---
license: other
license_name: tencent-hunyuan-community
license_link: https://huggingface.co/Tencent-Hunyuan/HunyuanDiT/blob/main/LICENSE.txt
language:
- en
---

# HunyuanDiT TensorRT Acceleration

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.

## 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
sh trt/install.sh
```

## 3. Build the TensorRT engine.


### Method 1: Use the prebuilt engine

We provide some prebuilt TensorRT engines. 

|  Supported GPU   |                                                  Download Link                                                  |            Remote Path            |
|:----------------:|:---------------------------------------------------------------------------------------------------------------:|:---------------------------------:|
| GeForce RTX 3090 | [HuggingFace](https://huggingface.co/Tencent-Hunyuan/TensorRT-engine/blob/main/engines/RTX3090/model_onnx.plan) | `engines/RTX3090/model_onnx.plan` |
| GeForce RTX 4090 | [HuggingFace](https://huggingface.co/Tencent-Hunyuan/TensorRT-engine/blob/main/engines/RTX4090/model_onnx.plan) | `engines/RTX4090/model_onnx.plan` |
|       A100       |  [HuggingFace](https://huggingface.co/Tencent-Hunyuan/TensorRT-engine/blob/main/engines/A100/model_onnx.plan)   |  `engines/A100/model_onnx.plan`   |

Use the following command to download and place the engine in the specified location.

```shell
huggingface-cli download Tencent-Hunyuan/TensorRT-engine <Remote Path> --local-dir ./ckpts/t2i/model_trt/engine
```

### Method 2: Build your own engine

If you are using a different GPU, you can build the engine using the following command.

```shell
# Set the TensorRT build environment variables first. We provide a script to set up the environment.
source trt/activate.sh

# Method 1: Build the TensorRT engine. By default, it will read the `ckpts` folder in the current directory.
sh trt/build_engine.sh

# Method 2: If your model directory is not `ckpts`, you need to specify the model directory.
sh trt/build_engine.sh </path/to/ckpts>
```

4. Run the inference using the TensorRT model.

```shell
# 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
```