lightricks/ltx-video-13b-distilled/image-to-video

Name: LTX Video-0.9.7 13B Distilled
Author: Lightricks

Ltx Video 13b Distilled by Lightricks - animate still images into dynamic videos with AI. Transform photos into cinematic clips with natural motion, camera movement, and optional audio generation.

Input

Prompt(string)

Text prompt to guide generation

Image URL or upload(string)

Upload

PNG, JPEG, WebP, or GIF · 20 MiB maximum

Image URL for Image-to-Video task

This field is required.

Resolution(string)

Resolution of the generated video. Allowed values: 480p, 720p.

Loras(array)

LoRA weights to use for generation

Expand Prompt(boolean)

Whether to expand the prompt using a language model.

Second Pass Skip Initial Steps(integer)

111

The number of inference steps to skip in the initial steps of the second pass. By skipping some steps at the beginning, the second pass can focus on smaller details instead of larger changes. Range: 1 to 11.

Frame Rate(integer)

160

The frame rate of the video. Range: 1 to 60.

Tone Map Compression Ratio(number)

The compression ratio for tone mapping. This is used to compress the dynamic range of the video to improve visual quality. A value of 0.0 means no compression, while a value of 1.0 means maximum compression. Range: 0 to 1.

Enable Detail Pass(boolean)

Whether to use a detail pass. If True, the model will perform a second pass to refine the video and enhance details. This incurs a 2.0x cost multiplier on the base price.

Number of Inference Steps(integer)

212

Number of inference steps during the first pass. Range: 2 to 12.

Constant Rate Factor(integer)

051

The constant rate factor (CRF) to compress input media with. Compressed input media more closely matches the model's training data, which can improve motion quality. Range: 0 to 51.

Temporal AdaIN Factor(number)

The factor for adaptive instance normalization (AdaIN) applied to generated video chunks after the first. This can help deal with a gradual increase in saturation/contrast in the generated video by normalizing the color distribution across the video. A high value will ensure the color distribution is more consistent across the video, while a low value will allow for more variation in color distribution. Range: 0 to 1.

Second Pass Number of Inference Steps(integer)

212

Number of inference steps during the second pass. Range: 2 to 12.

Reverse Video(boolean)

Whether to reverse the video.

Number of Frames(integer)

91441

The number of frames in the video. Range: 9 to 1441.

Aspect Ratio(string)

The aspect ratio of the generated image. Allowed values: 21:9, 16:9, 3:2, 4:3, 5:4, 1:1, 4:5, 3:4, 2:3, 9:16.

Seed(integer)

Random seed for generation

Idle

Example output — click Run to generate your own

Production endpoint

Send your first request

OpenAI-compatible endpoint with unified authentication and usage tracking.

POSThttps://api.sandbase.ai/v1/run

Model IDlightricks/ltx-video-13b-distilled/image-to-video

Input Schema

17 parameters · 2 required · 15 optional

Parameter	Type	Required	Description
`image`	string	Required	Image URL for Image-to-Video task
`prompt`	string	Required	Text prompt to guide generation
`seed`	integer	Optional	Random seed for generation
`loras`	object[]	Optional	LoRA weights to use for generation · Default: []
`frame_rate`	integer	Optional	The frame rate of the video. · Min: 1 · Max: 60 · Default: 24
`num_frames`	integer	Optional	The number of frames in the video. · Min: 9 · Max: 1441 · Default: 121
`resolution`	string	Optional	Resolution of the generated video. · Options: 480p, 720p · Default: "720p" 480p720p
`aspect_ratio`	string	Optional	The aspect ratio of the generated image. · Options: 21:9, 16:9, 3:2, 4:3, 5:4, 1:1, 4:5, 3:4, 2:3, 9:16 21:916:93:24:35:41:14:53:42:39:16
`expand_prompt`	boolean	Optional	Whether to expand the prompt using a language model. · Default: false
`reverse_video`	boolean	Optional	Whether to reverse the video. · Default: false
`enable_detail_pass`	boolean	Optional	Whether to use a detail pass. If True, the model will perform a second pass to refine the video and enhance details. This incurs a 2.0x cost multiplier on the base price. · Default: false
`constant_rate_factor`	integer	Optional	The constant rate factor (CRF) to compress input media with. Compressed input media more closely matches the model's training data, which can improve motion quality. · Min: 0 · Max: 51 · Default: 29
`temporal_adain_factor`	number	Optional	The factor for adaptive instance normalization (AdaIN) applied to generated video chunks after the first. This can help deal with a gradual increase in saturation/contrast in the generated video by normalizing the color distribution across the video. A high value will ensure the color distribution is more consistent across the video, while a low value will allow for more variation in color distribution. · Min: 0 · Max: 1 · Default: 0.5
`tone_map_compression_ratio`	number	Optional	The compression ratio for tone mapping. This is used to compress the dynamic range of the video to improve visual quality. A value of 0.0 means no compression, while a value of 1.0 means maximum compression. · Min: 0 · Max: 1 · Default: 0
`first_pass_num_inference_steps`	integer	Optional	Number of inference steps during the first pass. · Min: 2 · Max: 12 · Default: 8
`second_pass_skip_initial_steps`	integer	Optional	The number of inference steps to skip in the initial steps of the second pass. By skipping some steps at the beginning, the second pass can focus on smaller details instead of larger changes. · Min: 1 · Max: 11 · Default: 5
`second_pass_num_inference_steps`	integer	Optional	Number of inference steps during the second pass. · Min: 2 · Max: 12 · Default: 8

Output Schema

Field	Type	Description
`id`	string	Unique identifier for the generation task
`status`	string	Task status: pending, running, completed, failed, timeout
`model`	string	Model used for the generation
`outputs`	array	Array of output items
`outputs[].url`	string	URL of the generated artifact
`outputs[].content_type`	string	MIME type (e.g. image/png, video/mp4)
`error`	object \| null	Error details if failed, null on success
`error.type`	string	Machine-readable error type code
`error.message`	string	Human-readable error description

Async Workflow

This model uses asynchronous execution. Submit a request and poll for the result.

Submit — POST to /v1/run, receive an id
Poll — GET /v1/run/{id} until status is completed, failed, or timeout
Retrieve — Read outputs from the completed response

Code Examples

Ready-to-run snippets

# Step 1: Submit
curl -X POST https://api.sandbase.ai/v1/run \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
  "model": "lightricks/ltx-video-13b-distilled/image-to-video",
  "image": "https://static.sandbase.ai/examples/lightricks/ltx-video-13b-distilled/image-to-video/input_image_0.jpg",
  "loras": [],
  "prompt": "The astronaut gets up and walks away",
  "frame_rate": 24,
  "num_frames": 121,
  "resolution": "720p",
  "expand_prompt": false,
  "reverse_video": false,
  "enable_detail_pass": false,
  "constant_rate_factor": 29,
  "temporal_adain_factor": 0.5,
  "tone_map_compression_ratio": 0,
  "first_pass_num_inference_steps": 8,
  "second_pass_skip_initial_steps": 5,
  "second_pass_num_inference_steps": 8
}'

# Step 2: Poll result (replace <id>)
curl https://api.sandbase.ai/v1/run/<id> \
  -H "Authorization: Bearer YOUR_API_KEY"

← Back to LTX Video-0.9.7 13B Distilled

API README

LTX Video-0.9.7 13B Distilled

LTX Video-0.9.7 13B Distilled is a LTX route built for image-to-video generation. It turns a source image and motion direction into a moving sequence that develops the supplied frame, giving creators a focused endpoint instead of forcing one generic workflow across materially different production tasks. The route belongs to a family known for audio-visual timing, cinematic motion, and shot-level controllability, so it is best evaluated as a creative system for intentional shots and assets rather than as a one-click novelty generator.

Use this endpoint when the input contract and deliverable match that job exactly. Its documented controls include seed (Random seed for generation); loras (LoRA weights to use for generation); frame_rate (The frame rate of the video); num_frames (The number of frames in the video). Together, these controls help teams plan predictable iterations, compare outputs under stable settings, and connect generation to iterative film, advertising, and social-video workflows without hiding the operational choices that shape the result.

Highlights

Purpose-built Image-to-video generation. The route accepts a source image and motion direction and produces a moving sequence that develops the supplied frame; its interface is scoped to that transformation, keeping source assets and creative intent explicit.
Creative direction. Prompts can describe subject behavior, composition, camera intent, lighting, material, atmosphere, and temporal progression so the result is driven by a shot plan rather than isolated keywords.
Route-specific control. The request exposes seed (Random seed for generation); loras (LoRA weights to use for generation); frame_rate (The frame rate of the video); num_frames (The number of frames in the video), allowing the same concept to be tested systematically while preserving a repeatable production setup.
Pipeline-ready output. The generated media asset is returned through the documented asynchronous output contract, which suits review queues, batch iteration, and downstream automation. Editors can review pacing, continuity, lens language, choreography, transitions, temporal artifacts, soundtrack alignment, color response, delivery framing, and cut compatibility before approval.

Pricing

Configuration	Price
Standard request	`$0.040000`

When to Use

Scenario	Why this model fits
Create the exact route output	Choose it when you need image-to-video generation and already have a source image and motion direction.
Develop controlled variations	Keep the main brief fixed while changing one documented setting at a time to compare motion, framing, quality, or asset behavior.
Build repeatable batches	Use a consistent request shape for catalog, campaign, storyboard, game-asset, or social-content production.
Preserve source intent	Prefer this route when the supplied reference material must remain the foundation of a moving sequence that develops the supplied frame.
Connect a media pipeline	Use asynchronous results in an automated review, approval, post-production, or asset-management workflow.

Prompt Guide

Start with the desired result, then describe the source relationship, subject action, composition or camera behavior, lighting, style, and timing. For image-to-video generation, state what must remain stable as clearly as what should change. Use only fields exposed by the schema; the example below is structurally valid for this route.

{
  "prompt": "The astronaut gets up and walks away",
  "image": "https://static.sandbase.ai/examples/lightricks/ltx-video-13b-distilled/image-to-video/input_image_0.jpg",
  "seed": 1,
  "loras": [],
  "frame_rate": 24
}

Technical Specs

Specification	Value
Model ID	`lightricks/ltx-video-13b-distilled/image-to-video`
Workflow	Image-to-video generation
Required inputs	`prompt`, `image`
`seed`	`integer`
`image`	`string`
`loras`	`array`
`prompt`	`string`
`frame_rate`	`integer`; minimum: `1`; maximum: `60`
`num_frames`	`integer`; minimum: `9`; maximum: `1441`
`resolution`	`string`; options: `480p`, `720p`
`aspect_ratio`	`string`; options: `21:9`, `16:9`, `3:2`, `4:3`, `5:4`, `1:1`, `4:5`, `3:4`, `2:3`, `9:16`
`expand_prompt`	`boolean`
`reverse_video`	`boolean`