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SDXL Lightning 4-step API

bytedance/sdxl-lightning-4step

SDXL-Lightning, Stable Diffusion XL’den damıtılmış, yalnızca birkaç adımda yüksek kaliteli 1024px görseller üreten yıldırım hızında bir text-to-image modelidir.

A100
0.00215033
GitHub
Lisans
Ağırlıklar
Makale
OpenAPI

Model girdisi

Input

Input prompt

Negative Input prompt

Min: 256 - Max: 1280

Width of output image. Recommended 1024 or 1280

Min: 256 - Max: 1280

Height of output image. Recommended 1024 or 1280

Min: 1 - Max: 4

Number of images to output.

scheduler

Min: 1 - Max: 10

Number of denoising steps. 4 for best results

Min: 0 - Max: 50

Scale for classifier-free guidance

Random seed. Leave blank to randomize the seed

Disable safety checker for generated images

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Model çıktısı

Output

Generated image output
Generated in 1.938 seconds
Logs (7 lines)

Örnek istekler

Örnekler

Example output 1Example output 2Example output 3Example output 4Example output 5

SDXL Lightning 4-step API

SDXL Lightning 4-step is a text-to-image AI model by bytedance. On ModelRunner it runs through a REST API or via MCP from any AI assistant, at about $0.00215033 per image.

POST https://queue.modelrunner.run/bytedance/sdxl-lightning-4step

cURL

# Submit a request to the queue. Input fields go at the top level of the
# body. The optional reserved "metadata" object holds your own flat string
# tags — stored on the request, never sent to the model; filter later with
# GET https://queue.modelrunner.run/requests?metadata=<url-encoded JSON>.
curl -X POST https://queue.modelrunner.run/bytedance/sdxl-lightning-4step \
  -H "Authorization: Key $MRUN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "seed": 103,
    "width": 1280,
    "height": 1024,
    "prompt": "two friends cooking together in a sunlit Scandinavian kitchen, one slicing herbs while the other stirs a pot, visible…",
    "scheduler": "K_EULER",
    "num_outputs": 1,
    "guidance_scale": 0,
    "negative_prompt": "worst quality, low quality, deformed, extra fingers, fused hands, disembodied limbs, awkward pose, motion blur, water…",
    "num_inference_steps": 4,
    "disable_safety_checker": false,
    "metadata": {
      "project": "my-project"
    }
  }'
# → { "request_id": "...", "status_url": "...", "response_url": "..." }

# Poll status_url until "COMPLETED", then fetch the result
curl "https://queue.modelrunner.run/bytedance/sdxl-lightning-4step/requests/$REQUEST_ID/status" \
  -H "Authorization: Key $MRUN_API_KEY"
curl "https://queue.modelrunner.run/bytedance/sdxl-lightning-4step/requests/$REQUEST_ID" \
  -H "Authorization: Key $MRUN_API_KEY"

JavaScript

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

const result = await modelrunner.subscribe("bytedance/sdxl-lightning-4step", {
  input: {
    "seed": 103,
    "width": 1280,
    "height": 1024,
    "prompt": "two friends cooking together in a sunlit Scandinavian kitchen, one slicing herbs while the other stirs a pot, visible…",
    "scheduler": "K_EULER",
    "num_outputs": 1,
    "guidance_scale": 0,
    "negative_prompt": "worst quality, low quality, deformed, extra fingers, fused hands, disembodied limbs, awkward pose, motion blur, water…",
    "num_inference_steps": 4,
    "disable_safety_checker": false
  },
});
console.log(result);

Python

import os
import requests

headers = {"Authorization": f"Key {os.environ['MRUN_API_KEY']}"}

submitted = requests.post(
    "https://queue.modelrunner.run/bytedance/sdxl-lightning-4step",
    headers=headers,
    json={
      "seed": 103,
      "width": 1280,
      "height": 1024,
      "prompt": "two friends cooking together in a sunlit Scandinavian kitchen, one slicing herbs while the other stirs a pot, visible…",
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "guidance_scale": 0,
      "negative_prompt": "worst quality, low quality, deformed, extra fingers, fused hands, disembodied limbs, awkward pose, motion blur, water…",
      "num_inference_steps": 4,
      "disable_safety_checker": false
    },
).json()

# Poll submitted["status_url"] until "COMPLETED", then:
result = requests.get(submitted["response_url"], headers=headers).json()

Input parameters

Input parameters of SDXL Lightning 4-step
NameTypeRequiredDescription
promptstringnoInput prompt Default: "self-portrait of a woman, lightning in the background".
negative_promptstringnoNegative Input prompt Default: "worst quality, low quality".
widthintegernoWidth of output image. Recommended 1024 or 1280 Default: 1024.
heightintegernoHeight of output image. Recommended 1024 or 1280 Default: 1024.
num_outputsintegernoNumber of images to output. Default: 1.
schedulerenumnoscheduler One of: DDIM, DPMSolverMultistep, HeunDiscrete, KarrasDPM, K_EULER_ANCESTRAL, K_EULER, PNDM, DPM++2MSDE. Default: "K_EULER".
num_inference_stepsintegernoNumber of denoising steps. 4 for best results Default: 4.
guidance_scalenumbernoScale for classifier-free guidance Default: 0.
seedintegernoRandom seed. Leave blank to randomize the seed Default: 0.
disable_safety_checkerbooleannoDisable safety checker for generated images Default: false.

Machine-readable: OpenAPI schema · llms.txt

Use SDXL Lightning 4-step from Claude & Cursor (MCP)

Point Claude Code, Claude Desktop, Cursor, or any MCP client at the ModelRunner MCP server and SDXL Lightning 4-step becomes a tool your assistant can call directly — it authorizes via OAuth (no API key in config) and runs this model with the run_model tool using the endpoint bytedance/sdxl-lightning-4step.

MCP client config (Claude Desktop, Cursor)

{
  "mcpServers": {
    "modelrunner": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://mcp.modelrunner.run/mcp"]
    }
  }
}

Claude Code

claude mcp add --transport http modelrunner https://mcp.modelrunner.run/mcp

Then ask your assistant, for example: “Run bytedance/sdxl-lightning-4step on ModelRunner to generate image”. MCP setup guide.

Model Detayları

Model Detayları

# SDXL-Lightning

**SDXL-Lightning**, hem **hız** hem de **kalite** için tasarlanmış, son teknoloji bir text-to-image üretim modelidir; şaşırtıcı derecede az adımda **yüksek çözünürlüklü 1024px görseller** üretebilir.

[`stabilityai/stable-diffusion-xl-base-1.0`](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) temeli üzerine kurulmuştur ve hızlı ama güçlü görsel sentezi için *Progressive Adversarial Diffusion Distillation* yöntemini kullanır.

## Özellikler - 🚀 1, 2, 4 ve 8 adımlık damıtılmış modellerle **yıldırım hızında inference**. - **2, 4 ve 8 adımlık** modeller olağanüstü görsel kalitesi sunar. - **1 adımlık** model deneyseldir; uç verimliliğin sınırlarını araştırır.

- 🧩 **Model formatları**: - **Tam UNet checkpoint’leri** – en iyi görsel kalitesini verir. - **LoRA checkpoint’leri** – hafiftir ve başka temel modellere kolayca uygulanır.

## Open Source SDXL-Lightning’i, topluluğu en ileri hızlı diffusion teknolojisiyle güçlendirme araştırmamızın bir parçası olarak tamamen open source yayımlıyoruz. Ayrıntılar için makalemize bakın: **SDXL-Lightning: Progressive Adversarial Diffusion Distillation**.