):  
-> We recommend users to test the TensorRT version on NVIDIA GPUs with Compute Capability >= 8.0,(For example, RTX4090, 
-> RTX3090, H800, A10/A100/A800, etc.) you can query the Compute Capability corresponding to your GPU from 
-> [here](https://developer.nvidia.com/cuda-gpus#compute). For NVIDIA GPUs with Compute Capability < 8.0, if you want to 
-> try the TensorRT version, you may encounter errors that the TensorRT Engine file cannot be generated or the inference 
-> performance is poor, the main reason is that TensorRT does not support fused mha kernel on this architecture.
-
-## 🛠 Instructions
-
-### 1. Download dependencies from huggingface.
-
-```shell
-cd HunyuanDiT
-# Use the huggingface-cli tool to download the model.
-huggingface-cli download Tencent-Hunyuan/TensorRT-libs --local-dir ./ckpts/t2i/model_trt
-```
-
-### 2. Install the TensorRT dependencies.
-
-```shell
-# Extract and install the TensorRT dependencies.
-sh trt/install.sh
-
-# Set the TensorRT build environment variables. We provide a script to set up the environment.
-source trt/activate.sh
-```
-
-### 3. Build the TensorRT engine.
-
-
-#### Method 1: Use the prebuilt engine
-
-We provide some prebuilt [TensorRT Engines](https://huggingface.co/Tencent-Hunyuan/TensorRT-engine), which need to be downloaded from Huggingface.
-
-|  Supported GPU   |            Remote Path            |
-|:----------------:|:---------------------------------:|
-| GeForce RTX 3090 | `engines/RTX3090/model_onnx.plan` |
-| GeForce RTX 4090 | `engines/RTX4090/model_onnx.plan` |
-|       A100       |  `engines/A100/model_onnx.plan`   |
-
-Use the following command to download and place the engine in the specified location. 
-
-*Note: Please replace `<Remote Path>` with the corresponding remote path in the table above.*
-
-```shell
-export REMOTE_PATH=<Remote Path>
-huggingface-cli download Tencent-Hunyuan/TensorRT-engine ${REMOTE_PATH} ./ckpts/t2i/model_trt/engine/
-ln -s ${REMOTE_PATH} ./ckpts/t2i/model_trt/engine/model_onnx.plan
-```
-
-#### Method 2: Build your own engine
-
-If you are using a different GPU, you can build the engine using the following command.
-
-```shell
-# Build the TensorRT engine. By default, it will read the `ckpts` folder in the current directory.
-sh trt/build_engine.sh
-```
-
-Finally, if you see the output like `&&&& PASSED TensorRT.trtexec [TensorRT v9200]`, the engine is built successfully.
-
-### 4. Run the inference using the TensorRT model.
-
-```shell
-# Important: If you have not activated the environment, please run the following command.
-source trt/activate.sh
-
-# Run the inference using the prompt-enhanced model + HunyuanDiT TensorRT model.
-python sample_t2i.py --prompt "渔舟唱晚" --infer-mode trt
-
-# Close prompt enhancement. (save GPU memory)
-python sample_t2i.py --prompt "渔舟唱晚" --infer-mode trt --no-enhance
-```
-
-### 5. Notice
-
-The TensorRT engine is designed to support following shapes of input for performance reasons.
-In the future, we will verify and try to support arbitrary shapes.
-
-```python
-STANDARD_SHAPE = [
-    [(768, 768), (1024, 1024), (1280, 1280)],   # 1:1
-    [(1024, 768), (1152, 864), (1280, 960)],    # 4:3
-    [(768, 1024), (864, 1152), (960, 1280)],    # 3:4
-    [(1280, 768)],                              # 16:9
-    [(768, 1280)],                              # 9:16
-]
-```
-
-## ❓ Q&A
-
-Please refer to the [Q&A](./QA.md) for more questions and answers about building the TensorRT Engine.
-
+---
+license: other
+license_name: tencent-hunyuan-community
+license_link: https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/blob/main/LICENSE.txt
+language:
+- en
+---
+
+# HunyuanDiT TensorRT Acceleration
+
+Language: **English** | [**中文**](https://huggingface.co/Tencent-Hunyuan/TensorRT-libs/blob/main/README_zh.md)
+
+We provide a TensorRT version of [HunyuanDiT](https://github.com/Tencent/HunyuanDiT) for inference acceleration 
+(faster than flash attention). One can convert the torch model to TensorRT model using the following steps based on
+**TensorRT-9.2.0.5** and **cuda (11.7 or 11.8)**.
+
+> ⚠️ Important Reminder (Suggestion for testing the TensorRT acceleration version):  
+> We recommend users to test the TensorRT version on NVIDIA GPUs with Compute Capability >= 8.0,(For example, RTX4090, 
+> RTX3090, H800, A10/A100/A800, etc.) you can query the Compute Capability corresponding to your GPU from 
+> [here](https://developer.nvidia.com/cuda-gpus#compute). For NVIDIA GPUs with Compute Capability < 8.0, if you want to 
+> try the TensorRT version, you may encounter errors that the TensorRT Engine file cannot be generated or the inference 
+> performance is poor, the main reason is that TensorRT does not support fused mha kernel on this architecture.
+
+## 🛠 Instructions
+
+### 1. Download dependencies from huggingface.
+
+```shell
+cd HunyuanDiT
+# Use the huggingface-cli tool to download the model.
+huggingface-cli download Tencent-Hunyuan/TensorRT-libs --local-dir ./ckpts/t2i/model_trt
+```
+
+### 2. Install the TensorRT dependencies.
+
+```shell
+# Extract and install the TensorRT dependencies.
+sh trt/install.sh
+
+# Set the TensorRT build environment variables. We provide a script to set up the environment.
+source trt/activate.sh
+```
+
+### 3. Build the TensorRT engine.
+
+
+#### Method 1: Use the prebuilt engine
+
+We provide some prebuilt [TensorRT Engines](https://huggingface.co/Tencent-Hunyuan/TensorRT-engine), which need to be downloaded from Huggingface.
+
+|  Supported GPU   |            Remote Path            |
+|:----------------:|:---------------------------------:|
+| GeForce RTX 3090 | `engines/RTX3090/model_onnx.plan` |
+| GeForce RTX 4090 | `engines/RTX4090/model_onnx.plan` |
+|       A100       |  `engines/A100/model_onnx.plan`   |
+
+Use the following command to download and place the engine in the specified location. 
+
+*Note: Please replace `<Remote Path>` with the corresponding remote path in the table above.*
+
+```shell
+export REMOTE_PATH=<Remote Path>
+huggingface-cli download Tencent-Hunyuan/TensorRT-engine ${REMOTE_PATH} ./ckpts/t2i/model_trt/engine/
+ln -s ${REMOTE_PATH} ./ckpts/t2i/model_trt/engine/model_onnx.plan
+```
+
+#### Method 2: Build your own engine
+
+If you are using a different GPU, you can build the engine using the following command.
+
+```shell
+# Build the TensorRT engine. By default, it will read the `ckpts` folder in the current directory.
+sh trt/build_engine.sh
+```
+
+Finally, if you see the output like `&&&& PASSED TensorRT.trtexec [TensorRT v9200]`, the engine is built successfully.
+
+### 4. Run the inference using the TensorRT model.
+
+```shell
+# Important: If you have not activated the environment, please run the following command.
+source trt/activate.sh
+
+# Run the inference using the prompt-enhanced model + HunyuanDiT TensorRT model.
+python sample_t2i.py --prompt "渔舟唱晚" --infer-mode trt
+
+# Close prompt enhancement. (save GPU memory)
+python sample_t2i.py --prompt "渔舟唱晚" --infer-mode trt --no-enhance
+```
+
+### 5. Notice
+
+The TensorRT engine is designed to support following shapes of input for performance reasons.
+In the future, we will verify and try to support arbitrary shapes.
+
+```python
+STANDARD_SHAPE = [
+    [(1024, 1024), (1280, 1280)],   # 1:1
+    [(1024, 768), (1152, 864), (1280, 960)],    # 4:3
+    [(768, 1024), (864, 1152), (960, 1280)],    # 3:4
+    [(1280, 768)],                              # 16:9
+    [(768, 1280)],                              # 9:16
+]
+```
+
+## ❓ Q&A
+
+Please refer to the [Q&A](./QA.md) for more questions and answers about building the TensorRT Engine.
+