Instructions to use stabilityai/stable-diffusion-3.5-medium-tensorrt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TensorRT
How to use stabilityai/stable-diffusion-3.5-medium-tensorrt with TensorRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
| pipeline_tag: text-to-image | |
| inference: false | |
| library_name: tensorrt | |
| license: other | |
| license_name: stabilityai-ai-community | |
| license_link: LICENSE.md | |
| tags: | |
| - tensorrt | |
| - sd3.5-medium | |
| - text-to-image | |
| - onnx | |
| extra_gated_prompt: >- | |
| By clicking "Agree", you agree to the [License | |
| Agreement](https://huggingface.co/stabilityai/stable-diffusion-3.5-large/blob/main/LICENSE.md) | |
| and acknowledge Stability AI's [Privacy | |
| Policy](https://stability.ai/privacy-policy). | |
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| language: | |
| - en | |
| # Stable Diffusion 3.5 Medium TensorRT | |
| ## Introduction | |
| This repository hosts the **TensorRT-optimized version** of **Stable Diffusion 3.5 Medium**, developed in collaboration between [Stability AI](https://stability.ai) and [NVIDIA](https://huggingface.co/nvidia). This implementation leverages NVIDIA's TensorRT deep learning inference library to deliver significant performance improvements while maintaining the exceptional image quality of the original model. | |
| Stable Diffusion 3.5 Medium is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features improved performance in image quality, typography, complex prompt understanding, and resource-efficiency. The TensorRT optimization makes these capabilities accessible for production deployment and real-time applications. | |
| ## Model Details | |
| ### Model Description | |
| This repository holds the ONNX exports of the T5, MMDiT and VAE models in BF16 precision. | |
| ## Performance using TensorRT 10.13 | |
| #### Timings for 30 steps at 1024x1024 | |
| | Accelerator | Precision | CLIP-G | CLIP-L | T5 | MMDiT x 30 | VAE Decoder | Total | | |
| |-------------|-----------|------------|--------------|--------------|-----------------------|---------------------|------------------------| | |
| | H100 | BF16 | 16.52 ms | 6.83 ms | 8.46 ms | 2358.34 ms | 72.58 ms | 2496.63 ms | | |
| ## Usage Example | |
| 1. Follow the [setup instructions](https://github.com/NVIDIA/TensorRT/blob/release/sd35/demo/Diffusion/README.md) on launching a TensorRT NGC container. | |
| ```shell | |
| git clone https://github.com/NVIDIA/TensorRT.git | |
| cd TensorRT | |
| git checkout release/sd35 | |
| docker run --rm -it --gpus all -v $PWD:/workspace nvcr.io/nvidia/pytorch:25.01-py3 /bin/bash | |
| ``` | |
| 2. Install libraries and requirements | |
| ```shell | |
| cd demo/Diffusion | |
| python3 -m pip install --upgrade pip | |
| pip3 install -r requirements.txt | |
| python3 -m pip install --pre --upgrade --extra-index-url https://pypi.nvidia.com tensorrt-cu12 | |
| ``` | |
| 3. Generate HuggingFace user access token | |
| To download model checkpoints for the Stable Diffusion 3.5 checkpoints, please request access on the [Stable Diffusion 3.5 Medium](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium) page. | |
| You will then need to obtain a `read` access token to HuggingFace Hub and export as shown below. See [instructions](https://huggingface.co/docs/hub/security-tokens). | |
| ```bash | |
| export HF_TOKEN=<your access token> | |
| ``` | |
| 4. Perform TensorRT optimized inference: | |
| - **Stable Diffusion 3.5 Medium in BF16 precision** | |
| ``` | |
| python3 demo_txt2img_sd35.py \ | |
| "a beautiful photograph of Mt. Fuji during cherry blossom" \ | |
| --version=3.5-medium \ | |
| --bf16 \ | |
| --download-onnx-models \ | |
| --denoising-steps=30 \ | |
| --guidance-scale 3.5 \ | |
| --build-static-batch \ | |
| --use-cuda-graph \ | |
| --hf-token=$HF_TOKEN | |
| ``` | |