Stable Diffusion img2img 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-img2img",
 9    image="/sd-img2img-input.jpeg",
10    samples=1,
11    prompt="A fantasy landscape, trending on artstation, mystical sky",
12    negative_prompt="nude, disfigured, blurry",
13    scheduler="DDIM",
14    num_inference_steps=25,
15    guidance_scale=10.5,
16    strength=0.75,
17    seed=98877465625,
18    img_width=512,
19    img_height=512,
20    base64=False,
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    "image": "/sd-img2img-input.jpeg",
32    "samples": 1,
33    "prompt": "A fantasy landscape, trending on artstation, mystical sky",
34    "negative_prompt": "nude, disfigured, blurry",
35    "scheduler": "DDIM",
36    "num_inference_steps": 25,
37    "guidance_scale": 10.5,
38    "strength": 0.75,
39    "seed": 98877465625,
40    "img_width": 512,
41    "img_height": 512,
42    "base64": False,
43}
44job = client.submit_async("sd1.5-img2img", **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)