Wan 2.7 Image Generation Pro 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    "wan2.7-image-pro",
 9    prompt="A breathtaking aerial view of a futuristic city at golden hour, towering glass skyscrapers with lush vertical gardens, flying vehicles, cinematic lighting, ultra-detailed, photorealistic",
10    negative_prompt="blurry, low quality, distorted, watermark, text",
11    size="2K",
12    seed=42,
13    watermark=False,
14)
15print(result["status"])                      # COMPLETED
16print(result.get("output"))                  # model output (e.g. media URL)
17print(result["metrics"]["inference_time"])   # server compute seconds
18
19# --- Or submit + poll manually (track request_id, control the cadence) ---
20from segmind import SegmindClient, InferenceFailed, InferenceTimeout
21
22client = SegmindClient()                      # reads SEGMIND_API_KEY
23payload = {
24    "prompt": "A breathtaking aerial view of a futuristic city at golden hour, towering glass skyscrapers with lush vertical gardens, flying vehicles, cinematic lighting, ultra-detailed, photorealistic",
25    "negative_prompt": "blurry, low quality, distorted, watermark, text",
26    "size": "2K",
27    "seed": 42,
28    "watermark": False,
29}
30job = client.submit_async("wan2.7-image-pro", **payload)
31print(job.request_id)                         # available immediately
32try:
33    result = job.wait(timeout=600, interval=1.0)
34except InferenceTimeout as e:
35    print("still running:", e.request_id)
36except InferenceFailed as e:
37    print("failed:", e.detail)