Text to 3D Model
Transform text descriptions into fully textured 3D models. Just describe what you want and let Flow do the work.
From prompt to GLBin a few API calls
No complex setup. No 3D expertise required. Create a flow, poll until it finishes, then download the model.
Read the Quickstart ->quickstart.py
import base64
import requests
import time
API_KEY = "your-api-key-here"
BASE_URL = "https://flows.generio.ai"
headers = {"Authorization": f"Bearer {API_KEY}"}
payload = {
"template": "model_generate_fromprompt",
"parameters": {"quality": "high"},
"inputs": [{"data": "A modern office chair", "additional": None}],
"additional": None,
}
# 1. Create a flow
response = requests.post(f"{BASE_URL}/flows", headers=headers, json=payload)
flow_id = response.json()["flow_id"]
# 2. Poll until complete
while True:
status = requests.get(
f"{BASE_URL}/flows/{flow_id}", headers=headers
).json()
if status["state"] in ["completed", "failed", "aborted"]:
break
time.sleep(5)
if status["state"] != "completed":
raise SystemExit(status["state"])
# 3. Download your GLB (per-output asset_id)
outputs = requests.get(
f"{BASE_URL}/flows/{flow_id}/outputs", headers=headers
).json()["outputs"]
asset_id = outputs[0]["asset_id"]
r = requests.get(
f"{BASE_URL}/flows/{flow_id}/outputs/{asset_id}?include_data=true",
headers=headers,
)
data_uri = r.json()["data"]
binary = base64.b64decode(data_uri.split(",", 1)[1])
with open(f"model_{asset_id}.glb", "wb") as f:
f.write(binary)Community & Support
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