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Stable Diffusion Tritonserver

Stable Diffusion Tritonserver is an open-source repository of Docker instructions and an inference notebook for serving Stable Diffusion models with NVIDIA Triton Inference Server. Its documented setup uses ONNX model files, with a separate TensorRT conversion path.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Stable Diffusion Tritonserver — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a repository-based starting point for serving Stable Diffusion through Triton on a self-managed, GPU-enabled environment.

Who it is for

  • Developers setting up self-hosted Stable Diffusion inference.
  • Teams using Docker and NVIDIA Triton to serve ONNX models.
  • Practitioners exploring a TensorRT conversion path for Triton-based inference.

Documented uses

  • Build a Docker image containing the Triton serving setup.
  • Mount Stable Diffusion model files into a Triton model repository and start the server.
  • Use a Triton client notebook to send inference requests.
  • Explore converting models for use with the documented serving approach.
Before you choose

Requirements & limits

The repository is public and lists an Apache-2.0 license. The documented deployment is self-hosted and uses Docker; no hosted service is described.

  • The example model is Stable Diffusion v1.4 in ONNX format and is fetched from Hugging Face using Git LFS.
  • The documented container command requests NVIDIA GPU access and sets shared memory to 16384 MB.
  • The README points to a branch for TensorRT conversion but provides no performance results or cross-hardware compatibility guarantees.
  • Replacing the model with another ONNX model is marked Likely compatible, with suitable Triton configuration and preprocessing potentially required.
  • Using the documented TensorRT conversion path is marked Verified; this describes the stated path, not a performance or hardware guarantee.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Build and start the documented ONNX Triton server

Set up the repository's Stable Diffusion v1.4 ONNX model in a Dockerized Triton server.

Before you start

  • Docker
  • A Linux or server environment with NVIDIA GPU access, as indicated by the documented Docker run command
  • Git and Git LFS
  • The Hugging Face model repository kamalkraj/stable-diffusion-v1-4-onnx

Steps

  1. Clone the project repository and the Hugging Face ONNX model repository, after installing Git LFS.

  2. Extract models.tar.gz in the model repository.

  3. Build the Docker image with the repository's Dockerfile.

  4. Run the container with GPU access, the documented ports and shared-memory setting, mounting the model directory at /models and starting Triton with that model repository.

Expected outcome

A Triton server is started with the mounted Stable Diffusion model repository.

Guide limitations

  • The instructions require GPU access and specify a 16 GB shared-memory setting.
  • The guide documents server startup, not measured performance or hardware compatibility.
Documentation 1 ↗
Source-based practical guide

Send inference requests with the notebook client

Use the repository's notebook to make inference requests to Triton.

Before you start

  • A running Triton server configured with the model
  • Python and pip
  • The repository's Inference.ipynb notebook

Steps

  1. Install the HTTP-enabled Triton client with pip install "tritonclient[http]".

  2. Run the repository's inference notebook to submit requests.

Expected outcome

The notebook provides the documented client path for requesting model inference from Triton.

Guide limitations

  • The README does not specify notebook inputs or output-handling details.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Stable Diffusion TritonserverBuild a Docker image containing the Triton serving setup.Docker, Linux, Self-hosted, ServerThe repository is public and lists an Apache-2.0 license. The documented deployment is self-hosted and uses Docker; no hosted service is described.
MorphingdbImport a libtorch model and create predictions from values in a PostgreSQL table.Docker, Linux, Self-hosted, ServerThe README documents a Docker image and a build-from-repository option, followed by connecting to PostgreSQL and enabling the pgdl extension.
Gateway — pyadavSend chat-completion requests to a provider through a common server endpoint.ServerThe repository is public and identifies the project as Apache-2.0 licensed. README installation instructions include binaries, platform package managers and Docker.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
kamalkraj/stable-diffusion-tritonserver · Official homepage title · retained in its source language
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