usamaehsan/ltx-2.5

Public
440 runs

Run usamaehsan/ltx-2.5 with an API

Use one of our client libraries to get started quickly. Clicking on a library will take you to the Playground tab where you can tweak different inputs, see the results, and copy the corresponding code to use in your own project.

Input schema

The fields you can use to run this model with an API. If you don't give a value for a field its default value will be used.

Field Type Default value Description
prompt
string
Text prompt describing the video
image
string
Optional first-frame image (image-to-video)
keyframe_2
string
Optional second keyframe. The video is steered to pass through this image at keyframe_2_at seconds. Leave keyframe_2_at at -1 to pin it to the final frame (first-frame/last-frame interpolation).
keyframe_2_at
number
-1

Min: -1

Position of keyframe_2 in seconds; -1 = last frame
keyframe_3
string
Optional third keyframe
keyframe_3_at
number
-1

Min: -1

Position of keyframe_3 in seconds; -1 = last frame
keyframe_4
string
Optional fourth keyframe
keyframe_4_at
number
-1

Min: -1

Position of keyframe_4 in seconds; -1 = last frame
keyframe_strength
number
1

Max: 1

How hard the keyframes are enforced. 1.0 locks them exactly; lower values let the model blend, which can look more natural.
generated_keyframes
integer
0

Max: 8

Have the model invent this many evenly spaced keyframes before filling in between them. Can improve coherence on long clips. 0 = off.
audio
string
Optional audio track to drive the video. The video is generated against this audio instead of the model inventing its own. When duration is 0 the clip length follows the audio.
audio_start_time
number
0
Seconds to skip into the audio file
camera_move
None
none
Camera motion to impose on the shot. 'none' leaves the camera to the prompt; 'static' actively holds it still.
camera_strength
number
1

Max: 2

How strongly the camera move is applied. Above ~1.2 the motion gets more literal at the cost of image quality.
control_video
string
Control-signal video for in-context control. Must be frame-aligned with the shot you want: a depth / pose / canny-edge pass for control_type 'union', or coloured motion splines for 'motion_track'. Ignored unless control_type is set.
control_type
None
none
Which in-context adapter reads control_video. 'union' handles depth, pose and canny (it infers which from the video itself); 'motion_track' follows drawn point trajectories.
control_strength
number
1

Max: 1

How closely the output follows control_video. 1.0 keeps the reference clean and fully authoritative.
reference_image
string
Context image: a person, product, logo or place to put INTO the video, anywhere in the shot (not a frame lock -- use image for that). Best as a reference sheet: one clean panel per element on black, no text. Write the prompt as 'Reference sheet: <what the panels show>' then 'Generated video: <the action>'; a plain prompt is wrapped automatically. Trained at 768x448 landscape. Cannot be combined with control_video.
width
integer
768

Min: 256

Max: 1536

None
height
integer
1280

Min: 256

Max: 1536

None
duration
number
4

Max: 20

Clip length in seconds. 0 = auto: follow the audio if one is given, otherwise let the model predict a length from the prompt.
seed
integer
-1
-1 = random

Output schema

The shape of the response you’ll get when you run this model with an API.

Schema
{
  "type": "string",
  "title": "Output",
  "format": "uri"
}