Multi-modal Inference

Step 1
Configure Docker permissions

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:

Bash

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 .

Bash

sudo usermod -aG docker $USER
newgrp docker

Step 2
Launch the TensorRT container environment

Start the NVIDIA PyTorch container with GPU access and HuggingFace cache mounting. This provides the TensorRT development environment with all required dependencies pre-installed.

Bash

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

Step 3
Clone and set up TensorRT repository

Download the TensorRT repository and configure the environment for diffusion model demos.

Bash

git clone https://github.com/NVIDIA/TensorRT.git -b main --single-branch && cd TensorRT
export TRT_OSSPATH=/workspace/TensorRT/
cd $TRT_OSSPATH/demo/Diffusion

Step 4
Install required dependencies

Install NVIDIA ModelOpt and other dependencies for model quantization and optimization.

Bash

# 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.

Bash

export HF_TOKEN = <YOUR_HUGGING_FACE_TOKEN>

Step 5
Run Flux.1 Dev model inference

Test multi-modal inference using the Flux.1 Dev model with different precision formats.

Substep A. BF16 quantized precision

Bash

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

Bash

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

Bash

python3 demo_txt2img_flux.py "a beautiful photograph of Mt. Fuji during cherry blossom" \
  --hf-token=$HF_TOKEN --fp4 --download-onnx-models

Step 6
Run Flux.1 Schnell model inference

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)

Bash

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

Bash

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

Bash

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

Step 7
Run SDXL model inference

Test the SDXL model for comparison with different precision formats.

Substep A. BF16 precision

Bash

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

Bash

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

Step 8
Validate inference outputs

Check that the models generated images successfully and measure performance differences.

Bash

# 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__}')"

Step 9
Cleanup and rollback

Remove downloaded models and exit container environment to free disk space.

WARNING

This will delete all cached models and generated images

Bash

# Exit container
exit

# Remove HuggingFace cache (optional)
rm -rf $HOME/.cache/huggingface/

Step 10
Next steps

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.

Multi-modal Inference

Step 1
Configure Docker permissions

Step 2
Launch the TensorRT container environment

Step 3
Clone and set up TensorRT repository

Step 4
Install required dependencies

Step 5
Run Flux.1 Dev model inference

Step 6
Run Flux.1 Schnell model inference

Step 7
Run SDXL model inference

Step 8
Validate inference outputs

Step 9
Cleanup and rollback

Step 10
Next steps

Resources

Multi-modal Inference

Step 1
Configure Docker permissions

Step 2
Launch the TensorRT container environment

Step 3
Clone and set up TensorRT repository

Step 4
Install required dependencies

Step 5
Run Flux.1 Dev model inference

Step 6
Run Flux.1 Schnell model inference

Step 7
Run SDXL model inference

Step 8
Validate inference outputs

Step 9
Cleanup and rollback

Step 10
Next steps

Resources

Multi-modal Inference

Step 1Configure Docker permissions

Step 2Launch the TensorRT container environment

Step 3Clone and set up TensorRT repository

Step 4Install required dependencies

Step 5Run Flux.1 Dev model inference

Step 6Run Flux.1 Schnell model inference

Step 7Run SDXL model inference

Step 8Validate inference outputs

Step 9Cleanup and rollback

Step 10Next steps

Resources

Multi-modal Inference

Step 1Configure Docker permissions

Step 2Launch the TensorRT container environment

Step 3Clone and set up TensorRT repository

Step 4Install required dependencies

Step 5Run Flux.1 Dev model inference

Step 6Run Flux.1 Schnell model inference

Step 7Run SDXL model inference

Step 8Validate inference outputs

Step 9Cleanup and rollback

Step 10Next steps

Resources

Step 1
Configure Docker permissions

Step 2
Launch the TensorRT container environment

Step 3
Clone and set up TensorRT repository

Step 4
Install required dependencies

Step 5
Run Flux.1 Dev model inference

Step 6
Run Flux.1 Schnell model inference

Step 7
Run SDXL model inference

Step 8
Validate inference outputs

Step 9
Cleanup and rollback

Step 10
Next steps

Step 1
Configure Docker permissions

Step 2
Launch the TensorRT container environment

Step 3
Clone and set up TensorRT repository

Step 4
Install required dependencies

Step 5
Run Flux.1 Dev model inference

Step 6
Run Flux.1 Schnell model inference

Step 7
Run SDXL model inference

Step 8
Validate inference outputs

Step 9
Cleanup and rollback

Step 10
Next steps