# Stable Diffusion 3.5 Large Turbo

> Generate high-quality images from a text prompt in just a few steps — the fastest, lowest-cost Stable Diffusion 3.5 tier.

## Overview

- **Endpoint**: `https://queue.modelrunner.run/stability-ai/stable-diffusion-v3.5-large-turbo`
- **Model ID**: `stability-ai/stable-diffusion-v3.5-large-turbo`
- **Category**: text-to-image
- **Kind**: inference
- **Tags**: stability-ai, stable-diffusion, sd-3.5, text-to-image, image-generation, turbo

## Pricing

- **Price**: $0.015 per megapixel

## Request Lifecycle

This model runs on the ModelRunner **asynchronous queue API** — a single POST does not return the output. Every call requires an `Authorization: Key $MODEL_RUNNER_KEY` header. Run three steps:

1. **Submit** — `POST https://queue.modelrunner.run/stability-ai/stable-diffusion-v3.5-large-turbo` with a JSON body holding the input fields at the top level. The body may also include a reserved top-level `metadata` object — a flat string map (max 16 keys, key ≤64 / value ≤512 chars) stored on the request for your own tagging. It is never sent to the model; filter your request history with `GET https://queue.modelrunner.run/requests?metadata=<url-encoded JSON>` (exact key=value matches, AND-ed). The response carries request handles only (no output yet):

```json
{
  "status": "IN_QUEUE",
  "request_id": "<21-char id>",
  "status_url": "https://queue.modelrunner.run/stability-ai/stable-diffusion-v3.5-large-turbo/requests/<request_id>/status",
  "response_url": "https://queue.modelrunner.run/stability-ai/stable-diffusion-v3.5-large-turbo/requests/<request_id>",
  "cancel_url": "https://queue.modelrunner.run/stability-ai/stable-diffusion-v3.5-large-turbo/requests/<request_id>/cancel"
}
```

2. **Poll status** — `GET <status_url>` until `status` is `COMPLETED`. Possible values are `IN_QUEUE`, `IN_PROGRESS`, `COMPLETED`, `FAILED`, `CANCELLED`. A `FAILED` request responds with HTTP 400 and an `error` field.

3. **Read result** — `GET <response_url>`. Returns the finished request, including the generated `output`:

```json
{ "id": "<request_id>", "status": "COMPLETED", "output": ..., "input": ... }
```

The JavaScript and Python SDKs below perform steps 2–3 for you. In any language without an SDK (Swift, Go, Kotlin, etc.) you must implement the polling loop and the final result fetch yourself — see the cURL example for the full flow.

### Input Schema

- **`seed`** (`integer | null`, _optional_):
  The same seed and the same prompt given to the same version of the model will output the same image every time.

- **`prompt`** (`string`, _required_):
  The text prompt describing the image to generate.

- **`image_size`** (`ImageSize | image_size_enum`, _optional_):
  The size of the generated image. Choose a preset (e.g. 'square_hd', 'portrait_16_9') or pass a custom {width, height} object.
  - Default: `"landscape_4_3"`
  - Options: `"square_hd"`, `"square"`, `"portrait_4_3"`, `"portrait_16_9"`, `"landscape_4_3"`, `"landscape_16_9"`

- **`output_format`** (`OutputFormatEnum`, _optional_):
  The format of the generated image.
  - Default: `"jpeg"`
  - Options: `"jpeg"`, `"png"`

- **`guidance_scale`** (`number`, _optional_):
  The CFG (Classifier Free Guidance) scale. This model is guidance-distilled and defaults to 0; raising it is usually unnecessary and can reduce quality.
  - Default: `0`
  - Range: `0` to `20`

- **`negative_prompt`** (`string`, _optional_):
  Describe what you do NOT want to appear in the image.
  - Default: `""`

- **`num_inference_steps`** (`integer`, _optional_):
  The number of inference steps to perform. This is a few-step distilled model: the default is 4 and the maximum is 12. Raising it can add detail at the cost of speed.
  - Default: `4`
  - Range: `1` to `12`

- **`enable_safety_checker`** (`boolean`, _optional_):
  If set to true, the safety checker will be enabled.
  - Default: `true`

### Output Schema

_No `Output` schema properties are available._

## Default Example

**Input**

```json
{
  "prompt": "a weathered lighthouse keeper studying a hand-drawn nautical chart under a brass oil lamp, dramatic chiaroscuro, oil painting style",
  "image_size": "landscape_16_9",
  "output_format": "jpeg",
  "guidance_scale": 0,
  "negative_prompt": "",
  "num_inference_steps": 4,
  "enable_safety_checker": true
}
```

**Output**

```json
[
  "https://media.modelrunner.ai/O4kOCQSHvWXRXzPs9ifIP.jpeg"
]
```
## Usage Examples

### cURL

The queue API is asynchronous: submit the request, poll `status_url` until it is `COMPLETED`, then read the result from `response_url`. Requires `jq`.

```bash
# 1. Submit the request (returns request handles, not the output)
SUBMIT=$(curl --silent --request POST \
  --url https://queue.modelrunner.run/stability-ai/stable-diffusion-v3.5-large-turbo \
  --header "Authorization: Key $MODEL_RUNNER_KEY" \
  --header "Content-Type: application/json" \
  --data '{
  "prompt": "a weathered lighthouse keeper studying a hand-drawn nautical chart under a brass oil lamp, dramatic chiaroscuro, oil painting style",
  "image_size": "landscape_16_9",
  "output_format": "jpeg",
  "guidance_scale": 0,
  "negative_prompt": "",
  "num_inference_steps": 4,
  "enable_safety_checker": true
}')

STATUS_URL=$(echo "$SUBMIT" | jq -r '.status_url')
RESPONSE_URL=$(echo "$SUBMIT" | jq -r '.response_url')

# 2. Poll until the request leaves the queue / in-progress state
while true; do
  STATUS=$(curl --silent --url "$STATUS_URL" \
    --header "Authorization: Key $MODEL_RUNNER_KEY" | jq -r '.status')
  echo "Status: $STATUS"
  case "$STATUS" in
    COMPLETED) break ;;
    FAILED|CANCELLED) echo "Request $STATUS"; exit 1 ;;
  esac
  sleep 1
done

# 3. Read the finished request, including the generated output
curl --silent --url "$RESPONSE_URL" \
  --header "Authorization: Key $MODEL_RUNNER_KEY"
```

### JavaScript

```javascript
import { modelrunner } from "@modelrunner/client";

const result = await modelrunner.subscribe("stability-ai/stable-diffusion-v3.5-large-turbo", {
  input: {
  "prompt": "a weathered lighthouse keeper studying a hand-drawn nautical chart under a brass oil lamp, dramatic chiaroscuro, oil painting style",
  "image_size": "landscape_16_9",
  "output_format": "jpeg",
  "guidance_scale": 0,
  "negative_prompt": "",
  "num_inference_steps": 4,
  "enable_safety_checker": true
}
});

console.log(result.data);
```

### Python

```python
import asyncio
import modelrunner_ai

async def main():
    response = await modelrunner_ai.submit_async(
        "stability-ai/stable-diffusion-v3.5-large-turbo",
        arguments={
  "prompt": "a weathered lighthouse keeper studying a hand-drawn nautical chart under a brass oil lamp, dramatic chiaroscuro, oil painting style",
  "image_size": "landscape_16_9",
  "output_format": "jpeg",
  "guidance_scale": 0,
  "negative_prompt": "",
  "num_inference_steps": 4,
  "enable_safety_checker": true
}
    )
    result = await response.get()
    print(result["output"])

asyncio.run(main())
```

## Additional Resources

- [Playground](https://modelrunner.ai/models/stability-ai/stable-diffusion-v3.5-large-turbo)
- [OpenAPI Schema](https://modelrunner.ai/models/stability-ai/stable-diffusion-v3.5-large-turbo/openapi.json)
- [LLM Instructions](https://modelrunner.ai/models/stability-ai/stable-diffusion-v3.5-large-turbo/llms.txt)