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Ltx 2 19b Ic LoRA Trainer

Ltx 2 19b Ic LoRA Trainer

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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

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 images. Include 10-20 high-quality, diverse images 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:


Parameters

ParameterDefaultDescription
dataZIP file containing training images (required)
trigger_wordUnique word to activate your trained concept
steps500Total training iterations
learning_rate0.0002Training speed (lower = more stable, higher = faster)
lora_rank32Adapter capacity (higher = more detail, larger file)

Pricing

Training StepsPrice (USD)
100$0.75
500$3.75
1,000$7.50
2,000$15.00

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 images 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.
  • Product Visualization — Train on product photos for consistent video presentations.

Pro Tips

  • Use 10-20 high-quality, diverse images 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.

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Guidance


Notes

  • Higher parameter values (steps, lora_rank) will increase training time.
  • Training time scales with the number of images 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


# Submit the task
curl --location --request POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/ltx-2-19b/ic-lora-trainer" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{
    "trigger_word": "p3r5on",
    "steps": 500,
    "learning_rate": 0.0002,
    "lora_rank": 32
}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai/api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
datastringYes--Upload a zip file containing paired reference and target videos for IC-LoRA training. Reference videos must be named with '_ref.mp4' suffix (e.g., 'video1_ref.mp4' pairs with 'video1.mp4'). Reference and target videos must have identical resolution and length. Each text file should have the same name as the video file it corresponds to for captions.
trigger_wordstringNop3r5on-The phrase that will trigger the model to generate a video.
stepsintegerNo500100 ~ 20000Number of steps to train the LoRA on.
learning_ratenumberNo0.00020.00000 ~ 1.00000
lora_rankintegerNo321 ~ 128

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayArray of URLs to the generated content (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.has_nsfw_contentsarrayArray of boolean values indicating NSFW detection for each output
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Request Parameters

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction, the ID of the prediction to get
data.modelstringModel ID used for the prediction
data.outputsstringArray of URLs to the generated content (empty when status is not completed).
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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