SANA 1.5 1.6B text-to-image

This service turns a text prompt into a 1024x1024 PNG. It runs NVIDIA/MIT's SANA 1.5 1.6B diffusion model (diffusers, bf16) on one GPU. Generation settings are fixed: 1024x1024, 20 steps, guidance scale 4.5. You send a prompt and, optionally, a seed.

Base URL: {{BASE_URL}}

Routes

POST /v1/images/generations

OpenAI-style image generation. Returns one image as base64 PNG.

curl -s {{BASE_URL}}/v1/images/generations {{AUTH}} \
  -H "Content-Type: application/json" \
  -d '{"prompt": "a lighthouse at sunset", "seed": 7}' \
  | python3 -c 'import sys,json,base64; d=json.load(sys.stdin); open("out.png","wb").write(base64.b64decode(d["data"][0]["b64_json"])); print(d["seed"], d["params_used"])'

Request fields:

• prompt (string, required): what to draw. English works best.
• seed (integer >= 0, optional): the random seed. The same prompt and seed give the same image. Leave it out for a random one; the response's top-level seed field says which seed was used, so you can reproduce a result you liked.

Also accepted, for stock OpenAI clients: model (any string), n (must be 1), response_format (must be "b64_json"), size (must be "1024x1024"), user. Any other field, or a different value for a fixed one (for example "num_inference_steps": 4), is rejected with 400.

Response:

{"created": 1761530000, "model": "sana-1.5-1.6b", "seed": 7, "elapsed_seconds": 4.1,
 "data": [{"b64_json": "<PNG, base64>"}],
 "params_used": {"size": "1024x1024", "num_inference_steps": 20, "guidance_scale": 4.5}}

The OpenAI Python SDK works too:

from openai import OpenAI
client = OpenAI(base_url="{{BASE_URL}}/v1", api_key="unused")
r = client.images.generate(model="sana-1.5-1.6b", prompt="a lighthouse at sunset",
                           response_format="b64_json", extra_body={"seed": 7})

GET /v1/config

The fixed settings and the request fields this service accepts.

curl -s {{BASE_URL}}/v1/config {{AUTH}}

GET /status

Where the service is in its start-up: phase is one of building, downloading, starting, loading, warming, ready, error, with eta_seconds and, while starting, a wait_hint.

curl -s {{BASE_URL}}/status {{AUTH}}

Timings and concurrency

• First start: builds a container image and downloads about 10 GB of weights. After that, loading and a warm-up take about 70 s (budgeted at 180 s).
• Each image takes about 5 s on a DGX Spark (GB10).
• Requests run one at a time; extra requests wait in line. Send them one by one, or a few in parallel at most, and allow at least 60 s per request in your client timeout.

Errors

• 503 with Retry-After while the service is still starting: wait that many seconds and retry. GET /status shows progress.
• 400: a field was rejected; the body's error says which and why.
• 422: a request field has the wrong type (for example prompt missing or empty, seed negative).
• 5xx otherwise: retry once, then check GET /status.