LTX-2 Fast
LTX-2 Fast is the high speed tier of the LTX-2 video foundation model. It targets rapid cinematic iteration with strong motion quality and visual consistency. Generate short synced audio video clips from text or image prompts with low latency and efficient GPU use.
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:2@1 -
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
integer paired with height -
Width of the generated media in pixels.
Learn more 2 resources
-
height
integer paired with width -
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 2 values
Provider Settings
Parameters specific to this model provider. These must be nested inside the providerSettings.lightricks object.
providerSettings.lightricks object.-
providerSettings»lightricks»generateAudiogenerateAudio
boolean default: false -
Generate synchronized audio.
Stormglass Lighthouse Approach
{
"taskType": "videoInference",
"taskUUID": "2c3254e1-4830-4554-b8a1-94fdd68cc8a7",
"model": "lightricks:2@1",
"positivePrompt": "Using the reference image as the opening frame, create a cinematic coastal sequence: the camera glides slowly forward along the slick path toward the glass-topped lighthouse as wind drives spray across the lens plane, waves slam against black rocks, gulls wheel overhead, the beacon begins a slow rotating sweep through mist, distant lightning flickers inside towering storm clouds, puddles ripple under gusts, fine environmental motion throughout, realistic textures, strong depth, dramatic atmosphere, natural color grading, consistent structure and composition, immersive synced ocean-and-storm audio",
"width": 1920,
"height": 1080,
"duration": 8,
"fps": 25,
"providerSettings": {
"lightricks": {
"generateAudio": true
}
},
"inputs": {
"frameImages": [
{
"image": "https://assets.runware.ai/assets/inputs/458f3b8d-6911-4f15-b938-c2b2b6797b4a.jpg",
"frame": "first"
}
]
}
}{
"taskType": "videoInference",
"taskUUID": "2c3254e1-4830-4554-b8a1-94fdd68cc8a7",
"videoUUID": "1b852344-1a9c-4cd4-8ecf-49d4489e5d68",
"videoURL": "https://vm.runware.ai/video/os/a10d08/ws/5/vi/1b852344-1a9c-4cd4-8ecf-49d4489e5d68.mp4",
"cost": 0.32
}Windblown Tundra Caravan Crossing
{
"taskType": "videoInference",
"taskUUID": "0f487490-2178-4360-87b3-16d69a805729",
"model": "lightricks:2@1",
"positivePrompt": "A cinematic wide shot of a nomadic caravan crossing a vast frozen tundra at blue-hour twilight, shaggy pack animals pulling wooden sleds draped with patterned blankets, travelers in layered wool and fur, gusts of snow skimming across the ground, long grass and prayer ribbons whipping in the wind, distant ice ridges and low clouds on the horizon. The camera begins low near frost-covered stones and slowly tracks alongside the caravan, then gently rises to reveal the full line of travelers stretching across the landscape. Rich atmospheric depth, crisp cold air, visible breath, realistic footfalls and sled tracks, natural cloth motion, subtle lens haze, immersive scale, grounded cinematic realism. Synchronized audio of wind gusts, creaking sled runners, muffled bells, animal snorts, boots crunching through crusted snow, and quiet human chatter carried by the wind.",
"width": 1920,
"height": 1080,
"duration": 8,
"fps": 25,
"providerSettings": {
"lightricks": {
"generateAudio": true
}
}
}{
"taskType": "videoInference",
"taskUUID": "0f487490-2178-4360-87b3-16d69a805729",
"videoUUID": "2fa269a3-1519-4415-9e14-6d0504888aa8",
"videoURL": "https://vm.runware.ai/video/os/a17d13/ws/5/vi/2fa269a3-1519-4415-9e14-6d0504888aa8.mp4",
"cost": 0.32
}Flooded Grand Piano Atrium
{
"taskType": "videoInference",
"taskUUID": "2c98a2fb-7c10-48d3-a9f3-25382dc857c1",
"model": "lightricks:2@1",
"positivePrompt": "Using the reference image as the first frame, create a cinematic sequence in a vast flooded atrium centered on a lone grand piano. The camera glides slowly forward at water level, with gentle parallax across columns and vines. Ripples spread through the shallow water, floating sheet music drifts and turns, dust motes shimmer in angled morning light, a few droplets fall from the glass roof, and faint breeze stirs leaves and loose pages. Preserve the architectural layout and realistic materials from the reference image, emphasizing elegant motion, reflective surfaces, atmospheric depth, and natural synchronized ambience of water laps, soft creaks, distant birds, and airy room tone.",
"width": 1920,
"height": 1080,
"duration": 8,
"fps": 25,
"providerSettings": {
"lightricks": {
"generateAudio": true
}
},
"inputs": {
"frameImages": [
{
"image": "https://assets.runware.ai/assets/inputs/6d14984d-9545-4766-8d70-68ec871e016a.jpg",
"frame": "first"
}
]
}
}{
"taskType": "videoInference",
"taskUUID": "2c98a2fb-7c10-48d3-a9f3-25382dc857c1",
"videoUUID": "94a5f157-c29d-4041-8e42-a6c624dd5923",
"videoURL": "https://vm.runware.ai/video/os/a07d11/ws/5/vi/94a5f157-c29d-4041-8e42-a6c624dd5923.mp4",
"cost": 0.32
}