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LTX 2.3 Text to Video

wavespeed-ai /

LTX-2.3 is a DiT-based audio-video foundation model designed to generate synchronized video and audio within a single model, with improved audio and visual quality as well as enhanced prompt adherence. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

text-to-video
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$0.1cho mỗi lần chạy·~10 / $1

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A wide aerial shot slowly drifts over a dense rainforest at golden hour. Thick mist rises from the canopy as shafts of warm orange light pierce through the trees. A winding river reflects the fading sky below. The camera tilts down gradually, revealing a waterfall cascading into a dark green pool. Ambient sounds of water and distant birds fill the scene.

A street-level tracking shot moves through a rain-soaked Tokyo alley at night. Neon signs in red and blue reflect off the wet pavement. People walk past with umbrellas, faces partially lit by shop windows. Steam rises from a ramen stall on the left. The camera drifts forward at a slow, steady pace, passing lanterns swaying gently in the wind.

A medium shot of a young woman in a flowing white dress standing on a cliff overlooking the ocean. Strong wind blows her hair and dress sideways. She slowly raises both arms outward, closes her eyes, and tilts her face upward toward the overcast sky. The camera slowly circles her from left to right. Waves crash loudly against the rocks far below.

A cinematic wide shot of a massive spacecraft descending through thick storm clouds over a futuristic city at night. Lightning flashes illuminate the underbelly of the ship as it breaks through the clouds. City lights stretch across the horizon below. The camera holds still as the ship passes directly overhead, its engines roaring, displacement wind bending the clouds outward.

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README

LTX-2.3 Text-to-Video

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 generates synchronized video and audio from text prompts in a single pass, bringing together the core building blocks of modern video generation with open weights and practical execution.

Why Choose This?

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

  • Synchronized audio-video Generates video with matching audio in a single model pass, no separate audio production needed.

  • DiT-based architecture Built on Diffusion Transformer technology for high-fidelity, 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

ParameterRequiredDescription
promptYesText description of the video scene, motion, and audio
resolutionNoOutput resolution: 480p, 720p (default), or 1080p
durationNoVideo length in seconds (5-20)
seedNoRandom seed for reproducibility (-1 for random)

Resolution Options

ResolutionBest For
480pFast previews, iteration, lowest cost
720pBalanced quality and cost (default)
1080pFinal delivery, maximum detail

How to Use

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

Pricing

Resolution5s10s15s20s
480p$0.10$0.20$0.30$0.40
720p$0.15$0.30$0.45$0.60
1080p$0.20$0.40$0.60$0.80

Best Use Cases

  • Content Creation — Generate video content with synchronized audio from text descriptions.
  • Social Media — Create engaging video posts with cohesive sound.
  • Marketing — Produce video ads with matching audio from creative briefs.
  • Storytelling — Bring written narratives to life with video and audio.
  • Prototyping — Quickly visualize concepts with audio-visual output.

Pro Tips

  • Audio is automatic — sound is generated based on visual content and prompt context.
  • Describe specific audio when needed (e.g., "rain", "jazz", "crowd noise").
  • Keep prompts clear and specific for better prompt adherence.
  • Iterate at 480p to dial in content, 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.
  • For longer content, generate multiple clips and edit together.
  • Model is not intended to provide factual information.

Related Models

Lưu ý:Trang web này sử dụng các mô hình AI do bên thứ ba cung cấp.

Ltx 2.3 Text To Video API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/ltx-2.3/text-to-video 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 2.3 Text To Video below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "resolution": "720p",
    "aspect_ratio": "16:9",
    "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/text-to-video" \
  -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
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/wavespeed-ai/ltx-2.3/text-to-video";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "prompt": "A cinematic shot of a city at sunset, soft golden light",
        "resolution": "720p",
        "aspect_ratio": "16:9",
        "duration": 5,
        "seed": -1
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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));
}
Python example
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 = {
    "prompt": "A cinematic shot of a city at sunset, soft golden light",
    "resolution": "720p",
    "aspect_ratio": "16:9",
    "duration": 5,
    "seed": -1
}

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-2.3/text-to-video", 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 2.3 Text To Video API — Frequently asked questions

What is the Ltx 2.3 Text To Video API?

Ltx 2.3 Text To Video is a WaveSpeedAI model for video generation, exposed as a REST API on WaveSpeedAI. LTX-2.3 is a DiT-based audio-video foundation model designed to generate synchronized video and audio within a single model, with improved audio and visual quality as well as enhanced prompt adherence. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Ltx 2.3 Text To Video API?

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-2.3-text-to-video.

How much does Ltx 2.3 Text To Video cost per run?

Ltx 2.3 Text To Video starts at $0.10 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.

What inputs does Ltx 2.3 Text To Video accept?

Key inputs: `prompt`, `aspect_ratio`, `resolution`, `duration`, `seed`. 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-2.3-text-to-video.

How long does Ltx 2.3 Text To Video take to generate?

Median end-to-end generation time on WaveSpeedAI is around 19 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Ltx 2.3 Text To Video outputs commercially?

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.

LTX 2.3 Text to Video | Powerful Text-to-Video API | WaveSpeedAI