Stable Diffusion v3 Medium

This documentation is valid for the following list of our models:

• stable-diffusion-v3-medium

Try in Playground

Model Overview

An advanced text-to-image generation model that utilizes a Multimodal Diffusion Transformer (MMDiT) architecture to produce high-quality images from textual descriptions.

Setup your API Key

If you don’t have an API key for the AI/ML API yet, feel free to use our Quickstart guide.

API Schema

post

Body

application/json

modelstring · enumRequiredPossible values:

promptstring · max: 4000Required

The text prompt describing the content, style, or composition of the image to be generated.

num_imagesnumber · min: 1 · max: 4Optional

The number of images to generate.

Default: 1

seedinteger · min: 1Optional

The same seed and the same prompt given to the same version of the model will output the same image every time.

image_sizeany ofOptionalDefault: square_hd

or

string · enumOptional

The size of the generated image.

Possible values:

negative_promptstringOptional

The description of elements to avoid in the generated image.

prompt_expansionbooleanOptional

If set to True, prompt will be upsampled with more details.

guidance_scalenumber · min: 1 · max: 20Optional

The CFG (Classifier Free Guidance) scale is a measure of how close you want the model to stick to your prompt when looking for a related image to show you.

num_inference_stepsinteger · min: 1 · max: 50Optional

The number of inference steps to perform.

enable_safety_checkerbooleanOptional

If set to True, the safety checker will be enabled.

Default: true

Responses

200Success

application/json

post

/v1/images/generations

JavaScriptPythoncURLHTTP

async function main() {
  const response = await fetch('https://api.aimlapi.com/v1/images/generations', {
    method: 'POST',
    headers: {
      'Authorization': 'Bearer <YOUR_AIMLAPI_KEY>',
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({
      model: 'stable-diffusion-v3-medium',
      prompt: 'A T-Rex relaxing on a beach, lying on a sun lounger and wearing sunglasses.',
    }),
  });

  const data = await response.json();
  console.log(JSON.stringify(data, null, 2));
}

main();

200Success

{
  "data": [
    {
      "url": "https://cdn.aimlapi.com/generations/hedgehog/1749730923700-29fe35d2-4aef-4bc5-a911-6c39884d16a8.png",
      "b64_json": null
    }
  ],
  "meta": {
    "usage": {
      "tokens_used": 120000
    }
  }
}

Quick Example

Let's generate an image using a simple prompt.

Python

import requests
import json

def main():
    response = requests.post(
        "https://api.aimlapi.com/v1/images/generations",
        headers={
            # Insert your AIML API Key instead of <YOUR_AIMLAPI_KEY>:
            "Authorization": "Bearer <YOUR_AIMLAPI_KEY>",
            "Content-Type": "application/json",
        },
        json={
            "prompt": "A T-Rex relaxing on a beach, lying on a sun lounger and wearing sunglasses.",
            "model": "stable-diffusion-v3-medium",
            "image_size": "landscape_16_9"
        }
    )

    data = response.json()
    print(json.dumps(data, indent=2, ensure_ascii=False))

if __name__ == "__main__":
    main()

JS

Response

{
  images: [
    {
      url: 'https://cdn.aimlapi.com/squirrel/files/monkey/pAs554_StzWBkrLMgTH5a.png',
      width: 1024,
      height: 576,
      content_type: 'image/jpeg'
    }
  ],
  timings: { inference: 1.1477893170085736 },
  seed: 3544609846964942300,
  has_nsfw_concepts: [ false ],
  prompt: 'A T-Rex relaxing on a beach, lying on a sun lounger and wearing sunglasses. Realistic photo.',
  num_images: 1
}

We obtained the following 1024x576 image by running this code example:

"A T-Rex relaxing on a beach, lying on a sun lounger and wearing sunglasses."