Ltx 2 19b Image To Video LoRA API Documentation

Ltx 2 19b Image To Video LoRA API Documentation

Playground

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LTX-2 19b Image-to-Video LoRA is the first DiT-based audio-video foundation model with synchronized audio and video generation. This LoRA version supports custom style adapters for personalized video generation. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

LTX-2 Image-to-Video LoRA generates customized videos from images with synchronized audio and full LoRA support. Apply up to 3 custom LoRA adapters for style personalization — perfect for branded content, custom characters, and unique visual styles.

Looking for the standard version? Try LTX-2 19B Image-to-Video without LoRA support.


Why Choose This?

  • LoRA support Apply up to 3 custom LoRA adapters to personalize style, characters, or visual aesthetics.

  • Image-conditioned video with audio Transforms a static image into a moving video with synchronized audio generated in a single pass.

  • Preserves input composition Maintains the subject, framing, and lighting of your reference image while adding natural motion.

  • High-fidelity output Leverages a 19B-parameter DiT architecture for detailed, temporally consistent video.

  • Flexible resolution Supports 480p, 720p, and 1080p outputs to balance quality and cost.


Parameters

ParameterRequiredDescription
imageYesSource image to animate (upload or public URL)
promptYesText description of motion, action, and audio cues
resolutionNoOutput resolution: 480p, 720p (default), or 1080p
durationNoVideo length in seconds (5-20, default: 5)
lorasNoList of LoRA adapters to apply (up to 3)
seedNoRandom seed for reproducibility (-1 for random)

LoRA Format

Each LoRA in the loras array has:

  • path (required) — URL to the LoRA weights file
  • scale (optional) — Weight multiplier from 0-4, default 1

How to Use

  1. Upload your image — drag and drop or paste a public URL.
  2. Write your prompt — describe motion, style, and audio cues.
  3. Add LoRAs — click ”+ Add Item” to include custom LoRA adapters.
  4. Adjust LoRA scale — use 0.5-1.0 for subtle effects, 1.0-2.0 for stronger influence.
  5. Set resolution and duration — choose based on quality needs.
  6. Run — submit and download the generated video.

Pricing

Resolution5s10s15s20s
480p$0.075$0.15$0.225$0.30
720p$0.10$0.20$0.30$0.40
1080p$0.15$0.30$0.45$0.60

Billing Rules

  • Base price: $0.10 (720p, 5 seconds)
  • Resolution multiplier: 480p = 0.75×, 720p = 1×, 1080p = 1.5×
  • Duration: Scales linearly (per 5 seconds)
  • Total cost = duration × $0.10 × resolution_multiplier / 5

Best Use Cases

  • Custom Character Animation — Apply character LoRAs for consistent identity across videos.
  • Brand Content — Use trained LoRAs for consistent brand visual identity.
  • Artistic Styles — Apply style LoRAs for anime, painterly, or other aesthetics.
  • Product Visualization — Customize product appearance with trained adapters.

Pro Tips

  • Start with scale 1.0 and adjust up or down based on results.
  • Combine LoRAs carefully — multiple LoRAs can conflict; test combinations.
  • Match LoRA to content — use character LoRAs for character content, style LoRAs for aesthetics.
  • Audio adapts automatically — the model generates appropriate audio even with custom styles.
  • Use high-quality, sharp input images for best results.

Notes

  • Maximum video duration is 20 seconds.
  • Up to 3 LoRAs can be applied simultaneously.
  • LoRA version pricing is 25% higher than standard version.
  • The aspect ratio of output video is influenced by your input image.


Reference

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'
{
  "prompt": "A cinematic ocean wave at sunrise, highly detailed",
  "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
  "resolution": "720p",
  "duration": 5,
  "seed": -1
}
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/image-to-video-lora" \
  -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

ParameterTypeRequiredDefaultRangeDescription
promptstringYes-The positive prompt for the generation.
imagestringYes-The image for the generation.
resolutionstringNo720p480p, 720p, 1080pVideo resolution.
durationintegerNo55 ~ 20The duration of the generated media in seconds.
lorasarray<object>No0 ~ 3 itemsList of LoRAs to apply (max 3).
seedintegerNo-1-The random seed to use for the generation. -1 means a random seed will be used.

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.outputsarrayOutput values, usually URL strings; some models return text strings or structured result objects (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

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
data.modelstringModel ID used for the prediction
data.outputsarray<string | object>Array of generated outputs (empty when status is not completed). Items are usually URL strings, but may be text strings or structured result objects, depending on the model.
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to poll for the prediction result
data.statusstringStatus: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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