Flux 3 Action
इंटीग्रेट करें
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/hi/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
]
}
}
Flux 3 Action मॉडल (flux-3-action/so101) को Doitong API के ज़रिए कॉल करें। एजेंट के लिए दस्तावेज़: https://doitong.com/hi/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
]
}
यदि रिस्पॉन्स स्टेटस "queued" या "running" है, तो GET https://vit.doitong.com/api/v1/requests/REQUEST_ID?wait=20 को तब तक पोल करें जब तक यह "succeeded" न हो जाए; फ़ाइलें outputs[].url में हैं। उसी बॉडी के साथ POST https://vit.doitong.com/api/v1/quote/flux-3-action/so101 चलाने से रन से पहले सटीक कीमत मिलती है।
यह अनुरोध: ≈$0.003 3 API क्रेडिट इसे इस पेज पर चलाएँ
आपकी नई API कुंजी। यह केवल अभी दिखाया गया है और कोड तथा नीचे दिए गए API कुंजी फ़ील्ड में पहले से मौजूद है। इसे गुप्त रखें: इसे कभी भी ब्राउज़र में चलने वाले कोड में न डालें।
आपका पहला परिणाम यहाँ आएगा
नीचे एक अनुरोध बनाएँ और उसे अपनी API कुंजी से चलाएँ। आउटपुट हमारे स्टोरेज में कॉपी किए जाते हैं और हमेशा के लिए या 7 दिन तक मुफ़्त में रखे जाते हैं।
अनुरोध बनाएँ
कंट्रोल मॉडल स्कीमा से जनरेट होते हैं। इन्हें बदलने पर कोड और कीमत अपडेट होती है।
/run/flux-3-action/so101?wait=20
कोटेशन → $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
}
}
}
यह अनुरोध चलाएँ
आपकी key इसी ब्राउज़र में रहती है: इसे केवल Doitong API को भेजा जाता है और केवल तभी (localStorage) में सेव किया जाता है जब आप Remember चुनते हैं। API कुंजी प्राप्त करें
Flux 3 Action के बारे में
मीडिया के बजाय, यह अनुमानित `actions`, उनकी कंट्रोल दर, कॉलम लेबल और नए चंक का अनुरोध करने से पहले निष्पादित किए जाने वाले शुरुआती स्टेप्स की संख्या (`execute_steps`) वाला JSON लौटाता है। आवश्यक इनपुट टेक्स्ट `prompt` (अधिकतम 2000 वर्ण), `scene_image_url`, `wrist_image_url` और 6 मानों वाली `state` हैं। वैकल्पिक इतिहास इनपुट (`state_history`, `command_history`, इतिहास इमेज) समय-संदर्भ देते हैं। `num_inference_steps` 1 से 8 तक और `guidance_scale` 1 से 10 तक चलता है।
पैरामीटर
promptआवश्यक
scene_image_urlआवश्यक
wrist_image_urlआवश्यक
stateआवश्यक
state_historyवैकल्पिक
command_historyवैकल्पिक
history_scene_image_urlवैकल्पिक
history_wrist_image_urlवैकल्पिक
num_inference_stepsवैकल्पिक
guidance_scaleवैकल्पिक
seedवैकल्पिक
फ़ाइल पैरामीटर में सार्वजनिक https:// URL, 10 MB तक का data: URI, या अपलोड का file_url लिया जाता है। फ़ाइलें
आउटपुट
{
"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"
}
}
आउटपुट के फ़ील्ड
- actions
- array of array of numberकालानुक्रमिक पंक्तियों वाली predicted action array, जो control steps दर्शाती है; मान unclipped होते हैं, इसलिए robot पर joint, velocity और workspace limits लागू करनी होंगी। 30 Hz पर SO-101, deg और gripper % में 42x6 absolute commands देता है; 15 Hz पर DROID, rad में 32x8 absolute joint targets और gripper का closed fraction देता है।
- action_labels
- array of stringaction columns से संबंधित नाम।
- action_hz
- numberaction rows की control frequency।
- execute_steps
- integerदूसरा chunk मांगने से पहले निष्पादित की जाने वाली शुरुआती actions की संख्या।
- seed
- integersampling के दौरान उपयोग की गई random seed।
- timings
- objectसर्वर से प्राप्त timing measurements, सेकंड में व्यक्त।
और मॉडल: वीडियो से वीडियो
AI एजेंट के लिए: इस पेज का Markdown यहाँ है https://doitong.com/hi/developers/models/flux-3-action/so101.md. सभी मॉडल और गाइड: https://doitong.com/developers/llms.txt