Kling O1 Reference Image 2 Video Serverless API

 1# pip install "segmind>=1.1.0"
 2# export SEGMIND_API_KEY="YOUR_API_KEY"
 3from segmind import SegmindClient, InferenceFailed, InferenceTimeout
 4
 5# Async (v2) — recommended for long-running / video models.
 6# run() blocks up to 600s; submit_async + job.wait(timeout=...) sets a longer
 7# deadline and keeps the request_id so you can re-poll later.
 8client = SegmindClient()                      # reads SEGMIND_API_KEY
 9payload = {
10    "prompt": "Create a cinematic video sequence with smooth camera movements of @image1 riding on @image2",
11    "image_urls": ["https://segmind-resources.s3.amazonaws.com/output/01641ffc-a5dc-4383-a978-8d14f785247d-Screenshot_2025-11-24_120343.png", "https://segmind-resources.s3.amazonaws.com/output/5aab39a0-7dbb-42c3-9269-bf52141ae0ca-horse.jpg"],
12    "duration": "5",
13    "aspect_ratio": "16:9",
14}
15job = client.submit_async("kling-o1-reference-image-to-video", **payload)
16print(job.request_id)                         # available immediately
17try:
18    result = job.wait(timeout=900, interval=2.0)
19    print(result["status"])                  # COMPLETED
20    print(result.get("output"))              # model output (e.g. video URL)
21except InferenceTimeout as e:
22    print("still running:", e.request_id)    # re-poll later with this id
23except InferenceFailed as e:
24    print("failed:", e.detail)
25
26# Fast models (<=600s) can use the one-liner instead:
27# result = segmind.run("kling-o1-reference-image-to-video", **payload)