Stable Diffusion XL 0.9 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    "sdxl-txt2img",
 9    prompt="a beautiful stack of rocks sitting on top of a beach, a picture, red black white golden colors, chakras, packshot, stock photo",
10    negative_prompt="asian, african, makeup, fake, cgi, 3d, doll, art, anime, cartoon, illustration, painting, digital, blur, sepia, b&w, unnatural, unreal, photoshop, filter, airbrush, poor anatomy, lr",
11    samples=1,
12    scheduler="dpmpp_sde_ancestral",
13    num_inference_steps=25,
14    guidance_scale=8,
15    strength=1,
16    seed=2784983004,
17    img_width=768,
18    img_height=768,
19    base64=False,
20)
21print(result["status"])                      # COMPLETED
22print(result.get("output"))                  # model output (e.g. media URL)
23print(result["metrics"]["inference_time"])   # server compute seconds
24
25# --- Or submit + poll manually (track request_id, control the cadence) ---
26from segmind import SegmindClient, InferenceFailed, InferenceTimeout
27
28client = SegmindClient()                      # reads SEGMIND_API_KEY
29payload = {
30    "prompt": "a beautiful stack of rocks sitting on top of a beach, a picture, red black white golden colors, chakras, packshot, stock photo",
31    "negative_prompt": "asian, african, makeup, fake, cgi, 3d, doll, art, anime, cartoon, illustration, painting, digital, blur, sepia, b&w, unnatural, unreal, photoshop, filter, airbrush, poor anatomy, lr",
32    "samples": 1,
33    "scheduler": "dpmpp_sde_ancestral",
34    "num_inference_steps": 25,
35    "guidance_scale": 8,
36    "strength": 1,
37    "seed": 2784983004,
38    "img_width": 768,
39    "img_height": 768,
40    "base64": False,
41}
42job = client.submit_async("sdxl-txt2img", **payload)
43print(job.request_id)                         # available immediately
44try:
45    result = job.wait(timeout=600, interval=1.0)
46except InferenceTimeout as e:
47    print("still running:", e.request_id)
48except InferenceFailed as e:
49    print("failed:", e.detail)