Stable Diffusion 3 Large Text to Image Serverless API
Stable Diffusion is a type of latent diffusion model that can generate images from text. It was created by a team of researchers and engineers from CompVis, Stability AI, and LAION. Stable Diffusion v2 is a specific version of the model architecture. It utilizes a downsampling-factor 8 autoencoder with an 865M UNet and OpenCLIP ViT-H/14 text encoder for the diffusion model. When using the SD 2-v model, it produces 768x768 px images. It uses the penultimate text embeddings from a CLIP ViT-H/14 text encoder to condition the generation process.
POST /v2/stable-diffusion-3-large-txt2img · 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 "stable-diffusion-3-large-txt2img",
9 prompt="A whimsical and high-resolution highly realistic image of a panda in a vintage cosmonaut suit. The panda is holding a sign that reads 'I love flying to the moon!' in playful lettering. The panda's helmet has a small propeller on top and a Indian flag patch, adding to the cosmic vibe. The background features a retro-styled spaceship with rockets and stars, giving the impression of a thrilling journey through space",
10 mode="text-to-image",
11 aspect_ratio="1:1",
12 output_format="jpeg",
13 base64=False,
14 negative_prompt="ugly, blurry, bad anatomy, blurred, watermark, grainy, signature, cut off, draft",
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 "prompt": "A whimsical and high-resolution highly realistic image of a panda in a vintage cosmonaut suit. The panda is holding a sign that reads 'I love flying to the moon!' in playful lettering. The panda's helmet has a small propeller on top and a Indian flag patch, adding to the cosmic vibe. The background features a retro-styled spaceship with rockets and stars, giving the impression of a thrilling journey through space",
26 "mode": "text-to-image",
27 "aspect_ratio": "1:1",
28 "output_format": "jpeg",
29 "base64": False,
30 "negative_prompt": "ugly, blurry, bad anatomy, blurred, watermark, grainy, signature, cut off, draft",
31}
32job = client.submit_async("stable-diffusion-3-large-txt2img", **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) 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-large-txt2img",
9 prompt="A whimsical and high-resolution highly realistic image of a panda in a vintage cosmonaut suit. The panda is holding a sign that reads 'I love flying to the moon!' in playful lettering. The panda's helmet has a small propeller on top and a Indian flag patch, adding to the cosmic vibe. The background features a retro-styled spaceship with rockets and stars, giving the impression of a thrilling journey through space",
10 mode="text-to-image",
11 aspect_ratio="1:1",
12 output_format="jpeg",
13 base64=False,
14 negative_prompt="ugly, blurry, bad anatomy, blurred, watermark, grainy, signature, cut off, draft",
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 "prompt": "A whimsical and high-resolution highly realistic image of a panda in a vintage cosmonaut suit. The panda is holding a sign that reads 'I love flying to the moon!' in playful lettering. The panda's helmet has a small propeller on top and a Indian flag patch, adding to the cosmic vibe. The background features a retro-styled spaceship with rockets and stars, giving the impression of a thrilling journey through space",
26 "mode": "text-to-image",
27 "aspect_ratio": "1:1",
28 "output_format": "jpeg",
29 "base64": False,
30 "negative_prompt": "ugly, blurry, bad anatomy, blurred, watermark, grainy, signature, cut off, draft",
31}
32job = client.submit_async("stable-diffusion-3-large-txt2img", **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)API Endpoint
https://api.segmind.com/v1/stable-diffusion-3-large-txt2imgParameters
aspect_ratiooptionalstringaspect_ratio
"1:1""16:9""1:1""21:9""2:3""3:2""4:5""5:4""9:16""9:21"base64optionalbooleanBase64 encoding of the output image.
falsemodeoptionalstringType of mode.
"text-to-image""text-to-image""image-to-image"negative_promptoptionalstringPrompts to exclude, eg. 'bad anatomy, bad hands'
""output_formatoptionalstringOutput format.
"jpeg""jpeg""png"promptoptionalstringPrompt to render
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/stable-diffusion-3-large-txt2imgSubmit — 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