flux-pro-1.1 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",
 9    seed=12345,
10    width=1024,
11    height=1024,
12    prompt="A photo of an astronaut with a mirrored helmet standing in a field of sunflowers. The spacesuit has a NASA logo on it. The astronaut is covered in dirt and has vines wrapped around his body. The sky is dark and cloudy.",
13    aspect_ratio="1:1",
14    output_format="png",
15    output_quality=80,
16    safety_tolerance=2,
17    prompt_upsampling=False,
18)
19print(result["status"])                      # COMPLETED
20print(result.get("output"))                  # model output (e.g. media URL)
21print(result["metrics"]["inference_time"])   # server compute seconds
22
23# --- Or submit + poll manually (track request_id, control the cadence) ---
24from segmind import SegmindClient, InferenceFailed, InferenceTimeout
25
26client = SegmindClient()                      # reads SEGMIND_API_KEY
27payload = {
28    "seed": 12345,
29    "width": 1024,
30    "height": 1024,
31    "prompt": "A photo of an astronaut with a mirrored helmet standing in a field of sunflowers. The spacesuit has a NASA logo on it. The astronaut is covered in dirt and has vines wrapped around his body. The sky is dark and cloudy.",
32    "aspect_ratio": "1:1",
33    "output_format": "png",
34    "output_quality": 80,
35    "safety_tolerance": 2,
36    "prompt_upsampling": False,
37}
38job = client.submit_async("flux-1.1-pro", **payload)
39print(job.request_id)                         # available immediately
40try:
41    result = job.wait(timeout=600, interval=1.0)
42except InferenceTimeout as e:
43    print("still running:", e.request_id)
44except InferenceFailed as e:
45    print("failed:", e.detail)