Luma Uni-1 Max 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    "luma-uni-1-max",
 9    prompt="Change the sky to a dramatic sunset with warm orange and pink clouds, keep the town, houses, boats and water unchanged",
10    image="https://segmind-resources.s3.amazonaws.com/input/luma-uni-1-max-source.jpg",
11    aspect_ratio="16:9",
12    style="auto",
13)
14print(result["status"])                      # COMPLETED
15print(result.get("output"))                  # model output (e.g. media URL)
16print(result["metrics"]["inference_time"])   # server compute seconds
17
18# --- Or submit + poll manually (track request_id, control the cadence) ---
19from segmind import SegmindClient, InferenceFailed, InferenceTimeout
20
21client = SegmindClient()                      # reads SEGMIND_API_KEY
22payload = {
23    "prompt": "Change the sky to a dramatic sunset with warm orange and pink clouds, keep the town, houses, boats and water unchanged",
24    "image": "https://segmind-resources.s3.amazonaws.com/input/luma-uni-1-max-source.jpg",
25    "aspect_ratio": "16:9",
26    "style": "auto",
27}
28job = client.submit_async("luma-uni-1-max", **payload)
29print(job.request_id)                         # available immediately
30try:
31    result = job.wait(timeout=600, interval=1.0)
32except InferenceTimeout as e:
33    print("still running:", e.request_id)
34except InferenceFailed as e:
35    print("failed:", e.detail)