---
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-10.1.0.27** 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.

##### Hunyuan-DiT v1.2

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

##### Using Previous versions,  Hunyuan-DiT <= v1.1
```shell
# v1.1 
sh trt/build_engine.sh 1.1
# v1.0 
sh trt/build_engine.sh 1.0
```

Finally, if you see the output like `&&&& PASSED TensorRT.trtexec [TensorRT v10100]`, 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
    [(1280, 960)],                  # 4:3
    [(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.