Stable Diffusion 3.5 Large Text 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-3.5-large-txt2img",
 9    prompt="~*~ aesthetic ~*~ #boho #fashion full-body 30-something woman laying on microfloral grass, candid pose, overlay reads Stable Diffusion 3.5, cheerful cursive typography font,",
10    negative_prompt="low quality, blurry",
11    steps=25,
12    guidance_scale=5.5,
13    seed=98552302,
14    sampler="euler",
15    scheduler="sgm_uniform",
16    width=1024,
17    height=1024,
18    aspect_ratio="custom",
19    batch_size=1,
20    image_format="jpeg",
21    image_quality=95,
22    base64=False,
23)
24print(result["status"])                      # COMPLETED
25print(result.get("output"))                  # model output (e.g. media URL)
26print(result["metrics"]["inference_time"])   # server compute seconds
27
28# --- Or submit + poll manually (track request_id, control the cadence) ---
29from segmind import SegmindClient, InferenceFailed, InferenceTimeout
30
31client = SegmindClient()                      # reads SEGMIND_API_KEY
32payload = {
33    "prompt": "~*~ aesthetic ~*~ #boho #fashion full-body 30-something woman laying on microfloral grass, candid pose, overlay reads Stable Diffusion 3.5, cheerful cursive typography font,",
34    "negative_prompt": "low quality, blurry",
35    "steps": 25,
36    "guidance_scale": 5.5,
37    "seed": 98552302,
38    "sampler": "euler",
39    "scheduler": "sgm_uniform",
40    "width": 1024,
41    "height": 1024,
42    "aspect_ratio": "custom",
43    "batch_size": 1,
44    "image_format": "jpeg",
45    "image_quality": 95,
46    "base64": False,
47}
48job = client.submit_async("stable-diffusion-3.5-large-txt2img", **payload)
49print(job.request_id)                         # available immediately
50try:
51    result = job.wait(timeout=600, interval=1.0)
52except InferenceTimeout as e:
53    print("still running:", e.request_id)
54except InferenceFailed as e:
55    print("failed:", e.detail)