apps/infsh/ltx-video

ltx-video

Create high-quality, realistic, and customizable videos quickly, with capabilities for producing detailed, high-resolution content.

run in browser run via API

run with your agent

# install belt

$curl -fsSL https://cli.inference.sh | sh

# view schema & details

$belt app get infsh/ltx-video

# run

$belt app run infsh/ltx-video

api reference

about

create high-quality, realistic, and customizable videos quickly, with capabilities for producing detailed, high-resolution content.

1. calling the api

install the client

the client provides a convenient way to interact with the api.

bash

1pip install inferencesh

setup your api key

set INFERENCE_API_KEY as an environment variable. get your key from settings → api keys.

bash

1export INFERENCE_API_KEY="inf_your_key"

run and get result

submit a request and wait for the final result. best for batch processing or when you don't need progress updates.

python

1from inferencesh import inference23client = inference()456result = client.run({7        "app": "infsh/ltx-video",8        "input": {}9    })1011print(result["output"])

stream live updates

get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks.

python

1from inferencesh import inference23client = inference()456# stream=True yields updates as they arrive7for update in client.run({8        "app": "infsh/ltx-video",9        "input": {}10    }, stream=True):11    if update.get("progress"):12        print(f"progress: {update['progress']}%")13    if update.get("output"):14        print(f"output: {update['output']}")

2. authentication

the api uses api keys for authentication. see the authentication docs for detailed setup instructions.

3. files

file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.

automatic upload

the python sdk automatically detects local file paths and uploads them. urls are passed through as-is.

python

1# local file paths are automatically uploaded2result = client.run({3    "app": "infsh/ltx-video",4    "input": {5        "image": "/path/to/local/image.png",  # detected & uploaded6        "audio": "https://example.com/audio.mp3",  # url passed through7    }8})

manual upload

you can also upload files manually and use the returned url.

python

1# upload and get a hosted URL2file = client.files.upload("/path/to/file.png")3print(file.uri)  # https://cloud.inference.sh/...

4. webhooks

get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.

python

1result = client.run({2    "app": "infsh/ltx-video",3    "input": {},4    "webhook": "https://your-server.com/webhook"5}, wait=False)

webhook payload

your endpoint receives a JSON POST with the task result:

json

1{2  "id": "task_abc123",3  "status": 9,4  "output": { ... },5  "error": "",6  "session_id": null,7  "created_at": "2024-01-15T10:30:00Z",8  "updated_at": "2024-01-15T10:30:05Z"9}

idstring— task id

statusnumber— terminal status (9=completed, 10=failed, 11=cancelled)

outputobject— task output (when completed)

errorstring— error message (when failed)

session_idstring— session id (if using sessions)

created_atstring— iso timestamp

updated_atstring— iso timestamp

5. schema

input

promptstring*

text prompt to guide video generation

negative_promptstring

negative prompt to specify undesired features

default: "worst quality, inconsistent motion, blurry, jittery, distorted"

widthinteger

width of the output video frames

default: 704

heightinteger

height of the output video frames

default: 480

num_framesinteger

number of frames to generate

default: 121

frame_rateinteger

frame rate for the output video

default: 30

num_inference_stepsinteger

number of denoising steps. use 4,8,16 for distilled models

default: 40

guidance_scalenumber

scale for classifier-free guidance

default: 3

seedinteger

random seed for reproducibility

default: 171198

conditioning_imagesarray

list of conditioning images

offload_to_cpuboolean

whether to offload to cpu

image_cond_noise_scalenumber

scale of noise for conditioning

default: 0.15

input_mediaobject

input video file for video-to-video generation

strengthnumber

strength of input video influence

default: 1

enable_prompt_enhancementboolean

explicitly enable or disable prompt enhancement. if none, will use word count threshold logic.

output

videoobject*

a class representing a file in the inference.sh ecosystem.

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