IDM VTON 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    "idm-vton",
 9    crop=False,
10    seed=42,
11    steps=30,
12    category="upper_body",
13    force_dc=False,
14    human_img="https://segmind-sd-models.s3.amazonaws.com/display_images/idm-ip.png",
15    garm_img="https://segmind-sd-models.s3.amazonaws.com/display_images/idm-viton-dress.png",
16    mask_only=False,
17    garment_des="Green colour semi Formal Blazer",
18)
19print(result["status"])                      # COMPLETED
20print(result.get("output"))                  # model output (e.g. media URL)
21print(result["metrics"]["inference_time"])   # server compute seconds
22
23# --- Or submit + poll manually (track request_id, control the cadence) ---
24from segmind import SegmindClient, InferenceFailed, InferenceTimeout
25
26client = SegmindClient()                      # reads SEGMIND_API_KEY
27payload = {
28    "crop": False,
29    "seed": 42,
30    "steps": 30,
31    "category": "upper_body",
32    "force_dc": False,
33    "human_img": "https://segmind-sd-models.s3.amazonaws.com/display_images/idm-ip.png",
34    "garm_img": "https://segmind-sd-models.s3.amazonaws.com/display_images/idm-viton-dress.png",
35    "mask_only": False,
36    "garment_des": "Green colour semi Formal Blazer",
37}
38job = client.submit_async("idm-vton", **payload)
39print(job.request_id)                         # available immediately
40try:
41    result = job.wait(timeout=600, interval=1.0)
42except InferenceTimeout as e:
43    print("still running:", e.request_id)
44except InferenceFailed as e:
45    print("failed:", e.detail)