> ## Documentation Index
> Fetch the complete documentation index at: https://docs.flex.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Generate Images with Stable Diffusion 3.5 on FlexAI Endpoints
> Deploy Stable Diffusion 3.5 Large for AI image generation via FlexAI inference endpoints. High-quality output, auto-scaling, no code changes needed.
This experiment demonstrates how to deploy and use a text-to-image model (Stable Diffusion 3.5 Large) using Flexai's inference serving capabilities.
## 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
## Setup FlexAI Secret for Hugging Face Token
First, create a FlexAI secret that contains your Hugging Face token to access the inference model:
```bash theme={null}
# 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.
## Start the FlexAI Inference Endpoint
Start the FlexAI endpoint for the Stable Diffusion 3.5 Large model:
```bash theme={null}
INFERENCE_NAME=stable-diffusion-35-large
flexai inference serve $INFERENCE_NAME --runtime flexserve --hf-token-secret MY_HF_TOKEN -- --task text-to-image --model stabilityai/stable-diffusion-3.5-large
```
This command will:
* Create an inference endpoint named `stable-diffusion-35-large`
* Use the `flexserve` runtime optimized for text-to-image tasks
* Load the Stable Diffusion 3.5 Large model from Hugging Face
* Configure it for text-to-image generation
## Get Endpoint Information
Once the endpoint is deployed, you'll see the API key displayed in the output. Store it in an environment variable:
```bash theme={null}
export INFERENCE_API_KEY=
```
Then retrieve the endpoint URL:
```bash theme={null}
export INFERENCE_URL=$(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/](https://jqlang.org/)
## 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:
```bash theme={null}
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.
## 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)
⚡ Deploy this on FlexAI
Any model, NVIDIA or AMD, live in under 60 seconds. No code changes, no infra management, hardware-agnostic.