Ideogram V4.0q LoRA Trainer
التكامل
ideogram/v4/trainer
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.ru/v1/run/ideogram/v4/trainer?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ideogram/v4/trainer?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/ideogram/v4/trainer", 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/ar/developers/docs/mcp)
claude mcp add --transport http doitong-models https://api.doitong.ru/v1/mcp \
--header "Authorization: Bearer $DOITONG_API_KEY"
# 2. Your agent calls the "run" tool with:
{
"model_id": "ideogram/v4/trainer",
"input": {
"images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
}
استدعِ نموذج Ideogram V4.0q LoRA Trainer (ideogram/v4/trainer) عبر Doitong API. توثيق الوكلاء: https://doitong.com/ar/developers/models/ideogram/v4/trainer.md
POST https://api.doitong.ru/v1/run/ideogram/v4/trainer?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
إذا كانت حالة الاستجابة "queued" أو "running"، فأرسل طلبات GET إلى https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20 حتى تصبح "succeeded"؛ توجد الملفات في outputs[].url. يعيد POST https://api.doitong.ru/v1/quote/ideogram/v4/trainer مع نفس النص الأساسي السعر الدقيق قبل التشغيل.
هذا الطلب: 6.75 $ 6,750 رصيد API · ≈ 711.88 ₽ تشغيله في هذه الصفحة
مفتاح API الجديد الخاص بك. يظهر الآن فقط، وهو مُدرج بالفعل في الشيفرة وفي حقل مفتاح API أدناه. احتفظ به سريًا: لا تضعه أبدًا في شيفرة تعمل في المتصفح.
ستظهر نتيجتك الأولى هنا
أنشئ طلبًا أدناه وشغّله باستخدام مفتاح API الخاص بك. تُنسخ المخرجات إلى تخزيننا، وتُحفظ إلى الأبد أو لمدة 7 أيام مجانًا.
إنشاء طلب
تُنشأ عناصر التحكم من مخطط النموذج. يتحدّث الرمز والسعر عند تغييرها.
/run/ideogram/v4/trainer?wait=20
عرض السعر → 6.75 $
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.ru/v1/run/ideogram/v4/trainer?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/ideogram/v4/trainer?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.ru/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/ideogram/v4/trainer", 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": "ideogram/v4/trainer",
"input": {
"images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
},
"wait_seconds": 20
}
}
}
تشغيل هذا الطلب
يبقى مفتاحك في هذا المتصفح: لا يُرسل إلا إلى Doitong API ويُحفظ في localStorage فقط إذا فعّلت خيار التذكّر. الحصول على مفتاح API
حول Ideogram V4.0q LoRA Trainer
يدرّب LoRA على مجموعة بياناتك للتخصيص أو لأسلوب معين. يشير `images_data_url` إلى ملف zip من الصور، وتُقرأ الأوصاف من ملفات .txt ذات الأسماء نفسها، بينما يملأ `default_caption` الأوصاف المفقودة. تتراوح `steps` من 100 إلى 40000 (القيمة الافتراضية 1000)، ويمكن ضبط `learning_rate` و`resolution`. يُرجع ملف أوزان LoRA وملف إعدادات.
المعاملات
images_data_urlمطلوب
stepsاختياري
default_captionاختياري
resolutionاختياري
learning_rateاختياري
المخرجات
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "ideogram/v4/trainer",
"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": 6750,
"usd": "6.75",
"charged": 6750,
"refunded": 0
},
"input": {
"images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
},
"output": {
"diffusers_lora_file": {
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_name": "0-output.jpeg",
"file_size": 1843200
},
"config_file": {
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_name": "0-output.jpeg",
"file_size": 1843200
}
},
"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://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
"cancel": "https://api.doitong.ru/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
}
}
حقول المخرجات
- diffusers_lora_file
- object {url, content_type, file_name, file_size}URL لأوزان LoRA لـ Ideogram V4 الناتجة عن التدريب.
- config_file
- object {url, content_type, file_name, file_size}URL لإعداد التدريب المتاح للعامة.
أنواع Ideogram v4 الأخرى
مزيد من النماذج: التدريب
لوكلاء الذكاء الاصطناعي: تتوفر هذه الصفحة بصيغة Markdown على https://doitong.com/ar/developers/models/ideogram/v4/trainer.md. جميع النماذج والأدلة: https://doitong.com/developers/llms.txt