IPAdapter Style Transfer 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    "style-transfer",
 9    model="fast",
10    width=1024,
11    height=1024,
12    prompt="A panda running on a jogging track",
13    style_image="https://segmind-inference-io.s3.amazonaws.com/sample_images/40548cb9-ae1f-4caa-969d-382c913a74ef.webp",
14    output_format="webp",
15    output_quality=80,
16    number_of_images=1,
17    structure_depth_strength=1,
18    structure_denoising_strength=0.65,
19)
20print(result["status"])                      # COMPLETED
21print(result.get("output"))                  # model output (e.g. media URL)
22print(result["metrics"]["inference_time"])   # server compute seconds
23
24# --- Or submit + poll manually (track request_id, control the cadence) ---
25from segmind import SegmindClient, InferenceFailed, InferenceTimeout
26
27client = SegmindClient()                      # reads SEGMIND_API_KEY
28payload = {
29    "model": "fast",
30    "width": 1024,
31    "height": 1024,
32    "prompt": "A panda running on a jogging track",
33    "style_image": "https://segmind-inference-io.s3.amazonaws.com/sample_images/40548cb9-ae1f-4caa-969d-382c913a74ef.webp",
34    "output_format": "webp",
35    "output_quality": 80,
36    "number_of_images": 1,
37    "structure_depth_strength": 1,
38    "structure_denoising_strength": 0.65,
39}
40job = client.submit_async("style-transfer", **payload)
41print(job.request_id)                         # available immediately
42try:
43    result = job.wait(timeout=600, interval=1.0)
44except InferenceTimeout as e:
45    print("still running:", e.request_id)
46except InferenceFailed as e:
47    print("failed:", e.detail)