LTX-2.3
LTX-2.3 is a multimodal video generation model that produces synchronized video and audio from text or images. It supports text-to-video and image-to-video workflows with native dialogue and ambient sound generation, emphasizing temporal stability, strong motion coherence, and production-ready output quality for professional creative pipelines.
API Options
Platform-level options for task execution and delivery.
-
taskType
string required value: videoInference -
Identifier for the type of task being performed
-
taskUUID
string required UUID v4 -
UUID v4 identifier for tracking tasks and matching async responses. Must be unique per task.
-
outputType
string default: URL -
Video output type.
Allowed values 1 value
-
outputFormat
string default: MP4 -
Specifies the file format of the generated output. The available values depend on the task type and the specific model's capabilities.
- `MP4`: Widely supported video container (H.264), recommended for general use.
- `WEBM`: Optimized for web delivery.
- `MOV`: QuickTime format, common in professional workflows (Apple ecosystem).
Allowed values 3 values
-
outputQuality
integer min: 20 max: 99 default: 95 -
Compression quality of the output. Higher values preserve quality but increase file size.
-
webhookURL
string URI -
Specifies a webhook URL where JSON responses will be sent via HTTP POST when generation tasks complete. For batch requests with multiple results, each completed item triggers a separate webhook call as it becomes available.
Learn more 1 resource
- Webhooks PLATFORM
- Webhooks
-
deliveryMethod
string default: async -
Determines how the API delivers task results.
Allowed values 1 value
- Returns an immediate acknowledgment with the task UUID. Poll for results using getResponse. Required for long-running tasks like video generation.
Learn more 1 resource
- Task Polling PLATFORM
-
uploadEndpoint
string URI -
Specifies a URL where the generated content will be automatically uploaded using the HTTP PUT method. The raw binary data of the media file is sent directly as the request body. For secure uploads to cloud storage, use presigned URLs that include temporary authentication credentials.
Common use cases:
- Cloud storage: Upload directly to S3 buckets, Google Cloud Storage, or Azure Blob Storage using presigned URLs.
- CDN integration: Upload to content delivery networks for immediate distribution.
// S3 presigned URL for secure upload https://your-bucket.s3.amazonaws.com/generated/content.mp4?X-Amz-Signature=abc123&X-Amz-Expires=3600 // Google Cloud Storage presigned URL https://storage.googleapis.com/your-bucket/content.jpg?X-Goog-Signature=xyz789 // Custom storage endpoint https://storage.example.com/uploads/generated-image.jpgThe content data will be sent as the request body to the specified URL when generation is complete.
-
safety
object -
Content safety checking configuration for video generation.
Properties 2 properties
-
safety»checkContentcheckContent
boolean default: false -
Enable or disable content safety checking. When enabled, defaults to
fastmode.
-
safety»modemode
string default: none -
Safety checking mode for video generation.
Allowed values 3 values
- Disables checking.
- Checks key frames.
- Checks all frames.
-
-
ttl
integer min: 60 -
Time-to-live (TTL) in seconds for generated content. Only applies when
outputTypeisURL.
-
includeCost
boolean default: false -
Include task cost in the response.
-
numberResults
integer min: 1 max: 4 default: 1 -
Number of results to generate. Each result uses a different seed, producing variations of the same parameters.
Inputs
Input resources for the task (images, audio, etc). These must be nested inside the inputs object.
inputs object.-
inputs»frameImagesframeImages
array of strings or objects items: 1 -
An array of frame-specific image inputs to guide video generation. Each item can be either a plain image input (UUID, URL, Data URI, or Base64) or an object that pairs an image with a target frame position.
The
frameImagesparameter allows you to constrain specific frames within the video sequence, ensuring that particular visual content appears at designated points. This is different fromreferenceImages, which provide overall visual guidance without constraining specific timeline positions.When the
frameparameter is omitted, automatic distribution rules apply:- 1 image: Used as the first frame.
Examples 2 examples
Shorthand format: When you don't need to specify a frame position, you can pass a plain image input directly.
"frameImages": [ "aac49721-1964-481a-ae78-8a4e29b91402" ]Object format: When you need to specify a frame position, use an object with
imageandframe."frameImages": [ { "image": "aac49721-1964-481a-ae78-8a4e29b91402", "frame": "first" } ]Format 1: string[]
-
Image input (UUID, URL, Data URI, or Base64).
Format 2: object[] 2 properties
Generation Parameters
Core parameters for controlling the generated content.
-
model
string required value: lightricks:ltx@2.3 -
Identifier of the model to use for generation.
Learn more 3 resources
-
positivePrompt
string required min: 2 max: 10000 -
Text prompt describing elements to include in the generated output.
Learn more 2 resources
-
Width of the generated media in pixels.
Learn more 2 resources
-
Height of the generated media in pixels.
Learn more 2 resources
-
duration
float default: 6 -
Length of the generated video in seconds. The total number of frames produced is determined by duration multiplied by the model's frame rate (fps).
Allowed values 3 values
-
fps
integer default: 25 -
Frames per second for video generation. Higher values create smoother motion but require more processing time.
Allowed values 4 values
Settings
Technical parameters to fine-tune the inference process. These must be nested inside the settings object.
settings object.-
settings»audioaudio
boolean default: true -
Generate synchronized audio.
Stormy Lighthouse Arrival
{
"taskType": "videoInference",
"taskUUID": "9a58607f-5405-42c7-8376-c4ca84efd237",
"model": "lightricks:ltx@2.3",
"positivePrompt": "A cinematic wide shot of a lonely lighthouse on jagged black cliffs during a violent nighttime storm. Sheets of rain slash across the frame, waves crash explosively against the rocks, and the lighthouse beam sweeps rhythmically through dense sea mist. A small rescue boat battles the surf and approaches the cove, its lantern flickering in the wind. The camera slowly pushes forward with realistic motion, dramatic lighting, detailed water simulation, atmospheric fog, and natural temporal consistency. Generate synchronized audio with rolling thunder, crashing waves, heavy rain, wind gusts, distant foghorn, creaking boat wood, and shouted dialogue from the boat captain: 'Hold steady! We're almost there!'",
"width": 1920,
"height": 1080,
"duration": 8,
"fps": 25,
"settings": {
"audio": true
}
}{
"taskType": "videoInference",
"taskUUID": "9a58607f-5405-42c7-8376-c4ca84efd237",
"videoUUID": "2c3f798a-fb54-4858-8821-ca4e0acc1f39",
"videoURL": "https://vm.runware.ai/video/os/a11d13/ws/5/vi/2c3f798a-fb54-4858-8821-ca4e0acc1f39.mp4",
"cost": 0.48
}Noir Rooftop Monologue
{
"taskType": "videoInference",
"taskUUID": "bc3f21a7-1c21-4dc4-8afc-64811b6584da",
"model": "lightricks:ltx@2.3",
"positivePrompt": "A cinematic neo-noir rooftop scene at night in a rain-soaked futuristic city, vertical composition. A detective in a dark trench coat stands near the ledge under a flickering red neon sign, wet hair, subtle breath in the cold air, distant flying traffic and glowing billboards behind him. Slow push-in camera movement with gentle handheld realism, natural body motion, blinking, coat fluttering in the wind, raindrops streaking across the frame, reflections on concrete. He looks into camera and says in a low, weary voice: \"Every light in this city hides a lie, and tonight they are all speaking at once.\" Layer rich synchronized audio with his clear dialogue, soft rain, distant sirens, humming neon, wind gusts, and faraway traffic. Moody high-contrast lighting, realistic faces, filmic color grading, excellent temporal stability, coherent motion, premium cinematic quality.",
"width": 1080,
"height": 1920,
"duration": 8,
"fps": 25,
"settings": {
"audio": true
}
}{
"taskType": "videoInference",
"taskUUID": "bc3f21a7-1c21-4dc4-8afc-64811b6584da",
"videoUUID": "f6bace9f-2d1e-432b-afd1-89f4153cf5d0",
"videoURL": "https://vm.runware.ai/video/os/a20d05/ws/5/vi/f6bace9f-2d1e-432b-afd1-89f4153cf5d0.mp4",
"cost": 0.48
}Cinematic Spaceport Arrival
{
"taskType": "videoInference",
"taskUUID": "57482c02-0ec2-4222-9a65-74488db67251",
"model": "lightricks:ltx@2.3",
"positivePrompt": "A cinematic futuristic spaceport at blue hour, ultra-detailed, wide establishing shot transitioning into a smooth forward camera glide through a crowded arrival platform. Steam rises from vents, holographic signage flickers in multiple languages, docking lights sweep across polished metal surfaces, and travelers in varied costumes move with natural timing. A silver shuttle descends overhead with believable scale and motion, landing gear deploying as dust and mist swirl outward. In the foreground, a calm female pilot turns toward camera and says, 'Welcome to Orion Gate. Final boarding for the Europa run begins now.' Her speech is naturally synchronized with mouth movement. Layer rich ambient audio: distant engine rumble, intercom chatter, footsteps, rolling cargo carts, hydraulic hiss, soft electronic beeps, and the shuttle touchdown thud. Emphasize temporal stability, realistic crowd motion, coherent reflections, cinematic lighting, high production value, and immersive sound design.",
"width": 1920,
"height": 1080,
"duration": 8,
"fps": 25,
"settings": {
"audio": true
}
}{
"taskType": "videoInference",
"taskUUID": "57482c02-0ec2-4222-9a65-74488db67251",
"videoUUID": "0977780f-870b-4c1c-8058-88e5acd141c8",
"videoURL": "https://vm.runware.ai/video/os/a11d13/ws/5/vi/0977780f-870b-4c1c-8058-88e5acd141c8.mp4",
"cost": 0.48
}Neon Alley Dialogue Scene
{
"taskType": "videoInference",
"taskUUID": "e76260ca-cde2-4841-a099-a3bfc7c4114a",
"model": "lightricks:ltx@2.3",
"positivePrompt": "A cinematic nighttime scene in a rain-soaked neon alley in a futuristic Asian megacity. The camera begins with a slow dolly-in toward two characters standing beneath glowing shop signs and drifting steam. A tired courier in a reflective jacket turns to a detective holding a flickering holographic tablet. The courier says, \"I got the package here. You were followed.\" The detective replies quietly, \"I know. Keep walking and don't look back.\" Their speech is natural and synchronized with lip movement. Surround them with detailed ambient sound: distant hover traffic, soft rainfall hitting puddles, buzzing neon, muffled club bass from behind a metal door, footsteps splashing on wet pavement, and a brief rumble of thunder. The lighting is moody and high-contrast with vivid magenta, cyan, and amber reflections. Motion is realistic and coherent, with subtle head turns, cloth movement, drifting mist, and believable camera parallax. Production-ready cinematic quality.",
"width": 1920,
"height": 1080,
"duration": 8,
"fps": 24,
"settings": {
"audio": true
}
}{
"taskType": "videoInference",
"taskUUID": "e76260ca-cde2-4841-a099-a3bfc7c4114a",
"videoUUID": "4645fd1d-6341-48d4-a9c1-e54fafd4f2d7",
"videoURL": "https://vm.runware.ai/video/os/a19d05/ws/5/vi/4645fd1d-6341-48d4-a9c1-e54fafd4f2d7.mp4",
"cost": 0.48
}