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Setup multi-modal inference with TensorRT
To easily manage containers without sudo, you must be in the docker group. If you choose to skip this step, you will need to run Docker commands with sudo.
Open a new terminal and test Docker access. In the terminal, run:
docker ps
If you see a permission denied error (something like permission denied while trying to connect to the Docker daemon socket), add your user to the docker group so that you don't need to run the command with sudo .
sudo usermod -aG docker $USER
newgrp docker
Start the NVIDIA PyTorch container with GPU access and HuggingFace cache mounting. This provides the TensorRT development environment with all required dependencies pre-installed.
docker run --gpus all --ipc=host --ulimit memlock=-1 \
--ulimit stack=67108864 -it --rm --ipc=host \
-v $HOME/.cache/huggingface:/root/.cache/huggingface \
nvcr.io/nvidia/pytorch:25.11-py3
Download the TensorRT repository and configure the environment for diffusion model demos.
git clone https://github.com/NVIDIA/TensorRT.git -b main --single-branch && cd TensorRT
export TRT_OSSPATH=/workspace/TensorRT/
cd $TRT_OSSPATH/demo/Diffusion
Install NVIDIA ModelOpt and other dependencies for model quantization and optimization.
# Install OpenGL libraries
apt update
apt install -y libgl1 libglu1-mesa libglib2.0-0t64 libxrender1 libxext6 libx11-6 libxrandr2 libxss1 libxcomposite1 libxdamage1 libxfixes3 libxcb1
pip install nvidia-modelopt[torch,onnx]
sed -i '/^nvidia-modelopt\[.*\]=.*/d' requirements.txt
pip3 install -r requirements.txt
pip install onnxconverter_common
Set up your HuggingFace token to access open models.
export HF_TOKEN = <YOUR_HUGGING_FACE_TOKEN>
Test multi-modal inference using the Flux.1 Dev model with different precision formats.
Substep A. BF16 quantized precision
python3 demo_txt2img_flux.py "a beautiful photograph of Mt. Fuji during cherry blossom" \
--hf-token=$HF_TOKEN --download-onnx-models --bf16
Substep B. FP8 quantized precision
python3 demo_txt2img_flux.py "a beautiful photograph of Mt. Fuji during cherry blossom" \
--hf-token=$HF_TOKEN --quantization-level 4 --fp8 --download-onnx-models
Substep C. FP4 quantized precision
python3 demo_txt2img_flux.py "a beautiful photograph of Mt. Fuji during cherry blossom" \
--hf-token=$HF_TOKEN --fp4 --download-onnx-models
Test the faster Flux.1 Schnell variant with different precision formats.
WARNING
FP16 Flux.1 Schnell requires >48GB VRAM for native export
Substep A. FP16 precision (high VRAM requirement)
python3 demo_txt2img_flux.py "a beautiful photograph of Mt. Fuji during cherry blossom" \
--hf-token=$HF_TOKEN --version="flux.1-schnell"
Substep B. FP8 quantized precision
python3 demo_txt2img_flux.py "a beautiful photograph of Mt. Fuji during cherry blossom" \
--hf-token=$HF_TOKEN --version="flux.1-schnell" \
--quantization-level 4 --fp8 --download-onnx-models
Substep C. FP4 quantized precision
python3 demo_txt2img_flux.py "a beautiful photograph of Mt. Fuji during cherry blossom" \
--hf-token=$HF_TOKEN --version="flux.1-schnell" \
--fp4 --download-onnx-models
Test the SDXL model for comparison with different precision formats.
Substep A. BF16 precision
python3 demo_txt2img_xl.py "a beautiful photograph of Mt. Fuji during cherry blossom" \
--hf-token=$HF_TOKEN --version xl-1.0 --download-onnx-models
Substep B. FP8 quantized precision
python3 demo_txt2img_xl.py "a beautiful photograph of Mt. Fuji during cherry blossom" \
--hf-token=$HF_TOKEN --version xl-1.0 --download-onnx-models --fp8
Check that the models generated images successfully and measure performance differences.
# Check for generated images in output directory
ls -la *.png *.jpg 2>/dev/null || echo "No image files found"
# Verify CUDA is accessible
nvidia-smi
# Check TensorRT version
python3 -c "import tensorrt as trt; print(f'TensorRT version: {trt.__version__}')"
Remove downloaded models and exit container environment to free disk space.
WARNING
This will delete all cached models and generated images
# Exit container
exit
# Remove HuggingFace cache (optional)
rm -rf $HOME/.cache/huggingface/
Use the validated setup to generate custom images or integrate multi-modal inference into your applications. Try different prompts or explore model fine-tuning with the established TensorRT environment.