Magic Eraser 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    "magic-eraser",
 9    image="https://segmind-sd-models.s3.amazonaws.com/display_images/ai-majic-erase-ip.jpg",
10    mask="https://segmind-sd-models.s3.amazonaws.com/display_images/ai-majic-erase-mask.jpeg",
11    invert_mask=False,
12    grow_mask=10,
13    seed=468685,
14    base64=False,
15)
16print(result["status"])                      # COMPLETED
17print(result.get("output"))                  # model output (e.g. media URL)
18print(result["metrics"]["inference_time"])   # server compute seconds
19
20# --- Or submit + poll manually (track request_id, control the cadence) ---
21from segmind import SegmindClient, InferenceFailed, InferenceTimeout
22
23client = SegmindClient()                      # reads SEGMIND_API_KEY
24payload = {
25    "image": "https://segmind-sd-models.s3.amazonaws.com/display_images/ai-majic-erase-ip.jpg",
26    "mask": "https://segmind-sd-models.s3.amazonaws.com/display_images/ai-majic-erase-mask.jpeg",
27    "invert_mask": False,
28    "grow_mask": 10,
29    "seed": 468685,
30    "base64": False,
31}
32job = client.submit_async("magic-eraser", **payload)
33print(job.request_id)                         # available immediately
34try:
35    result = job.wait(timeout=600, interval=1.0)
36except InferenceTimeout as e:
37    print("still running:", e.request_id)
38except InferenceFailed as e:
39    print("failed:", e.detail)