Flux Canny Pro 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    "flux-canny-pro",
 9    seed=965778,
10    steps=40,
11    prompt="photo of a car on a street",
12    guidance=30,
13    control_image="https://segmind-sd-models.s3.us-east-1.amazonaws.com/display_images/flux-tools/flux-canny-pro-ip.jpg",
14    output_format="jpg",
15    safety_tolerance=2,
16    prompt_upsampling=False,
17)
18print(result["status"])                      # COMPLETED
19print(result.get("output"))                  # model output (e.g. media URL)
20print(result["metrics"]["inference_time"])   # server compute seconds
21
22# --- Or submit + poll manually (track request_id, control the cadence) ---
23from segmind import SegmindClient, InferenceFailed, InferenceTimeout
24
25client = SegmindClient()                      # reads SEGMIND_API_KEY
26payload = {
27    "seed": 965778,
28    "steps": 40,
29    "prompt": "photo of a car on a street",
30    "guidance": 30,
31    "control_image": "https://segmind-sd-models.s3.us-east-1.amazonaws.com/display_images/flux-tools/flux-canny-pro-ip.jpg",
32    "output_format": "jpg",
33    "safety_tolerance": 2,
34    "prompt_upsampling": False,
35}
36job = client.submit_async("flux-canny-pro", **payload)
37print(job.request_id)                         # available immediately
38try:
39    result = job.wait(timeout=600, interval=1.0)
40except InferenceTimeout as e:
41    print("still running:", e.request_id)
42except InferenceFailed as e:
43    print("failed:", e.detail)