MiniMax H3 Text to Image 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    "minimax-h3-text-to-image",
 9    prompt="A straight-on view of a Victorian herbalist's cabinet: a grid of small oak drawers, each with a round brass cup handle and a cream card label in a brass frame. The front labels read 'VALERIANA', 'LAVANDULA', 'CALENDULA' and 'ARNICA' in black serif capitals. A hand-painted wooden header board above the drawers reads 'HOLLOWAY HERBALIST' with 'EST. 1894' beneath in gold leaf. A brass mortar and pestle and a set of apothecary scales rest on the counter below. Even daylight from a side window, aged wood grain and tarnished brass.",
10    speed="standard",
11    num_images=1,
12    area="4K",
13    aspect_ratio="4:3",
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 straight-on view of a Victorian herbalist's cabinet: a grid of small oak drawers, each with a round brass cup handle and a cream card label in a brass frame. The front labels read 'VALERIANA', 'LAVANDULA', 'CALENDULA' and 'ARNICA' in black serif capitals. A hand-painted wooden header board above the drawers reads 'HOLLOWAY HERBALIST' with 'EST. 1894' beneath in gold leaf. A brass mortar and pestle and a set of apothecary scales rest on the counter below. Even daylight from a side window, aged wood grain and tarnished brass.",
25    "speed": "standard",
26    "num_images": 1,
27    "area": "4K",
28    "aspect_ratio": "4:3",
29}
30job = client.submit_async("minimax-h3-text-to-image", **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)