1. Request access through the model page on Hugging Face and wait for approval from the model authors.
2. Create a Custom keys workspace connection in Microsoft Foundry or Azure Machine Learning named HuggingFaceTokenConnection with the key HF_TOKEN and value your Hugging Face read or fine-grained token (marked as secret).
3. Create the Managed Online Endpoint with the property enforce_access_to_default_secret_stores set to enabled so it can access the secret connection value.
4. Once access is approved, the connection is configured, and the endpoint is created with read access to the token, you can deploy and use the model in Microsoft Foundry or Azure Machine Learning.

• Original Model Card
• text-to-image Task on Hugging Face

Send Request

curl <AZUREML_ENDPOINT_URL> \
    -X POST \
    -H "Authorization: Bearer <AZUREML_TOKEN>" \
    -H "Accept: image/png" \
    -H "Content-Type: application/json" \
    -d '{"inputs":"Astronaut riding a horse"}' \
    --output output.png

Supported Parameters

• inputs (string): The input text data (sometimes called "prompt")
• parameters (object):
  • guidance_scale (float): A higher guidance scale value encourages the model to generate images closely linked to the text prompt, but values too high may cause saturation and other artifacts.
  • negative_prompt (string): One prompt to guide what NOT to include in image generation.
  • num_inference_steps (integer): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
  • width (integer): The width in pixels of the output image.
  • height (integer): The height in pixels of the output image.
  • scheduler (string): Override the scheduler with a compatible one.
  • seed (integer): Seed for the random number generator.

OpenAI Image Generation API