Simple Vector Flux Lora 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    "Simple_Vector_Flux",
 9    prompt="v3ct0r style, simple vector art, salesman giving a thumbs up in front of a car, character asset, clip art",
10    steps=25,
11    seed=6652105,
12    scheduler="simple",
13    sampler_name="euler",
14    aspect_ratio="1:1",
15    lora_strength=1,
16)
17print(result["status"])                      # COMPLETED
18print(result.get("output"))                  # model output (e.g. media URL)
19print(result["metrics"]["inference_time"])   # server compute seconds
20
21# --- Or submit + poll manually (track request_id, control the cadence) ---
22from segmind import SegmindClient, InferenceFailed, InferenceTimeout
23
24client = SegmindClient()                      # reads SEGMIND_API_KEY
25payload = {
26    "prompt": "v3ct0r style, simple vector art, salesman giving a thumbs up in front of a car, character asset, clip art",
27    "steps": 25,
28    "seed": 6652105,
29    "scheduler": "simple",
30    "sampler_name": "euler",
31    "aspect_ratio": "1:1",
32    "lora_strength": 1,
33}
34job = client.submit_async("Simple_Vector_Flux", **payload)
35print(job.request_id)                         # available immediately
36try:
37    result = job.wait(timeout=600, interval=1.0)
38except InferenceTimeout as e:
39    print("still running:", e.request_id)
40except InferenceFailed as e:
41    print("failed:", e.detail)