Wan 2.5 Image to 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    "seed": 42,
11    "image": "https://segmind-resources.s3.amazonaws.com/output/21aeb463-bb17-4536-864b-0bd1e11594a9-EMXN1y8qTgoGdXBsb2FkEg55bGFiLXN0dW50LXNncBo0YWlfcG9ydGFsLzE3NTM5NjM5NTIvbjdwNDlYOURCbS8yZTk0X2wwXzAwMS0wXzAuanBlZw_1000x1000.webp",
12    "prompt": "Kitten in a McDonald's uniform stands on a stool, grilling burger patties. It flips the patties with a spatula, watches them sizzle, and occasionally looks around while steam rises from the grill.",
13    "duration": "5",
14    "resolution": "720p",
15    "negative_prompt": "unnecessary clutter, dark shadows",
16    "enable_prompt_expansion": True,
17}
18job = client.submit_async("wan-2.5-i2v", **payload)
19print(job.request_id)                         # available immediately
20try:
21    result = job.wait(timeout=900, interval=2.0)
22    print(result["status"])                  # COMPLETED
23    print(result.get("output"))              # model output (e.g. video URL)
24except InferenceTimeout as e:
25    print("still running:", e.request_id)    # re-poll later with this id
26except InferenceFailed as e:
27    print("failed:", e.detail)
28
29# Fast models (<=600s) can use the one-liner instead:
30# result = segmind.run("wan-2.5-i2v", **payload)