Flux-1.1 Pro Ultra Serverless API

 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    "flux-1.1-pro-ultra",
 9    raw=False,
10    seed=1234,
11    prompt="A luxurious, modern living room with a grand fireplace as the centerpiece. Large windows look out onto a dark, rainy night, where wet tree branches glisten under the night sky. The room is decorated with plush, dark blue sofas and warm amber lighting that casts a cozy glow, contrasting with the cool, moody weather outside. The floor is a polished dark wood, and raindrops gently streak down the windows, enhancing the atmosphere of comfort and serenity.",
12    aspect_ratio="1:1",
13    output_format="jpg",
14    safety_tolerance=2,
15)
16print(result["status"])                      # COMPLETED
17print(result.get("output"))                  # model output (e.g. media URL)
18print(result["metrics"]["inference_time"])   # server compute seconds
19
20# --- Or submit + poll manually (track request_id, control the cadence) ---
21from segmind import SegmindClient, InferenceFailed, InferenceTimeout
22
23client = SegmindClient()                      # reads SEGMIND_API_KEY
24payload = {
25    "raw": False,
26    "seed": 1234,
27    "prompt": "A luxurious, modern living room with a grand fireplace as the centerpiece. Large windows look out onto a dark, rainy night, where wet tree branches glisten under the night sky. The room is decorated with plush, dark blue sofas and warm amber lighting that casts a cozy glow, contrasting with the cool, moody weather outside. The floor is a polished dark wood, and raindrops gently streak down the windows, enhancing the atmosphere of comfort and serenity.",
28    "aspect_ratio": "1:1",
29    "output_format": "jpg",
30    "safety_tolerance": 2,
31}
32job = client.submit_async("flux-1.1-pro-ultra", **payload)
33print(job.request_id)                         # available immediately
34try:
35    result = job.wait(timeout=600, interval=1.0)
36except InferenceTimeout as e:
37    print("still running:", e.request_id)
38except InferenceFailed as e:
39    print("failed:", e.detail)