Flux Realism Lora with Upscale 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    "flux-realism-lora",
 9    prompt="a young woman smiling while speaking onstage from segmind, white background with corporate logos blurred out, tech conference",
10    steps=20,
11    seed=6652105,
12    scheduler="simple",
13    sampler_name="euler",
14    aspect_ratio="2:3",
15    width=1024,
16    height=1024,
17    upscale_value=2,
18    lora_strength=0.8,
19    samples=1,
20    upscale=False,
21)
22print(result["status"])                      # COMPLETED
23print(result.get("output"))                  # model output (e.g. media URL)
24print(result["metrics"]["inference_time"])   # server compute seconds
25
26# --- Or submit + poll manually (track request_id, control the cadence) ---
27from segmind import SegmindClient, InferenceFailed, InferenceTimeout
28
29client = SegmindClient()                      # reads SEGMIND_API_KEY
30payload = {
31    "prompt": "a young woman smiling while speaking onstage from segmind, white background with corporate logos blurred out, tech conference",
32    "steps": 20,
33    "seed": 6652105,
34    "scheduler": "simple",
35    "sampler_name": "euler",
36    "aspect_ratio": "2:3",
37    "width": 1024,
38    "height": 1024,
39    "upscale_value": 2,
40    "lora_strength": 0.8,
41    "samples": 1,
42    "upscale": False,
43}
44job = client.submit_async("flux-realism-lora", **payload)
45print(job.request_id)                         # available immediately
46try:
47    result = job.wait(timeout=600, interval=1.0)
48except InferenceTimeout as e:
49    print("still running:", e.request_id)
50except InferenceFailed as e:
51    print("failed:", e.detail)