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.
Idle
$0.1per run·~10 / $1
ExamplesView all
A slow push-in shot toward the entrance of an ancient stone temple at dawn. Thick morning fog clings to the carved columns and stone steps. Moss covers the lower walls. A single monk in orange robes walks up the steps from the left and disappears through the doorway. The camera continues its slow forward movement as birds begin to call in the distance and soft light warms the stone facade.
A low-angle ground shot of a lone wolf walking through a snow-covered pine forest at dusk. Its breath forms small clouds in the freezing air. Snow falls steadily, settling on its dark fur. The camera tracks slowly alongside at ground level as the wolf pauses, turns its head toward the camera, then continues forward. The forest is silent except for the crunch of snow underfoot.
A top-down overhead shot of a chef's hands carefully plating a dish on a dark ceramic plate. Tweezers place a single herb leaf onto a sauce swirl. Steam rises gently from the main component. The camera slowly zooms in as a final drizzle of golden oil catches the light. The kitchen background is softly blurred, warm ambient light from above.
A wide static shot of a remote cabin in the mountains at night. Warm amber light glows from two small windows. Snow falls heavily around the structure. A thick pine forest surrounds the cabin on three sides. Smoke rises slowly from the chimney and dissolves into the dark sky above. The scene is completely still except for the falling snow and the faint flicker of light inside.
Related Models
README
LTX-2.3 Text-to-Video LoRA
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?
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Improved quality Enhanced audio and visual quality compared to LTX-2, with better prompt adherence and more coherent outputs.
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Synchronized audio-video Generates video with matching audio in a single model pass, no separate audio production needed.
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DiT-based architecture Built on Diffusion Transformer technology for high-fidelity, temporally consistent video generation.
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Flexible resolution Supports 480p, 720p, and 1080p outputs to balance quality and cost.
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Variable duration Generate clips from 5 to 20 seconds.
Parameters
| Parameter | Required | Description |
|---|---|---|
| loras | No | List of LoRA models to apply (max 3, each with path and scale) |
| prompt | Yes | Text description of the video scene, motion, and audio |
| resolution | No | Output resolution: 480p, 720p (default), or 1080p |
| duration | No | Video length in seconds (5-20) |
| seed | No | Random seed for reproducibility (-1 for random) |
Resolution Options
| Resolution | Best For |
|---|---|
| 480p | Fast previews, iteration, lowest cost |
| 720p | Balanced quality and cost (default) |
| 1080p | Final delivery, maximum detail |
How to Use
- Write your prompt — describe the scene, motion, camera movement, and audio cues.
- Select resolution — 480p for iteration, 720p for balance, 1080p for final output.
- Set duration — 5-20 seconds based on your content needs.
- Run — submit and download the video with synchronized audio.
Pricing
| Resolution | 5s | 10s | 15s | 20s |
|---|---|---|---|---|
| 480p | $0.15 | $0.30 | $0.45 | $0.60 |
| 720p | $0.20 | $0.40 | $0.60 | $0.80 |
| 1080p | $0.25 | $0.50 | $0.75 | $1.00 |
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
- LTX-2.3 Image-to-Video — Animate images with synchronized audio.
- LTX-2 19B Text-to-Video — Previous generation model.
Ltx 2.3 Text To Video Lora 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-lora with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Ltx 2.3 Text To Video Lora below.
HTTP example
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/ltx-2.3/text-to-video-lora" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d '{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"resolution": "720p",
"aspect_ratio": "16:9",
"duration": 5,
"seed": -1
}'
# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
-H "Authorization: Bearer $WAVESPEED_API_KEY"
# When status is "completed", read the output from data.outputs[0].Node.js example
// npm install wavespeed
const WaveSpeed = require('wavespeed');
const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env
const result = await client.run("wavespeed-ai/ltx-2.3/text-to-video-lora", {
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"resolution": "720p",
"aspect_ratio": "16:9",
"duration": 5,
"seed": -1
});
console.log(result.outputs[0]); // → URL of the generated outputPython example
# pip install wavespeed
import wavespeed
output = wavespeed.run(
"wavespeed-ai/ltx-2.3/text-to-video-lora",
{
"prompt": "A cinematic shot of a city at sunset, soft golden light",
"resolution": "720p",
"aspect_ratio": "16:9",
"duration": 5,
"seed": -1
}
)
print(output["outputs"][0]) # → URL of the generated outputLtx 2.3 Text To Video Lora API — Frequently asked questions
What is the Ltx 2.3 Text To Video Lora API?
Ltx 2.3 Text To Video Lora is a WaveSpeedAI model for AI inference, exposed as a REST API on WaveSpeedAI. 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. You can call it programmatically or try it from the playground above.
How do I call the Ltx 2.3 Text To Video Lora 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 prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/wavespeed-ai/ltx-2.3-text-to-video-lora.
How much does Ltx 2.3 Text To Video Lora cost per run?
Ltx 2.3 Text To Video Lora 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 Lora accept?
Key inputs: `prompt`, `aspect_ratio`, `resolution`, `duration`, `seed`, `loras`. 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-lora.
How do I get started with the Ltx 2.3 Text To Video Lora API?
Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.
Can I use Ltx 2.3 Text To Video Lora 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.