Stable Diffusion Inpainting 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    "sd1.5-inpainting",
 9    prompt="mecha robot sitting on a bench",
10    negative_prompt="Disfigured, cartoon, blurry, nude",
11    samples=1,
12    image="https://segmind.com/inpainting-input-image.jpeg",
13    mask="https://segmind.com/inpainting-input-mask.jpeg",
14    scheduler="DDIM",
15    num_inference_steps=25,
16    guidance_scale=7.5,
17    strength=1,
18    seed=17123564234,
19    img_width=512,
20    img_height=512,
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": "mecha robot sitting on a bench",
32    "negative_prompt": "Disfigured, cartoon, blurry, nude",
33    "samples": 1,
34    "image": "https://segmind.com/inpainting-input-image.jpeg",
35    "mask": "https://segmind.com/inpainting-input-mask.jpeg",
36    "scheduler": "DDIM",
37    "num_inference_steps": 25,
38    "guidance_scale": 7.5,
39    "strength": 1,
40    "seed": 17123564234,
41    "img_width": 512,
42    "img_height": 512,
43}
44job = client.submit_async("sd1.5-inpainting", **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)