Scifi Serverless API
The most versatile photorealistic model that blends various models to achieve the amazing realistic images.
POST /v2/sd1.5-scifi · submit + poll 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-scifi",
9 prompt="futuristic sci-fi high-tech city of Atlantis in late afternoon light, wispy clouds in a blue sky",
10 negative_prompt="blurry, blurred, amateur, ugly, low quality, sketch, low resolution, warped, crooked, deformed, cartoony, low detail",
11 scheduler="dpmpp_2m",
12 num_inference_steps=30,
13 guidance_scale=5,
14 samples=1,
15 seed=516797155652,
16 img_width=512,
17 img_height=768,
18 base64=False,
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 "prompt": "futuristic sci-fi high-tech city of Atlantis in late afternoon light, wispy clouds in a blue sky",
30 "negative_prompt": "blurry, blurred, amateur, ugly, low quality, sketch, low resolution, warped, crooked, deformed, cartoony, low detail",
31 "scheduler": "dpmpp_2m",
32 "num_inference_steps": 30,
33 "guidance_scale": 5,
34 "samples": 1,
35 "seed": 516797155652,
36 "img_width": 512,
37 "img_height": 768,
38 "base64": False,
39}
40job = client.submit_async("sd1.5-scifi", **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) 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-scifi",
9 prompt="futuristic sci-fi high-tech city of Atlantis in late afternoon light, wispy clouds in a blue sky",
10 negative_prompt="blurry, blurred, amateur, ugly, low quality, sketch, low resolution, warped, crooked, deformed, cartoony, low detail",
11 scheduler="dpmpp_2m",
12 num_inference_steps=30,
13 guidance_scale=5,
14 samples=1,
15 seed=516797155652,
16 img_width=512,
17 img_height=768,
18 base64=False,
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 "prompt": "futuristic sci-fi high-tech city of Atlantis in late afternoon light, wispy clouds in a blue sky",
30 "negative_prompt": "blurry, blurred, amateur, ugly, low quality, sketch, low resolution, warped, crooked, deformed, cartoony, low detail",
31 "scheduler": "dpmpp_2m",
32 "num_inference_steps": 30,
33 "guidance_scale": 5,
34 "samples": 1,
35 "seed": 516797155652,
36 "img_width": 512,
37 "img_height": 768,
38 "base64": False,
39}
40job = client.submit_async("sd1.5-scifi", **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)API Endpoint
https://api.segmind.com/v1/sd1.5-scifiParameters
promptrequiredstringPrompt to render
base64optionalbooleanBase64 encoding of the output image.
falseguidance_scaleoptionalnumberScale for classifier-free guidance
7.5Range: 0.1 - 25img_heightoptionalintegerHeight of the Image
5125127681024img_widthoptionalintegerWidth of the image.
5125127681024negative_promptoptionalstringPrompts to exclude, eg. 'bad anatomy, bad hands, missing fingers'
num_inference_stepsoptionalintegerNumber of denoising steps.
20Range: 20 - 100samplesoptionalintegerNumber of samples to generate.
1Range: 1 - 4scheduleroptionalstringType of scheduler.
"UniPC""DDIM""DPM Multi""DPM Single""Euler a""Euler""Heun""DPM2 a Karras""DPM2 Karras""LMS""PNDM"+2 moreseedoptionalintegerSeed for image generation.
-1Response Type
Returns: Text/JSON
Asynchronous requests (v2)
Use Async for video, long-running (>~60s), or high-concurrency workloads; Sync is simplest for fast image & LLM calls. Async submits a request and you poll it to completion.
- 1
POST /v2/sd1.5-scifiSubmit — returns request_id, status_url, response_url
- 2
GET /v2/requests/{id}/statusPoll — until COMPLETED or FAILED
- 3
GET /v2/requests/{id}Result — final response body
Status states
- A FAILED request is served as HTTP 422 — the body still carries the error detail.
- An unknown or expired request_id returns HTTP 404.
- Results are retained for 1 hour, then expire.
- Content / RAI blocks surface as FAILED, not a separate state.
- Track completion by polling the status endpoint.
Common Error Codes
The API returns standard HTTP status codes. Detailed error messages are provided in the response body.
Bad Request
Invalid parameters or request format
Unauthorized
Missing or invalid API key
Forbidden
Insufficient permissions
Not Found
Model or endpoint not found
Insufficient Credits
Not enough credits to process request
Rate Limited
Too many requests
Server Error
Internal server error
Bad Gateway
Service temporarily unavailable
Timeout
Request timed out