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Stable Diffusion

Stable Diffusion is a state-of-the-art deep learning model designed for generating high-quality images from textual descriptions. It utilizes a latent diffusion process, a type of generative model that iteratively refines noise to produce detailed and realistic images. This model is highly efficient, scalable, and capable of running on consumer-grade hardware, making it widely used for applications in art generation, content creation, and research. Developed with open accessibility in mind, Stable Diffusion empowers developers and creators to explore generative AI while maintaining adaptability for various use cases.

To learn more about this application, visit the main Stable Diffusion app page.

Installing

Stable Diffusion can be installed on Apolo either via the CLI or the Web Console. Below are the detailed instructions for installing Stable Diffusion using Apolo CLI.

Installing via Apolo CLI

Step 1 — Obtain the application configuration file template:

apolo app-template get stable-diffusion > mystablediffusion.yaml

Step 2 — Customize the application parameters. Below is an example configuration file:

# Example mystablediffusion.yaml

template_name: stable-diffusion
template_version: v25.5.0
input:
  # Enable access to your application over the internet using HTTPS.
  ingress_http:
    # Require authenticated user
    auth: true
  # Select the resource preset used per service replica.
  preset:
    # The name of the preset.
    name: 'H100x1'
  # Configure the deployment settings and model selection for Stable Diffusion.
  stable_diffusion:
    # Set the number of replicas to deploy
    replica_count: 1
    # Hugging Face Model Configuration.
    hugging_face_model:
      # The name of the Hugging Face model.
      model_hf_name: "stabilityai/stable-diffusion-2"
      # The Hugging Face API token.
      hf_token:
        key: HF_TOKEN

Explanation of configuration parameters:

  • preset.name=gpu-l4-x1 requests one NVIDIA L4; Apolo auto‑sets CUDA_VISIBLE_DEVICES & default batch flags.

  • replica_count=2 launches two pods behind a load balancer for higher QPS.

  • hugging_face_model.model_hf_name points to SD‑XL‑Turbo for faster drafts.

  • hf_token pulls the secret string from the HF_TOKEN key in Apolo Secrets.

Step 3 — Deploy the application in your Apolo project:

apolo app install -f mystablediffusion.yaml

Monitor the application status using:

apolo app list

To uninstall the application, use:

apolo app uninstall <app-id>

If you want to see logs of the application, use:

apolo app logs <app-id>

For instructions on how to access the application, please refer to the Usage section.

Inputs / Outputs (schema v1)

Inputs

JSON Path

Default

Description

preset.name

–

GPU preset per replica.

ingress_http.auth

true

Basic‑auth gate.

stable_diffusion.replica_count

1

Number of pods.

stable_diffusion.hugging_face_model.model_hf_name

–

HF model repo.

stable_diffusion.hugging_face_model.hf_token

null

Secret or string token.

Outputs

Key

Purpose

external_api.*

Public REST endpoint (/v1/txt2img).

internal_api.*

In‑cluster endpoint for other workloads.

hf_model.model_hf_name

Echoes the loaded model.

Usage

After installation, you can utilize Stable Diffusion for image generation workflows and you can access it directly from your browser or using its Rest API

To view and manage installed instances of the Stable Diffusion app:

  1. Go to the Installed Apps tab.

  2. You will see a list of all running apps, including the Stable Diffusion app you just installed. To open the detailed information & uninstall the app, click the Details button.

Once in the Details" page, scroll down to the Outputs sections. To launch the applications, find the HTTP API output with with the public domain address, copy and open it in a dedicated browser window.

1 · REST API

Once the app is healthy Apolo exposes auto‑generated Swagger docs at:

https://<APP_HOST>/docs

txt2img endpoint

curl -X POST \
  -H "Authorization: Basic $(apolo config show-token)" \
  -H "accept: application/json" \
  -H "Content-Type: application/json" \
  -d '{
        "prompt": "Some prompt"
      }' \

The response contains a base64‑encoded PNG under images[0].

2 · Web UIs

  • SD.Next Web UI – the default root path (/).

References

PreviousFooocusNextHugging Face

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