SDXL

stability-ai /

Stability AI SDXL is a text-to-image generator that creates beautiful, high-quality images. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

text-to-image

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Bereit

Close-up food photography of a juicy, gourmet cheeseburger on a rustic wooden board, melted cheddar cheese dripping down the side, sesame seed bun is perfectly toasted, crispy bacon and fresh lettuce are visible, shallow depth of field, professional studio lighting, mouth-watering details.

$0.0026pro Durchlauf·~384 / $1

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Close-up food photography of a juicy, gourmet cheeseburger on a rustic wooden board, melted cheddar cheese dripping down the side, sesame seed bun is perfectly toasted, crispy bacon and fresh lettuce are visible, shallow depth of field, professional studio lighting, mouth-watering details.

A candid street style photograph of a young woman with short messy blonde hair, she is laughing heartily, mid-sentence, natural afternoon sunlight hitting her face, background is a bustling city street with blurred yellow taxis, shot on a 35mm film camera, photorealistic, shallow depth of field, golden hour glow.

Expansive, breathtaking landscape of the Scottish Highlands at dawn, mist rolling through the valleys, a lone stag standing on a distant hill, the sky is painted with soft hues of pink and orange, wide-angle lens, photorealistic, epic scale, serene and majestic atmosphere.

A cozy, sun-drenched living room with a bohemian-style interior, a comfortable-looking sofa with colorful pillows, a cat sleeping peacefully on a knitted blanket, sunlight filtering through a large window creating beautiful dust particles in the air, warm and inviting, photorealistic, detailed textures.

An artist's messy but organized workbench, various paint brushes in a jar, squeezed tubes of oil paint, palettes with mixed colors, a half-finished canvas on an easel, soft, diffused light from a nearby window, top-down view (flat lay), realistic clutter, highly detailed.

A rain-slicked neon-lit street in a futuristic Tokyo, reflections of towering holographic advertisements shimmer on the wet pavement, a lone figure with a glowing umbrella walks down the alley, steam rises from manholes, cyberpunk aesthetic, cinematic, volumetric lighting, Blade Runner style.

A giant, antique gramophone growing out of a desolate desert landscape, its horn pointed towards a sky filled with two moons, the scene is bathed in a surreal twilight glow, style of Salvador Dalí, hyperrealistic detail on the cracked desert floor and the weathered brass of the gramophone.

README

SDXL — Text / Image-to-Image

SDXL is Stability AI’s flagship diffusion model for high-quality image generation. It excels at photorealism, stylized illustration, and product renders.

Key Features

Text-to-Image (T2I): Generate fully new images from natural-language prompts.
Image-to-Image (I2I): Transform an input image toward your prompt while preserving composition.

Modes & When to Use

T2I: Best for concepting and fresh compositions.
I2I: Use when you already like the framing/pose and want a new style or content consistent with the source.

Inputs & Parameters

prompt (required): Describe subject, style, lighting, camera, mood.
image: PNG/JPEG/WebP; used as the starting point.
width / height: Flexible canvas; common choices 1024×1024, 1536×1536, 1024×1536, 1536×1024.
seed: -1 for random, or set any integer for reproducible results.

How to Use

A) Text-to-Image

Enter a prompt with subject + context + style + lighting + camera.
Set width/height for your target aspect ratio (e.g., 1536×1536 for square).
(Optional) Set seed for repeatability.
Run and iterate on wording or seed.

B) Image-to-Image

Upload an image.
Write a prompt describing the desired change or style.
(Optional) Set seed for repeatability.
Run; tweak strength or prompt until the balance feels right.

Price

Per image for $0.0026.

Prompting Tips

Structure prompts as: [subject] + [context] + [style/artist] + [lighting] + [camera] + [mood].
For photorealism: “cinematic lighting, shallow depth of field, 50mm, natural skin texture.”
For products: “studio sweep, three-point lighting, soft reflections, high detail.”
Keep negative prompts concise: “blurry, extra fingers, watermark, text, low-res.”
Lock a seed once you like the look to iterate predictably.

Notes

SDXL will honor platform safety rules; if a prompt is blocked, simplify and try again.
Commercial use follows Stability AI licensing and WaveSpeedAI terms.

Barrierefreiheit:Diese Website nutzt KI-Modelle von Drittanbietern.

Sdxl API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/stability-ai/sdxl with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Sdxl below.

HTTP example

# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/stability-ai/sdxl" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "image": "https://example.com/your-input.jpg",
    "size": "1024*1024",
    "seed": -1,
    "enable_base64_output": false
}'

# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY"

# When status is "completed", read the output from data.outputs[0].

Node.js example

// npm install wavespeed
const WaveSpeed = require('wavespeed');

const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env

const result = await client.run("stability-ai/sdxl", {
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "image": "https://example.com/your-input.jpg",
        "size": "1024*1024",
        "seed": -1,
        "enable_base64_output": false
});

console.log(result.outputs[0]); // → URL of the generated output

Python example

# pip install wavespeed
import wavespeed

output = wavespeed.run(
    "stability-ai/sdxl",
    {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "image": "https://example.com/your-input.jpg",
    "size": "1024*1024",
    "seed": -1,
    "enable_base64_output": false
}
)

print(output["outputs"][0])  # → URL of the generated output

Sdxl API — Frequently asked questions

What is the Sdxl API?

Sdxl is a Stability AI model for image generation, exposed as a REST API on WaveSpeedAI. Stability AI SDXL is a text-to-image generator that creates beautiful, high-quality images. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Sdxl API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID; poll the prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/stability-ai/stability-ai-sdxl.

How much does Sdxl cost per run?

Sdxl starts at $0.003 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Sdxl accept?

Key inputs: `prompt`, `image`, `size`, `seed`, `enable_base64_output`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/stability-ai/stability-ai-sdxl.

How long does Sdxl take to generate?

Average end-to-end generation time on WaveSpeedAI is around 7 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.

Can I use Sdxl outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Stability AI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.