Stable Diffusion 3 Turbo Image to Image 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    "stable-diffusion-3-turbo-img2img",
 9    mode="image-to-image",
10    image="https://segmind-sd-models.s3.amazonaws.com/display_images/sd3-turbo-i2i-input.jpg",
11    prompt="cyberpunk style frog, dark colors",
12    strength=1,
13    output_format="jpeg",
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    "mode": "image-to-image",
26    "image": "https://segmind-sd-models.s3.amazonaws.com/display_images/sd3-turbo-i2i-input.jpg",
27    "prompt": "cyberpunk style frog, dark colors",
28    "strength": 1,
29    "output_format": "jpeg",
30    "base64": False,
31}
32job = client.submit_async("stable-diffusion-3-turbo-img2img", **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)