Wiro - LTX Text to Video icon

Wiro - LTX Text to Video

LTX-Video is the first DiT-based video generation model capable of generating high-quality vide

Wiro - LTX Text to Video

AI-generated

Overview

This node integrates with the Wiro LTX Text to Video API to generate videos from text prompts. It is useful for scenarios where users want to create high-quality videos based on descriptive text inputs, such as generating video content for marketing, storytelling, or creative projects. The node sends the prompt and various parameters to the API, waits for the video generation task to complete, and outputs the video URL and status.

Use Case Examples

  1. Generate a video from a creative text prompt with specific frame rate and resolution settings.
  2. Create a video with a negative prompt to exclude certain elements, specifying the number of frames and inference steps for quality control.

Properties

Name Meaning
Prompt The main text prompt describing the content to generate in the video.
Negative Prompt Text prompt specifying elements to exclude from the video generation.
Fps Frames per second for the generated video.
Width Width of the generated video in pixels.
Height Height of the generated video in pixels.
Num Frames Number of frames to generate in the video.
Inferencesteps Number of inference steps to control the quality and detail of the video generation.
Seed Seed value for random number generation to ensure reproducibility of the video output.

Output

JSON

  • taskid - The unique identifier of the video generation task.
  • url - The URL where the generated video can be accessed.
  • status - The status of the video generation task, indicating if it completed successfully or failed.

Dependencies

  • Wiro API key and secret credentials for authentication

Troubleshooting

  • If the node throws an error indicating missing or invalid task ID or socket access token, verify that the Wiro API credentials are correct and that the API endpoint is reachable.
  • If the video generation task status is 'failed', check the input parameters for validity and ensure the prompt is appropriate for the model.

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