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.

Complete technical specification for integration
Ready-to-use code snippets for common workflows
API Options
Platform-level options for task execution and delivery.
taskType
stringrequiredvalue: videoInferenceIdentifier for the type of task being performed
taskUUID
stringrequiredUUID v4UUID v4 identifier for tracking tasks and matching async responses. Must be unique per task.
outputType
stringdefault: URLVideo output type.
Allowed values1 value
outputFormat
stringdefault: MP4Specifies 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 values3 values
outputQuality
integermin: 20max: 99default: 95Compression quality of the output. Higher values preserve quality but increase file size.
webhookURL
stringURISpecifies 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 more1 resource
- WebhooksPLATFORM
- Webhooks
deliveryMethod
stringdefault: asyncDetermines how the API delivers task results.
Allowed values1 value
- Returns an immediate acknowledgment with the task UUID. Poll for results using getResponse. Required for long-running tasks like video generation.
Learn more1 resource
- Task PollingPLATFORM
uploadEndpoint
stringURISpecifies 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
objectContent safety checking configuration for video generation.
Properties2 properties
safety»checkContentcheckContent
booleanEnable or disable content safety checking.
safety»modemode
stringdefault: noneSafety checking mode for video generation.
Allowed values3 values
- Disables checking.
- Checks key frames.
- Checks all frames.
ttl
integermin: 60Time-to-live (TTL) in seconds for generated content. Only applies when
outputTypeisURL.
includeCost
booleanInclude task cost in the response.
numberResults
integermin: 1max: 4default: 1Number 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 objectsmin items: 1max items: 500An 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 position in the video.
The
frameImagesparameter allows you to constrain specific frames within the video sequence, ensuring that particular visual content appears at designated points. Position can be specified usingframe(named positions or frame indices) ortimestamp(seconds), depending on model support. 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.
- 2 images: First and last frames.
- 3+ images: First and last frames, with intermediate images evenly spaced between.
Examples4 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 position, use an object with
imageand eitherframeortimestamp(model-dependent).First and last frames: With two images, they automatically become the first and last frames of the video sequence. You can mix shorthand and object formats."frameImages": [ { "image": "aac49721-1964-481a-ae78-8a4e29b91402", "frame": "first" } ]Multiple frames: With three or more images, the first and last are anchored while intermediate frames are evenly distributed across the timeline."frameImages": [ "aac49721-1964-481a-ae78-8a4e29b91402", { "image": "3ad204c3-a9de-4963-8a1a-c3911e3afafe", "frame": "last" } ]"frameImages": [ { "image": "aac49721-1964-481a-ae78-8a4e29b91402", "frame": "first" }, "c00abf5f-6cdb-4642-a01d-1bfff7bc3cf7", { "image": "3ad204c3-a9de-4963-8a1a-c3911e3afafe", "frame": "last" } ]Format 1: string[]
Image input (UUID, URL, Data URI, or Base64).
Format 2: object[]2 properties
inputs»audioaudio
stringAudio input (UUID or URL). Enables audio-to-video conditioning. The audio is merged with the generated video and trimmed to fit the video duration. Maximum duration 30 seconds.
Core Parameters
Primary parameters that define the task output.
model
stringrequiredvalue: lightricks:ltx@2.3Identifier of the model to use for generation.
Learn more3 resources
positivePrompt
stringrequiredmin: 2max: 10000Text prompt describing elements to include in the generated output.
Learn more1 resource
- PromptsLEARN
- Prompts
negativePrompt
stringPrompt to guide what to exclude from generation. Ignored when guidance is disabled (CFGScale ≤ 1).
Learn more1 resource
width
integerrequiredmin: 128max: 2048step: 64Width of the generated media in pixels.
Learn more2 resources
height
integerrequiredmin: 128max: 2048step: 64Height of the generated media in pixels.
Learn more2 resources
duration
floatrequiredmin: 1max: 20Length 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
integermin: 1max: 120default: 25Frames per second for video generation. Higher values create smoother motion but require more processing time.
seed
integermin: 0max: 9223372036854776000Random seed for reproducible generation. When not provided, a random seed is generated in the unsigned 32-bit range.
Learn more1 resource
- SeedLEARN
- Seed
steps
integermin: 1max: 100default: 15Total number of denoising steps. Higher values generally produce more detailed results but take longer.
Learn more1 resource
- StepsLEARN
- Steps
CFGScale
floatmin: 1max: 20step: 0.01default: 4Guidance scale representing how closely the output will resemble the prompt. Higher values produce results more aligned with the prompt.
Learn more1 resource
- Cfg ScaleLEARN
- Cfg Scale
lora
array of objectsmin items: 1With LoRA (Low-Rank Adaptation), you can adapt a model to specific styles or features by emphasizing particular aspects of the data. This technique enhances the quality and relevance of generated content and can be especially useful when the output needs to adhere to a specific artistic style or follow particular guidelines.
Multiple LoRA models can be used simultaneously to achieve different adaptation goals.
Examples1 example
"lora": [ { "model": "<lora-model-air>", "weight": 0.8 } ]Learn more1 resource
- LorasLEARN
Properties3 properties
lora»modelmodel
stringrequiredLoRA model identifier.
lora»weightweight
floatmin: -4max: 4step: 0.01default: 1Strength of the LoRA influence. A value of 0 means no influence. Higher values increase the influence, and negative values can be used to steer away from the LoRA's style.
lora»transformertransformer
stringdefault: bothTransformer stages to apply LoRA. Some video models use separate high-noise and low-noise processing stages, and LoRAs can be selectively applied to optimize their effectiveness.
Allowed values3 values
- Apply LoRA only to the high-noise processing stage (coarse structure and early generation steps).
- Apply LoRA only to the low-noise processing stage (fine details and later generation steps).
- Apply LoRA to both stages for full coverage.
- Loras
Settings
Technical parameters to fine-tune the inference process. These must be nested inside the settings object.
settings object.settings»enhancePromptenhancePrompt
booleandefault: trueEnable automatic prompt enhancement for cinematic results.