LTX-2.3 Fast
LTX-2.3 Fast is a performance-optimized variant of LTX 2.3 designed for rapid video generation with synchronized audio. It supports text-to-video, image-to-video, and audio-conditioned workflows while prioritizing speed, responsiveness, and cost efficiency for draft, preview, and high-velocity creative production use cases.
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-fast -
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).
-
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.
Neon Rain Alley Chase
{
"taskType": "videoInference",
"taskUUID": "44dfa40f-3edc-44d7-9c16-586920b953c4",
"model": "lightricks:ltx@2.3-fast",
"positivePrompt": "A cinematic cyberpunk alley at night during heavy rain, glossy neon reflections on wet pavement, a masked courier sprinting through drifting steam while hovering police drones sweep searchlights overhead, dynamic handheld camera movement, parallax depth, flickering holographic shop signs in magenta and cyan, puddle splashes, distant traffic glow, tense atmospheric action, realistic motion, richly detailed environment, synchronized urban chase audio with rain, footsteps, drone hum, echoing sirens, and pulsing synth tension",
"width": 1920,
"height": 1080,
"duration": 6,
"fps": 25,
"settings": {
"audio": true
}
}{
"taskType": "videoInference",
"taskUUID": "44dfa40f-3edc-44d7-9c16-586920b953c4",
"videoUUID": "c85254e5-89e3-45c0-90e8-ab37bcde5161",
"videoURL": "https://vm.runware.ai/video/os/a01d21/ws/5/vi/c85254e5-89e3-45c0-90e8-ab37bcde5161.mp4",
"cost": 0.24
}Cinematic POV Football
{
"taskType": "videoInference",
"taskUUID": "a8196bb5-f009-4929-b013-2bb22d3f9425",
"model": "lightricks:ltx@2.3-fast",
"positivePrompt": "First-person cinematic POV of an elite football player sprinting down the field during a night championship match, bright stadium floodlights, roaring crowd, defenders closing in, dynamic head-mounted camera motion, realistic hands and arms pumping, turf flying up under cleats, dramatic near-collision, fast dodge, spinning past a tackle, rushing toward the end zone, broadcast-quality realism, high energy, intense sports cinematography, natural motion blur, synchronized crowd cheers, footsteps, breath, impact sounds, immersive live stadium atmosphere",
"width": 1920,
"height": 1080,
"duration": 6,
"fps": 25,
"settings": {
"audio": true
}
}{
"taskType": "videoInference",
"taskUUID": "a8196bb5-f009-4929-b013-2bb22d3f9425",
"videoUUID": "fed11704-6d52-49b2-bc49-2af737664456",
"videoURL": "https://vm.runware.ai/video/os/a09d21/ws/5/vi/fed11704-6d52-49b2-bc49-2af737664456.mp4",
"cost": 0.24
}Neon Alley Chase
{
"taskType": "videoInference",
"taskUUID": "b99dcaa0-3c5b-4a99-b8c8-92d2740c4434",
"model": "lightricks:ltx@2.3-fast",
"positivePrompt": "A cinematic night-time cyberpunk alley soaked in rain, glowing neon signs in magenta and cyan reflecting on wet pavement, a sleek courier on a futuristic electric bike speeding through drifting steam, market tarps fluttering, puddles splashing, holographic billboards flickering overhead, dynamic camera tracking from low angle to side follow shot, rich atmosphere, dramatic motion blur, highly detailed environment, realistic lighting, energetic urban ambience with synchronized sound of rainfall, electric motor whine, distant sirens, buzzing signs, and tires spraying water",
"width": 1920,
"height": 1080,
"duration": 6,
"fps": 25,
"settings": {
"audio": true
}
}{
"taskType": "videoInference",
"taskUUID": "b99dcaa0-3c5b-4a99-b8c8-92d2740c4434",
"videoUUID": "d5419bed-1a50-4ffb-a27a-e799007ada70",
"videoURL": "https://vm.runware.ai/video/os/a17d13/ws/5/vi/d5419bed-1a50-4ffb-a27a-e799007ada70.mp4",
"cost": 0.24
}Cinematic F1 Racing
{
"taskType": "videoInference",
"taskUUID": "f92a00b4-442e-4981-9878-a8bf9bdb2554",
"model": "lightricks:ltx@2.3-fast",
"positivePrompt": "A cinematic Formula 1 race at golden hour on a world-class street circuit, low-slung camera tracking inches above the asphalt as sleek red and black F1 cars thunder through a tight corner, tire smoke, heat haze, sparks under the chassis, dramatic overtakes, cheering grandstands, fluttering flags, realistic reflections on polished bodywork, shallow depth of field, broadcast-grade action cinematography, intense speed, dynamic motion blur, crisp detail, realistic lighting, immersive atmosphere",
"width": 1920,
"height": 1080,
"duration": 6,
"fps": 25,
"settings": {
"audio": true
}
}{
"taskType": "videoInference",
"taskUUID": "f92a00b4-442e-4981-9878-a8bf9bdb2554",
"videoUUID": "f9dd6d51-28f2-406f-a9d2-32bf0f53cd55",
"videoURL": "https://vm.runware.ai/video/os/a17d13/ws/5/vi/f9dd6d51-28f2-406f-a9d2-32bf0f53cd55.mp4",
"cost": 0.24
}