SDXL Img2Img Serverless API

SDXL Img2Img is used for text-guided image-to-image translation. This model uses the weights from Stable Diffusion to generate new images from an input image using StableDiffusionImg2ImgPipeline from diffusers

~8.41s
PlaygroundAPI
Pricing
POST /v2/sdxl-img2img · submit + poll
 1# pip install "segmind>=1.1.0"
 2# export SEGMIND_API_KEY="YOUR_API_KEY"
 3import segmind
 4
 5# Async (v2): submit to the queue and block until COMPLETED.
 6# run() returns the final result dict (600s deadline, 1.0s poll by default).
 7result = segmind.run(
 8    "sdxl-img2img",
 9    image="https://segmind-sd-models.s3.amazonaws.com/display_images/sdxl-img2img-ip.jpg",
10    samples=1,
11    prompt="photorealistic, high-quality, ultra-detailed, image of (an origami bird with a layer that makes each folded polygonal section of the origami a different vivid c olor, an origami rainbow of color in a forest of origami plants and trees in the background), best quality, masterpiece, masterful composition, award, extremely detailed, incredibly high resolution, 32k, 16k, 8k, 4k, UHD, HDR, hyperrealistic, photo studio quality, amazing clarity, tack sharp, sharp focus, volumetric lighting, cinematic style",
12    negative_prompt="nude, disfigured, blurry",
13    scheduler="UniPC",
14    base_model="juggernaut",
15    num_inference_steps=30,
16    guidance_scale=6.5,
17    strength=0.65,
18    seed=98877465625,
19    base64=False,
20)
21print(result["status"])                      # COMPLETED
22print(result.get("output"))                  # model output (e.g. media URL)
23print(result["metrics"]["inference_time"])   # server compute seconds
24
25# --- Or submit + poll manually (track request_id, control the cadence) ---
26from segmind import SegmindClient, InferenceFailed, InferenceTimeout
27
28client = SegmindClient()                      # reads SEGMIND_API_KEY
29payload = {
30    "image": "https://segmind-sd-models.s3.amazonaws.com/display_images/sdxl-img2img-ip.jpg",
31    "samples": 1,
32    "prompt": "photorealistic, high-quality, ultra-detailed, image of (an origami bird with a layer that makes each folded polygonal section of the origami a different vivid c olor, an origami rainbow of color in a forest of origami plants and trees in the background), best quality, masterpiece, masterful composition, award, extremely detailed, incredibly high resolution, 32k, 16k, 8k, 4k, UHD, HDR, hyperrealistic, photo studio quality, amazing clarity, tack sharp, sharp focus, volumetric lighting, cinematic style",
33    "negative_prompt": "nude, disfigured, blurry",
34    "scheduler": "UniPC",
35    "base_model": "juggernaut",
36    "num_inference_steps": 30,
37    "guidance_scale": 6.5,
38    "strength": 0.65,
39    "seed": 98877465625,
40    "base64": False,
41}
42job = client.submit_async("sdxl-img2img", **payload)
43print(job.request_id)                         # available immediately
44try:
45    result = job.wait(timeout=600, interval=1.0)
46except InferenceTimeout as e:
47    print("still running:", e.request_id)
48except InferenceFailed as e:
49    print("failed:", e.detail)

API Endpoint

POSThttps://api.segmind.com/v1/sdxl-img2img

Parameters

imagerequired
string (uri)

Input Image.

promptrequired
string

Prompt to render

base_modeloptional
string

base model for image generation

Default: "juggernaut"
Allowed values :
"juggernaut""realvis""protovision""yamers_realistic"
base64optional
boolean

Base64 encoding of the output image.

Default: false
guidance_scaleoptional
number

Scale for classifier-free guidance

Default: 6.5Range: 0.1 - 25
negative_promptoptional
string

Prompts to exclude, eg. 'bad anatomy, bad hands, missing fingers'

num_inference_stepsoptional
integer

Number of denoising steps.

Default: 30Range: 20 - 100
samplesoptional
integer

Number of samples to generate.

Default: 1Range: 1 - 4
scheduleroptional
string

Type of scheduler.

Default: "UniPC"
Allowed values (29 total):
"DPM++ SDE Karras""DPM++ 2M Karras""DPM++ 2M SDE Karras""DPM++ 2M SDE Heun Karras""DPM++ 3M SDE Karras""LMS Karras""DPM2 Karras""DPM2 a Karras""DPM++ 2S a Karras""DPM++ 2M SDE Exponential"+19 more
seedoptional
integer

Seed for image generation.

Default: -1
strengthoptional
number

How much to transform the reference image

Default: 0.65Range: 0.1 - 1

Response Type

Returns: Image

Asynchronous requests (v2)

Use Async for video, long-running (>~60s), or high-concurrency workloads; Sync is simplest for fast image & LLM calls. Async submits a request and you poll it to completion.

  1. 1
    POST /v2/sdxl-img2img

    Submit — returns request_id, status_url, response_url

  2. 2
    GET /v2/requests/{id}/status

    Poll — until COMPLETED or FAILED

  3. 3
    GET /v2/requests/{id}

    Result — final response body

Status states

QUEUED— Accepted, waiting for a worker
PROCESSING— Running on a worker
COMPLETED— Done — result body is ready
FAILED— Errored (incl. content/RAI blocks)
  • A FAILED request is served as HTTP 422 — the body still carries the error detail.
  • An unknown or expired request_id returns HTTP 404.
  • Results are retained for 1 hour, then expire.
  • Content / RAI blocks surface as FAILED, not a separate state.
  • Track completion by polling the status endpoint.

Common Error Codes

The API returns standard HTTP status codes. Detailed error messages are provided in the response body.

400

Bad Request

Invalid parameters or request format

401

Unauthorized

Missing or invalid API key

403

Forbidden

Insufficient permissions

404

Not Found

Model or endpoint not found

406

Insufficient Credits

Not enough credits to process request

429

Rate Limited

Too many requests

500

Server Error

Internal server error

502

Bad Gateway

Service temporarily unavailable

504

Timeout

Request timed out