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This experiment demonstrates how to deploy and use a text-to-image model (Stable Diffusion 3.5 Large) using Flexai’s inference serving capabilities.

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Prerequisites

Before starting, make sure you have:
  • A Flexai account with access to the platform
  • A Hugging Face token with access to the stabilityai/stable-diffusion-3.5-large model
  • The flexai CLI installed and configured

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Setup FlexAI Secret for Hugging Face Token

First, create a FlexAI secret that contains your Hugging Face token to access the inference model: 
# Enter your HF token value when prompted
flexai secret create MY_HF_TOKEN
Make sure your Hugging Face token has access to the stabilityai/stable-diffusion-3.5-large model. You may need to accept the model’s license terms on Hugging Face first.

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Start the FlexAI Inference Endpoint

Start the FlexAI endpoint for the Stable Diffusion 3.5 Large model: 
INFERENCE_NAME=stable-diffusion-35-large
flexai inference serve $INFERENCE_NAME --runtime flexserve --hf-token-secret MY_HF_TOKEN -- --task text-to-image --model stabilityai/stable-diffusion-3.5-large
This command will:
  • Create an inference endpoint named stable-diffusion-35-large
  • Use the flexserve runtime optimized for text-to-image tasks
  • Load the Stable Diffusion 3.5 Large model from Hugging Face
  • Configure it for text-to-image generation

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Get Endpoint Information

Once the endpoint is deployed, you’ll see the API key displayed in the output. Store it in an environment variable: 
export INFERENCE_API_KEY=<API_KEY_FROM_ENDPOINT_CREATION_OUTPUT>
Then retrieve the endpoint URL: 
export INFERENCE_URL=$(flexai inference inspect $INFERENCE_NAME -j | jq .config.endpointUrl -r)
You’ll notice these export lines use the jq tool to extract values from the JSON output of the inspect command.If you don’t have it already, you can get jq from its official website: https://jqlang.org/

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Generate Images

Now you can generate images by making HTTP POST requests to your endpoint. Here’s an example that generates a high-quality image of a golden retriever: 
curl -X POST \
  -H "Authorization: Bearer $INFERENCE_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "inputs": "A highly detailed, realistic photograph of a happy golden retriever sitting on a sofa, 8k, cinematic",
    "parameters": {
      "height": 1024,
      "width": 1024,
      "num_inference_steps": 20,
      "seed": 42,
      "negative_prompt": "blurry, low quality, distorted, deformed, mutated, extra limbs, cropped, out of frame, ugly, unrealistic, cartoon, drawing, painting, watermark, text, logo, nsfw"
    }
  }' \
  -o dog.png \
  "$INFERENCE_URL/v1/images/generations"
This will save the generated image as dog.png in your current directory.

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Parameters Explanation

The API accepts the following parameters:
  • inputs: The text prompt describing the image you want to generate
  • height: Output image height in pixels
  • width: Output image width in pixels
  • num_inference_steps: Number of denoising steps (higher = better quality but slower)
  • seed: Random seed for reproducible results
  • negative_prompt: Description of what you don’t want in the image (helps improve quality)