Kling 2 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": "A photograph of a giant panda swimming in a crystal-clear outdoor pool. The panda is gracefully paddling with its black and white fur glistening in the sunlight, its playful expression clearly visible. The pool is surrounded by lush green foliage and colorful flowers, with a wooden deck leading to a grassy lawn. Soft, natural light bathes the scene, highlighting the water's clarity and the panda's movements.",
11    "duration": 5,
12    "cfg_scale": 0.5,
13    "start_image": "https://segmind-resources.s3.amazonaws.com/input/65a44af9-de76-474e-898f-57e83b0ff3b3-a-photograph-of-a-giant-panda-swimming-i_k_2X05UAQYWIOoZJLuVpyg_eFEiq19KSYO9FzXR4tdibQ.jpeg",
14    "aspect_ratio": "16:9",
15}
16job = client.submit_async("kling-2", **payload)
17print(job.request_id)                         # available immediately
18try:
19    result = job.wait(timeout=900, interval=2.0)
20    print(result["status"])                  # COMPLETED
21    print(result.get("output"))              # model output (e.g. video URL)
22except InferenceTimeout as e:
23    print("still running:", e.request_id)    # re-poll later with this id
24except InferenceFailed as e:
25    print("failed:", e.detail)
26
27# Fast models (<=600s) can use the one-liner instead:
28# result = segmind.run("kling-2", **payload)