Controlnet 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    "inpaint-auto",
 9    image="https://segmind-sd-models.s3.amazonaws.com/outputs/inpaint_auto.jpeg",
10    prompt="woman in space suit , underwater, full body, floating in water, air bubbles, detailed eyes, deep sea in background, water surface, god ray, fish",
11    negative_prompt="disfigured, deformed, ugly",
12    samples=1,
13    base_model="SDXL",
14    cn_model="Depth",
15    cn_processor="DPM++ 2M SDE Karras",
16    scheduler="DPM++ 2M SDE Karras",
17    num_inference_steps=25,
18    guidance_scale=7,
19    seed=12467,
20    strength=0.9,
21    base64=False,
22)
23print(result["status"])                      # COMPLETED
24print(result.get("output"))                  # model output (e.g. media URL)
25print(result["metrics"]["inference_time"])   # server compute seconds
26
27# --- Or submit + poll manually (track request_id, control the cadence) ---
28from segmind import SegmindClient, InferenceFailed, InferenceTimeout
29
30client = SegmindClient()                      # reads SEGMIND_API_KEY
31payload = {
32    "image": "https://segmind-sd-models.s3.amazonaws.com/outputs/inpaint_auto.jpeg",
33    "prompt": "woman in space suit , underwater, full body, floating in water, air bubbles, detailed eyes, deep sea in background, water surface, god ray, fish",
34    "negative_prompt": "disfigured, deformed, ugly",
35    "samples": 1,
36    "base_model": "SDXL",
37    "cn_model": "Depth",
38    "cn_processor": "DPM++ 2M SDE Karras",
39    "scheduler": "DPM++ 2M SDE Karras",
40    "num_inference_steps": 25,
41    "guidance_scale": 7,
42    "seed": 12467,
43    "strength": 0.9,
44    "base64": False,
45}
46job = client.submit_async("inpaint-auto", **payload)
47print(job.request_id)                         # available immediately
48try:
49    result = job.wait(timeout=600, interval=1.0)
50except InferenceTimeout as e:
51    print("still running:", e.request_id)
52except InferenceFailed as e:
53    print("failed:", e.detail)