Flux 3 Action
Integrar
flux-3-action/so101
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://vit.doitong.com/api/v1/run/flux-3-action/so101?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"scene_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"wrist_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"state": [
-18.94,
134.52,
120.15,
28.3,
-32.17,
27.02
]
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://vit.doitong.com/api/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://vit.doitong.com/api/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/flux-3-action/so101?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"scene_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"wrist_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"state": [
-18.94,
134.52,
120.15,
28.3,
-32.17,
27.02
]
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://vit.doitong.com/api/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"scene_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"wrist_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"state": [
-18.94,
134.52,
120.15,
28.3,
-32.17,
27.02,
],
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/flux-3-action/so101", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
# 1. Connect once (Claude Code; Cursor, VS Code, Claude Desktop: https://doitong.com/rm/developers/docs/mcp)
claude mcp add --transport http doitong-models https://vit.doitong.com/api/v1/mcp \
--header "Authorization: Bearer $DOITONG_API_KEY"
# 2. Your agent calls the "run" tool with:
{
"model_id": "flux-3-action/so101",
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"scene_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"wrist_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"state": [
-18.94,
134.52,
120.15,
28.3,
-32.17,
27.02
]
}
}
Cloma il model Flux 3 Action (flux-3-action/so101) tras la Doitong API. Documentaziun per agents: https://doitong.com/rm/developers/models/flux-3-action/so101.md
POST https://vit.doitong.com/api/v1/run/flux-3-action/so101?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"scene_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"wrist_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"state": [
-18.94,
134.52,
120.15,
28.3,
-32.17,
27.02
]
}
Sche il status da la resposta è "queued" u "running", fa polling GET https://vit.doitong.com/api/v1/requests/REQUEST_ID?wait=20 fin ch'el è "succeeded"; las datotecas èn en outputs[].url. POST https://vit.doitong.com/api/v1/quote/flux-3-action/so101 cun il medem body returna il pretsch exact avant l'execuziun.
Questa dumonda: ≈$0,003 3 credits API Lantschar sin questa pagina
Tia nova clav API. Quest vegn mussada mo ussa ed è gia en il code ed en il champ da la clav API sutvart. Mantegna ella secreta: na la metta mai en code che vegn exequì en in navigatur.
Tes emprim resultat vegn qua
Construis ina dumonda sutvart e l'exequescha cun tia clav API. Outputs vegnan copiads en nossa memorisaziun, conservads per adina u 7 dis gratuitamain.
Construir ina dumonda
Ils controls vegnan generads dal schema dal model. Il code e il pretsch s'actualiseschan durant che ti als midas.
/run/flux-3-action/so101?wait=20
calculaziun → $0,003
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://vit.doitong.com/api/v1/run/flux-3-action/so101?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"scene_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"wrist_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"state": [
-18.94,
134.52,
120.15,
28.3,
-32.17,
27.02
]
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://vit.doitong.com/api/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://vit.doitong.com/api/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/flux-3-action/so101?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"scene_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"wrist_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"state": [
-18.94,
134.52,
120.15,
28.3,
-32.17,
27.02
]
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://vit.doitong.com/api/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"scene_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"wrist_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"state": [
-18.94,
134.52,
120.15,
28.3,
-32.17,
27.02,
],
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/flux-3-action/so101", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "run",
"arguments": {
"model_id": "flux-3-action/so101",
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"scene_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"wrist_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"state": [
-18.94,
134.52,
120.15,
28.3,
-32.17,
27.02
]
},
"wait_seconds": 20
}
}
}
Executar questa dumonda
Tia clav resta en quest browser: ella vegn tramessa mo al Doitong API e memorisada (localStorage) mo sch'ella scheccas Tiar a memoria. Obtegnair ina clav API
Davart Flux 3 Action
Enstagl da medias, quai returna JSON cun las `actions` previsas, lur rata da controlla, las etichettas da colonnas e quants pass inizials d'exequir (`execute_steps`) avant da dumandar in nov bloc. Ils inputs obligatorics èn in `prompt` da text (fin a 2000 caracters), `scene_image_url`, `wrist_image_url` ed in `state` da 6 valurs. Inputs d'istorgia opziunals (`state_history`, `command_history`, imagens d'istorgia) dattan context temporal. `num_inference_steps` va da 1 a 8 e `guidance_scale` da 1 a 10.
Parameters
promptobligatori
scene_image_urlobligatori
wrist_image_urlobligatori
stateobligatori
state_historyopziunal
command_historyopziunal
history_scene_image_urlopziunal
history_wrist_image_urlopziunal
num_inference_stepsopziunal
guidance_scaleopziunal
seedopziunal
Ils parameters da datotecas acceptan ina URL publica https://, ina URI data: fin a 10 MB u il file_url d'ina chargiada. Datotecas
Output
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "flux-3-action/so101",
"status": "succeeded",
"created_at": "2026-10-20T12:00:00Z",
"started_at": "2026-10-20T12:00:02Z",
"completed_at": "2026-10-20T12:00:09Z",
"queue_position": null,
"cost": {
"credits": 3,
"usd": "0.003",
"charged": 3,
"refunded": 0
},
"input": {
"prompt": "A red fox trots through fresh snow at sunrise, soft golden light, shallow depth of field",
"scene_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"wrist_image_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
"state": [
-18.94,
134.52,
120.15,
28.3,
-32.17,
27.02
]
},
"output": {
"actions": [
[
1.0
]
],
"action_labels": [
"string"
],
"action_hz": 1.0,
"execute_steps": 1,
"seed": 42,
"timings": {}
},
"outputs": [
{
"type": "file",
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_size": 1843200,
"stored": true
}
],
"error": null,
"metrics": {
"inference_seconds": 6.8
},
"urls": {
"self": "https://vit.doitong.com/api/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
"cancel": "https://vit.doitong.com/api/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
}
}
Champs da l'output
- actions
- array of array of numberArray d'acziuns previsas cun lingias cronologicas che represchentan pass da control; las valurs n'èn betg limitadas, perquai ston ils limits da giugadiras, sveltezza e spazi da lavur vegnir applitgads al robot. A 30 Hz, SO-101 furnescha 42x6 cumonds absoluts en deg e pinz en %; a 15 Hz, DROID furnescha 32x8 mira da giugadiras absoluta en rad plus la part serrada dal pinz.
- action_labels
- array of stringNums correspundents a las colonnas d'acziun.
- action_hz
- numberFrequenza da control per las lingias d'acziun.
- execute_steps
- integerDumber d'acziuns inizialas d'exequir avant da dumandar in ulteriur bloc.
- seed
- integerSeed casual utilisà durant il sampling.
- timings
- objectMesiraziuns dal temp dal server, exprimidas en secundas.
Dapli models: Video a video
Per agents AI: questa pagina sco Markdown è disponibla sut https://doitong.com/rm/developers/models/flux-3-action/so101.md. Tut ils models e las guidas: https://doitong.com/developers/llms.txt