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# LTXVLatentUpsampler - ComfyUI Built-in Node Documentation

> Complete documentation for the LTXVLatentUpsampler node in ComfyUI. Learn its inputs, outputs, parameters and usage.

> This documentation was AI-generated. If you find any errors or have suggestions for improvement, please feel free to contribute! [Edit on GitHub](https://github.com/Comfy-Org/embedded-docs/blob/main/comfyui_embedded_docs/docs/LTXVLatentUpsampler/en.md)

The LTXVLatentUpsampler node increases the spatial resolution of a video latent representation by a factor of two. It uses a specialized upscale model to process the latent data, which is first un-normalized and then re-normalized using the provided VAE's channel statistics. This node is designed for video workflows within the latent space.

## Inputs

| Parameter       | Data Type              | Required | Range | Description                                                                                                           |
| --------------- | ---------------------- | -------- | ----- | --------------------------------------------------------------------------------------------------------------------- |
| `samples`       | LATENT                 | Yes      |       | The input latent representation of the video to be upscaled.                                                          |
| `upscale_model` | LATENT\_UPSCALE\_MODEL | Yes      |       | The loaded model used to perform the 2x upscaling on the latent data.                                                 |
| `vae`           | VAE                    | Yes      |       | The VAE model used to un-normalize the input latents before upscaling and to normalize the output latents afterwards. |

## Outputs

| Output Name | Data Type | Description                                                                                |
| ----------- | --------- | ------------------------------------------------------------------------------------------ |
| `LATENT`    | LATENT    | The upscaled latent representation, with spatial dimensions doubled compared to the input. |