Krea 2 Trainer
Unganisha
krea-2-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/krea-2-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 $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/krea-2-trainer?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + 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/krea-2-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/sw/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": "krea-2-trainer",
"input": {
"images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}
}
Piga modeli ya Krea 2 Trainer (krea-2-trainer) kupitia Doitong API. Nyaraka za mawakala: https://doitong.com/sw/developers/models/krea-2-trainer.md
POST https://api.doitong.ru/v1/run/krea-2-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"
}
Ikiwa hali ya jibu ni "queued" au "running", piga GET https://api.doitong.ru/v1/requests/REQUEST_ID?wait=20 mara kwa mara hadi iwe "succeeded"; faili ziko kwenye outputs[].url. POST https://api.doitong.ru/v1/quote/krea-2-trainer yenye mwili uleule hurejesha bei kamili kabla ya kuendesha.
Faili kama mfano: $ 0.30 salio za API 300 · ≈ ₽ 31.64 faili zako hupimwa kabla ya kuendesha Iendeshe kwenye ukurasa huu
Ufunguo wako mpya wa API. Inaonyeshwa sasa tu na tayari iko kwenye msimbo na katika sehemu ya ufunguo wa API hapa chini. Iweke siri: usiwahi kuiweka kwenye msimbo unaoendeshwa kwenye kivinjari.
Tokeo lako la kwanza linaonekana hapa
Unda ombi hapa chini na uliendeshe kwa ufunguo wako wa API. Matokeo yanakiliwa kwenye hifadhi yetu, yakihifadhiwa milele au 7 siku bila malipo.
Unda ombi
Vidhibiti vinazalishwa kutoka kwenye schema ya modeli. Msimbo na bei husasishwa unapovibadilisha.
/run/krea-2-trainer?wait=20
makadirio → $ 0.30
# 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/krea-2-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 $DOITONG_API_KEY"
const API = "https://api.doitong.ru/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/krea-2-trainer?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + 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/krea-2-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": "krea-2-trainer",
"input": {
"images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
},
"wait_seconds": 20
}
}
}
Endesha ombi hili
Key yako inabaki kwenye kivinjari hiki: inatumwa kwa Doitong API pekee na kuhifadhiwa (localStorage) tu ukiweka tiki kwenye Kumbuka. Pata ufunguo wa API
Kuhusu Krea 2 Trainer
Huboresha LoRA kwa .zip ya picha katika `images_data_url`, ikiwa na maelezo ya .txt yenye jina sawa kwa hiari na `trigger_phrase`. Modi za `auto_captioning` ni Off, Object/Character, Style na Custom. `steps` ni 50 hadi 10000 (chaguo-msingi 100), `resolution` ni 768 au 1024. Hutoa uzito wa LoRA na faili ya usanidi.
Vigezo
images_data_urlinahitajika
trigger_phrasesi lazima
auto_captioningsi lazima
stepssi lazima
learning_ratesi lazima
resolutionsi lazima
debug_datasetsi lazima
Tokeo
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "krea-2-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": 300,
"usd": "0.30",
"charged": 300,
"refunded": 0
},
"input": {
"images_data_url": "https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
},
"output": {
"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
},
"prompt_enhancement_system_prompt": "string",
"debug_dataset": {
"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"
}
}
Sehemu za tokeo
- lora_file
- object {url, content_type, file_name, file_size}URL ya kupakua uzani wa LoRA wa Krea-2 uliofunzwa katika umbizo la safetensors.
- config_file
- object {url, content_type, file_name, file_size}Kiungo cha faili ya JSON iliyo na usanidi wa mafunzo/LoRA.
- prompt_enhancement_system_prompt
- string | nullKidokezo cha mfumo kinachorejeshwa pamoja na `auto_captioning` ikiwa imewezeshwa, ili kusaidia kuandika au kuboresha vidokezo vya uelekezaji kwa kutumia kanuni ileile ya maelezo kama data ya mafunzo iliyo na maelezo kiotomatiki.
- debug_dataset
- object {url, content_type, file_name, file_size} | nullUpakuaji wa data ya mafunzo unaotolewa wakati `debug_dataset` imewezeshwa: kumbukumbu iliyo na mkusanyiko wa data uliotayarishwa na maelezo yake yaliyotengenezwa kiotomatiki.
Tofauti nyingine za Krea 2
Miundo zaidi: Mafunzo
Kwa mawakala wa AI: ukurasa huu katika Markdown unapatikana kwenye https://doitong.com/sw/developers/models/krea-2-trainer.md. Modeli na miongozo yote: https://doitong.com/developers/llms.txt