Imagen 4 Ultra Serverless API
Photorealistic images with native 2K resolution and precise text.
POST /v2/imagen-4-ultra · 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 "imagen-4-ultra",
9 prompt="A hyperrealistic portrait of a young woman with freckles in warm golden hour sunlight, cinematic shallow depth of field, ultra-detailed skin texture, professional photography, 8K",
10 negative_prompt="blurry, pixelated, ugly, deformed, cartoon, painting",
11 aspect_ratio="1:1",
12)
13print(result["status"]) # COMPLETED
14print(result.get("output")) # model output (e.g. media URL)
15print(result["metrics"]["inference_time"]) # server compute seconds
16
17# --- Or submit + poll manually (track request_id, control the cadence) ---
18from segmind import SegmindClient, InferenceFailed, InferenceTimeout
19
20client = SegmindClient() # reads SEGMIND_API_KEY
21payload = {
22 "prompt": "A hyperrealistic portrait of a young woman with freckles in warm golden hour sunlight, cinematic shallow depth of field, ultra-detailed skin texture, professional photography, 8K",
23 "negative_prompt": "blurry, pixelated, ugly, deformed, cartoon, painting",
24 "aspect_ratio": "1:1",
25}
26job = client.submit_async("imagen-4-ultra", **payload)
27print(job.request_id) # available immediately
28try:
29 result = job.wait(timeout=600, interval=1.0)
30except InferenceTimeout as e:
31 print("still running:", e.request_id)
32except InferenceFailed as e:
33 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 "imagen-4-ultra",
9 prompt="A hyperrealistic portrait of a young woman with freckles in warm golden hour sunlight, cinematic shallow depth of field, ultra-detailed skin texture, professional photography, 8K",
10 negative_prompt="blurry, pixelated, ugly, deformed, cartoon, painting",
11 aspect_ratio="1:1",
12)
13print(result["status"]) # COMPLETED
14print(result.get("output")) # model output (e.g. media URL)
15print(result["metrics"]["inference_time"]) # server compute seconds
16
17# --- Or submit + poll manually (track request_id, control the cadence) ---
18from segmind import SegmindClient, InferenceFailed, InferenceTimeout
19
20client = SegmindClient() # reads SEGMIND_API_KEY
21payload = {
22 "prompt": "A hyperrealistic portrait of a young woman with freckles in warm golden hour sunlight, cinematic shallow depth of field, ultra-detailed skin texture, professional photography, 8K",
23 "negative_prompt": "blurry, pixelated, ugly, deformed, cartoon, painting",
24 "aspect_ratio": "1:1",
25}
26job = client.submit_async("imagen-4-ultra", **payload)
27print(job.request_id) # available immediately
28try:
29 result = job.wait(timeout=600, interval=1.0)
30except InferenceTimeout as e:
31 print("still running:", e.request_id)
32except InferenceFailed as e:
33 print("failed:", e.detail)API Endpoint
https://api.segmind.com/v1/imagen-4-ultraParameters
promptrequiredstringDescribe your subject with style, lighting, and composition details. More specificity yields significantly better photorealistic results.
"A hyperrealistic portrait of a young woman with freckles in warm golden hour sunlight, cinematic shallow depth of field, ultra-detailed skin texture, professional photography, 8K"aspect_ratiooptionalstringSets image shape based on final use — 1:1 for social media, 16:9 for banners, 9:16 for Stories.
"1:1""1:1""4:3""3:4""9:16""16:9"negative_promptoptionalstringList elements to exclude from the image. Effective for removing artifacts, cartoonish looks, or unwanted visual styles.
"blurry, pixelated, ugly, deformed, cartoon, painting"Response 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/imagen-4-ultraSubmit — 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