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Multi-modal Inference

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Setup multi-modal inference with TensorRT

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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.

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:25.10-py3

Clone and set up TensorRT repository

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 required dependencies

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

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

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

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)

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

Run SDXL model inference

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

Validate inference outputs

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__}')"

Cleanup and rollback

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/

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.

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