SSD-1B 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    "ssd-1b",
 9    prompt="a futuristic cityscape at dusk, neon lights, reflections in water, ultrarealistic, high contrast, vibrant",
10    negative_prompt="blurry,out of focus",
11    samples=1,
12    scheduler="DPM Multi",
13    num_inference_steps=45,
14    guidance_scale=7,
15    seed=9876543210,
16    img_width=1024,
17    img_height=1024,
18    base64=False,
19)
20print(result["status"])                      # COMPLETED
21print(result.get("output"))                  # model output (e.g. media URL)
22print(result["metrics"]["inference_time"])   # server compute seconds
23
24# --- Or submit + poll manually (track request_id, control the cadence) ---
25from segmind import SegmindClient, InferenceFailed, InferenceTimeout
26
27client = SegmindClient()                      # reads SEGMIND_API_KEY
28payload = {
29    "prompt": "a futuristic cityscape at dusk, neon lights, reflections in water, ultrarealistic, high contrast, vibrant",
30    "negative_prompt": "blurry,out of focus",
31    "samples": 1,
32    "scheduler": "DPM Multi",
33    "num_inference_steps": 45,
34    "guidance_scale": 7,
35    "seed": 9876543210,
36    "img_width": 1024,
37    "img_height": 1024,
38    "base64": False,
39}
40job = client.submit_async("ssd-1b", **payload)
41print(job.request_id)                         # available immediately
42try:
43    result = job.wait(timeout=600, interval=1.0)
44except InferenceTimeout as e:
45    print("still running:", e.request_id)
46except InferenceFailed as e:
47    print("failed:", e.detail)