MiniMax H3 Reference to Video LoRA Trainer
Integra
minimax/h3/ref2va/trainer
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/minimax/h3/ref2va/trainer?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"training_data_url": "https://example.com/your-file.zip"
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/minimax/h3/ref2va/trainer?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"training_data_url": "https://example.com/your-file.zip"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"training_data_url": "https://example.com/your-file.zip",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/minimax/h3/ref2va/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/mt/developers/docs/mcp)
claude mcp add --transport http doitong-models https://api.doitong.com/v1/mcp \
--header "Authorization: Bearer $DOITONG_API_KEY"
# 2. Your agent calls the "run" tool with:
{
"model_id": "minimax/h3/ref2va/trainer",
"input": {
"training_data_url": "https://example.com/your-file.zip"
}
}
Ċempel il-mudell MiniMax H3 Reference to Video LoRA Trainer (minimax/h3/ref2va/trainer) permezz tad-Doitong API. Dokumentazzjoni għall-aġenti: https://doitong.com/mt/developers/models/minimax/h3/ref2va/trainer.md
POST https://api.doitong.com/v1/run/minimax/h3/ref2va/trainer?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"training_data_url": "https://example.com/your-file.zip"
}
Jekk l-istatus tar-rispons huwa "queued" jew "running", agħmel polling fuq GET https://api.doitong.com/v1/requests/REQUEST_ID?wait=20 sakemm ikun "succeeded"; il-fajls jinsabu f’outputs[].url. POST https://api.doitong.com/v1/quote/minimax/h3/ref2va/trainer bl-istess body jirritorna l-prezz eżatt qabel it-tħaddim.
Din it-talba: $30.00 30,000 krediti tal-API Ħaddmu f’din il-paġna
Iċ-ċavetta API l-ġdida tiegħek. Jintwera biss issa u diġà jinsab fil-kodiċi u fil-qasam taċ-ċavetta API hawn taħt. Żommu sigriet: qatt tpoġġih f’kodiċi li jaħdem f’browser.
Magħmul fuq Doitong
Xogħlijiet pubbliċi magħmula b’MiniMax H3 mill-komunità ta’ Doitong. Uża prompt mill-ġdid bi klikk waħda.
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Vidjo
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Ibni talba
Il-kontrolli jiġu ġġenerati mill-iskema tal-mudell. Il-kodiċi u l-prezz jaġġornaw hekk kif tibdilhom.
/run/minimax/h3/ref2va/trainer?wait=20
kwotazzjoni → $30.00
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/minimax/h3/ref2va/trainer?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"training_data_url": "https://example.com/your-file.zip"
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/minimax/h3/ref2va/trainer?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"training_data_url": "https://example.com/your-file.zip"
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"training_data_url": "https://example.com/your-file.zip",
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/minimax/h3/ref2va/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": "minimax/h3/ref2va/trainer",
"input": {
"training_data_url": "https://example.com/your-file.zip"
},
"wait_seconds": 20
}
}
}
Ħaddem din it-talba
Iċ-ċavetta tiegħek tibqa’ f’dan il-browser: tintbagħat biss lid-Doitong API u tiġi ssejvjata (localStorage) biss jekk timmarka Ftakar. Ikseb ċavetta API
Dwar MiniMax H3 Reference to Video LoRA Trainer
Jitħarreġ LoRA minn zip ta’ klipps tal-vidjo tas-suġġett sabiex l-inputs ta’ referenza jkunu jistgħu jiggwidaw il-ġenerazzjoni tal-vidjo. `reference_conditioning_p` jistabbilixxi kemm-il darba jintużaw ir-referenzi waqt it-taħriġ, u `resume_from_lora_url` ikompli minn LoRA mħarreġ qabel. Jistgħu jiġu kkonfigurati wkoll: `rank`, `number_of_steps`, `number_of_frames`, `frame_rate` u `aspect_ratio`. Jirritorna l-piżijiet u fajl ta’ konfigurazzjoni.
Parametri
training_data_urlmeħtieġ
rankmhux obbligatorju
number_of_stepsmhux obbligatorju
learning_ratemhux obbligatorju
number_of_framesmhux obbligatorju
frame_ratemhux obbligatorju
resolutionmhux obbligatorju
aspect_ratiomhux obbligatorju
trigger_phrasemhux obbligatorju
reference_conditioning_pmhux obbligatorju
resume_from_lora_urlmhux obbligatorju
auto_scale_inputmhux obbligatorju
split_input_into_scenesmhux obbligatorju
split_input_duration_thresholdmhux obbligatorju
debug_datasetmhux obbligatorju
strict_datasetmhux obbligatorju
Output
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "minimax/h3/ref2va/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": 30000,
"usd": "30.00",
"charged": 30000,
"refunded": 0
},
"input": {
"training_data_url": "https://example.com/your-file.zip"
},
"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
},
"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.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
"cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
}
}
Oqsma tal-output
- lora_file
- object {url, content_type, file_name, file_size}URL għal piżijiet LoRA mħarrġa f'format .safetensors.
- config_file
- object {url, content_type, file_name, file_size}Konfigurazzjoni għas-setup tal-endpoint tal-inferenza.
- debug_dataset
- object {url, content_type, file_name, file_size} | nullDisponibbli biss għal talbiet b’`debug_dataset` attivat, dan l-arkivju li jista’ jitniżżel fih id-dejta tat-taħriġ wara l-preprocessing.
Varjanti oħra ta’ MiniMax H3
Aktar mudelli: Taħriġ
Għall-aġenti tal-AI: din il-paġna bħala Markdown tinsab fuq https://doitong.com/mt/developers/models/minimax/h3/ref2va/trainer.md. Il-mudelli u l-gwidi kollha: https://doitong.com/developers/llms.txt