Segmind-Vega Serverless API

The Segmind-Vega Model is a distilled version of the Stable Diffusion XL (SDXL), offering a remarkable 70% reduction in size and an impressive 100% speedup while retaining high-quality text-to-image generation capabilities.

~2.36s
PlaygroundAPI
Pricing
POST /v2/segmind-vega · 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    "segmind-vega",
 9    prompt="cinematic photo detailed closeup portraid of a Beautiful cyberpunk woman, robotic parts, cables, lights, text; , high quality photography, 3 point lighting, flash with softbox, 4k, Canon EOS R3, hdr, smooth, sharp focus, high resolution, award winning photo, 80mm, f2.8, bokeh . 35mm photograph, film, bokeh, professional, 4k, highly detailed, high quality photography, 3 point lighting, flash with softbox, 4k, Canon EOS R3, hdr, smooth, sharp focus, high resolution, award winning photo, 80mm, f2.8, bokeh",
10    negative_prompt="(worst quality, low quality)",
11    samples=1,
12    scheduler="UniPC",
13    num_inference_steps=25,
14    guidance_scale=9,
15    seed=1232788698,
16    img_width=1024,
17    img_height=1024,
18    base64=False,
19)
20print(result["status"])                      # COMPLETED
21print(result.get("output"))                  # model output (e.g. media URL)
22print(result["metrics"]["inference_time"])   # server compute seconds
23
24# --- Or submit + poll manually (track request_id, control the cadence) ---
25from segmind import SegmindClient, InferenceFailed, InferenceTimeout
26
27client = SegmindClient()                      # reads SEGMIND_API_KEY
28payload = {
29    "prompt": "cinematic photo detailed closeup portraid of a Beautiful cyberpunk woman, robotic parts, cables, lights, text; , high quality photography, 3 point lighting, flash with softbox, 4k, Canon EOS R3, hdr, smooth, sharp focus, high resolution, award winning photo, 80mm, f2.8, bokeh . 35mm photograph, film, bokeh, professional, 4k, highly detailed, high quality photography, 3 point lighting, flash with softbox, 4k, Canon EOS R3, hdr, smooth, sharp focus, high resolution, award winning photo, 80mm, f2.8, bokeh",
30    "negative_prompt": "(worst quality, low quality)",
31    "samples": 1,
32    "scheduler": "UniPC",
33    "num_inference_steps": 25,
34    "guidance_scale": 9,
35    "seed": 1232788698,
36    "img_width": 1024,
37    "img_height": 1024,
38    "base64": False,
39}
40job = client.submit_async("segmind-vega", **payload)
41print(job.request_id)                         # available immediately
42try:
43    result = job.wait(timeout=600, interval=1.0)
44except InferenceTimeout as e:
45    print("still running:", e.request_id)
46except InferenceFailed as e:
47    print("failed:", e.detail)

API Endpoint

POSThttps://api.segmind.com/v1/segmind-vega

Parameters

promptrequired
string

Prompt to render

base64optional
boolean

Base64 encoding of the output image.

Default: false
guidance_scaleoptional
number

Scale for classifier-free guidance

Default: 7.5Range: 1 - 25
img_heightoptional
integer

Can only be 1024 for SDXL

Default: 1024
Allowed values :
1024
img_widthoptional
integer

Can only be 1024 for SDXL

Default: 1024
Allowed values :
1024
negative_promptoptional
string

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

num_inference_stepsoptional
integer

Number of denoising steps.

Default: 25Range: 20 - 100
samplesoptional
integer

Number of samples to generate.

Default: 1Range: 1 - 4
scheduleroptional
string

Type of scheduler.

Default: "DPM2 Karras"
Allowed values (12 total):
"DDIM""DPM Multi""DPM Single""Euler a""Euler""Heun""DPM2 a Karras""DPM2 Karras""LMS""PNDM"+2 more
seedoptional
integer

Seed for image generation.

Default: -1Range: -1 - 999999999999999

Response Type

Returns: Text/JSON

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/segmind-vega

    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