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