Ltx 2.3 Image To Video LoRA

Ltx 2.3 Image To Video LoRA

Playground

LTX-2.3 with LoRA support is a DiT-based audio-video foundation model designed to generate synchronized video and audio with custom styles, motion, or likeness training. Improved audio and visual quality with enhanced prompt adherence. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

LTX-2.3 is a significant update to the LTX-2 model, featuring improved audio and visual quality with enhanced prompt adherence. As a DiT-based (Diffusion Transformer) audio-video foundation model, it animates your input image into a high-fidelity video with synchronized audio generated in a single pass.

Why Choose This?

• Improved quality Enhanced audio and visual quality compared to LTX-2, with better prompt adherence and more coherent outputs.

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

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

• DiT-based architecture Built on Diffusion Transformer technology for detailed, temporally consistent video generation.

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

• Variable duration Generate clips from 5 to 20 seconds.

Parameters

Resolution Options

How to Use

1. Upload your image — the reference image that defines subject, composition, and lighting.
2. Write your prompt — describe the motion, camera movement, and audio cues.
3. Select resolution — 480p for iteration, 720p for balance, 1080p for final output.
4. Set duration — 5-20 seconds based on your content needs.
5. Run — submit and download the animated video with synchronized audio.

Pricing

Best Use Cases

• Product Animation — Bring product photos to life with subtle motion and ambient audio.
• Portrait Animation — Animate headshots and portraits with natural movement.
• Social Media — Create engaging animated content from static images.
• Marketing — Transform key visuals into video ads with cohesive sound.
• Storytelling — Animate storyboard frames or concept art.

Pro Tips

• Audio is automatic — sound is generated based on visual motion and prompt context.
• Describe specific audio when needed (e.g., “rain”, “jazz”, “crowd noise”).
• Use high-quality, sharp, well-exposed images for best results.
• Keep motion prompts simple — one clear action per prompt yields better results.
• Iterate at 480p to dial in motion, then render at higher resolution for final output.
• Use fixed seed when comparing prompt variations to isolate changes.

Notes

• Maximum video duration is 20 seconds.
• Width & height must be divisible by 32, frame count must be divisible by 8 + 1.
• The aspect ratio of output video is influenced by your input image.
• For longer content, generate multiple clips and edit together.

Related Models

• LTX-2.3 Text-to-Video — Generate video with audio from text only.
• LTX-2 19B Image-to-Video — Previous generation model.

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-2.3/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

Response Parameters

Result Request Parameters

Result Response Parameters