Ltx 2 19b Ic LoRA Trainer

Ltx 2 19b Ic LoRA Trainer

Playground

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.

Features

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.

Why Choose This?

• 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.

Training Process

1. Data Upload Prepare and upload a ZIP file containing your training videos. Include 3-10 high-quality, diverse video clips for best results.

2. Configure Trigger Word Set a unique trigger word (e.g., “p3r5on”) that will activate your trained style or character in prompts.

3. Adjust Training Parameters

• steps — Total training iterations (default: 500)
• learning_rate — Training speed (default: 0.0002)
• lora_rank — Adapter capacity (default: 32)

4. LoRA Training The system runs a tailored LoRA optimization loop:

• Freezes the base model weights
• Trains only the low-rank adapter layers
• Applies LTX-2 optimized settings for video generation

5. Model Export After training completes, you receive a LoRA adapter file (.safetensors) compatible with:

• LTX-2 19B Text-to-Video LoRA
• LTX-2 19B Image-to-Video LoRA

Parameters

Pricing

Billing Rules

• Base price: $0.75 per 100 steps
• Total cost = $0.75 × (steps / 100)
• Billed proportionally to the total number of steps in your job

Best Use Cases

• Character LoRAs — Train on character videos to maintain identity across video generations.
• Brand Styles — Create custom visual styles for consistent marketing video content.
• Art Styles — Capture specific artistic aesthetics for creative video projects.
• Motion Patterns — Train on specific movement styles or animation techniques.

Pro Tips

• Use 3-10 high-quality, diverse video clips of your subject for best results.
• Choose a unique trigger word that won’t conflict with common words.
• Higher lora_rank (32-64) captures more detail but increases training time and file size.
• Lower learning_rate is more stable but requires more steps.
• Start with default settings, then adjust if needed.

Try More Trainers

• Qwen Image 2512 LoRA Trainer — Train LoRAs for Qwen Image text-to-image model.
• Z-Image LoRA Trainer — Train LoRAs for Z-Image models with Turbo-compatible optimization.
• Flux Dev LoRA Trainer — Train LoRAs for Flux Dev image generation model.

Guidance

• How to Train your LoRA?
• How to Use your LoRA?

Notes

• Higher parameter values (steps, lora_rank) will increase training time.
• Training time scales with the number of video clips and total steps configured.
• For faster iterations, start with lower settings and increase gradually.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result

set -euo pipefail

export WAVESPEED_API_KEY="your-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 "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "${REQUEST_BODY}")

TASK=$(printf '%s' "${SUBMIT_RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "${TASK}" | jq -r '.id // empty')
if [ -z "${PREDICTION_ID}" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(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' "${RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
  STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')

  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
done

Parameters

Task Submission Parameters

Request Parameters

Response Parameters

Result Request Parameters

Result Response Parameters