LTX-2 IC-LoRA Trainer lets you train custom In-Context LoRA models for video-to-video transformations, including depth/pose adapters, video restoration, and style transfer. Upload a ZIP file containing paired videos to start. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Idle

$0.75per run·~13 / $10
LTX-2 19B IC-LoRA Trainer is a high-performance custom model training service for the LTX-2 19B video generation model. It allows you to train lightweight LoRA (Low-Rank Adaptation) adapters for personalized styles, characters, and concepts — bringing your custom visuals into AI-generated videos with synchronized audio.
Video LoRA training Train custom adapters specifically optimized for LTX-2's 19B DiT video architecture.
Character consistency Create LoRAs that maintain character identity across generated video clips.
Style personalization Capture specific artistic styles, brand aesthetics, or visual themes for video content.
Seamless integration Trained LoRAs work directly with LTX-2 Text-to-Video LoRA and Image-to-Video LoRA models.
Data Upload Prepare and upload a ZIP file containing your training videos. Include 3-10 high-quality, diverse video clips for best results.
Configure Trigger Word Set a unique trigger word (e.g., "p3r5on") that will activate your trained style or character in prompts.
Adjust Training Parameters
| Parameter | Default | Description |
|---|---|---|
| data | — | ZIP file containing training videos (required) |
| trigger_word | — | Unique word to activate your trained concept |
| steps | 500 | Total training iterations |
| learning_rate | 0.0002 | Training speed (lower = more stable, higher = faster) |
| lora_rank | 32 | Adapter capacity (higher = more detail, larger file) |
| Training Steps | Price (USD) |
|---|---|
| 100 | $0.75 |
| 500 | $3.75 |
| 1,000 | $7.50 |
| 2,000 | $15.00 |
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/ltx-2-19b/ic-lora-trainer with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Ltx 2 19b Ic Lora Trainer below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"data": "https://github.com/mdn/interactive-examples/archive/refs/heads/main.zip",
"steps": 500,
"learning_rate": 0.0002,
"lora_rank": 32
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/ltx-2-19b/ic-lora-trainer" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
-H "Authorization: Bearer $WAVESPEED_API_KEY")
RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$STATUS" in
completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
created|processing) sleep 2 ;;
*) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
esac
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/ltx-2-19b/ic-lora-trainer";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"data": "https://github.com/mdn/interactive-examples/archive/refs/heads/main.zip",
"steps": 500,
"learning_rate": 0.0002,
"lora_rank": 32
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
`https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
await new Promise(resolve => setTimeout(resolve, 2000));
}import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"data": "https://github.com/mdn/interactive-examples/archive/refs/heads/main.zip",
"steps": 500,
"learning_rate": 0.0002,
"lora_rank": 32
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/wavespeed-ai/ltx-2-19b/ic-lora-trainer", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout"}:
raise RuntimeError(result)
if status not in {"created", "processing"}:
raise RuntimeError(f"Unexpected status: {status}")
time.sleep(2)Ltx 2 19b Ic Lora Trainer is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. LTX-2 IC-LoRA Trainer lets you train custom In-Context LoRA models for video-to-video transformations, including depth/pose adapters, video restoration, and style transfer. Upload a ZIP file containing paired videos to start. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/ltx-2-19b-ic-lora-trainer.
Ltx 2 19b Ic Lora Trainer starts at $0.75 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
Key inputs: `data`, `learning_rate`, `lora_rank`, `steps`, `trigger_word`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/wavespeed-ai/ltx-2-19b-ic-lora-trainer.
Median end-to-end generation time on WaveSpeedAI is around 3812 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.
Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.