> ## Documentation Index
> Fetch the complete documentation index at: https://dripart-nav-docs-metadata-batch2.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# BerniniConditioning - ComfyUI Built-in Node Documentation

> The BerniniConditioning node prepares video and image conditioning data for the Wan2.2-A14B model.

The BerniniConditioning node prepares video and image conditioning data for the Wan2.2-A14B model. It encodes source videos, reference videos, and reference images using the provided VAE, then attaches them to the conditioning data as in-context tokens. The task is automatically inferred from which inputs are connected.

## Inputs

| Parameter          | Description                                                                                                                                                                                          | Data Type    | Required | Range                 |
| ------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------ | -------- | --------------------- |
| `positive`         | Positive conditioning data                                                                                                                                                                           | CONDITIONING | Yes      | -                     |
| `negative`         | Negative conditioning data                                                                                                                                                                           | CONDITIONING | Yes      | -                     |
| `vae`              | VAE model used to encode video and image inputs                                                                                                                                                      | VAE          | Yes      | -                     |
| `width`            | Width of the output latent (default: 832)                                                                                                                                                            | INT          | Yes      | 16 to 8192 (step: 16) |
| `height`           | Height of the output latent (default: 480)                                                                                                                                                           | INT          | Yes      | 16 to 8192 (step: 16) |
| `length`           | Number of frames in the output latent (default: 81)                                                                                                                                                  | INT          | Yes      | 1 to 8192 (step: 4)   |
| `batch_size`       | Number of videos to generate in a single batch (default: 1)                                                                                                                                          | INT          | Yes      | 1 to 4096             |
| `source_video`     | Source video to edit or restyle (v2v, rv2v). Resized to width/height and trimmed to length.                                                                                                          | IMAGE        | No       | -                     |
| `reference_video`  | Video to insert into the source video (ads2v). Trimmed to length and resized with preserved aspect ratio (long edge capped at ref\_max\_size).                                                       | IMAGE        | No       | -                     |
| `reference_images` | Reference images injected as in-context tokens (r2v, rv2v). Each image is encoded independently at its own native aspect ratio (long edge capped at ref\_max\_size). Up to 8 images can be provided. | IMAGE        | No       | 0 to 8 images         |
| `ref_max_size`     | Max size for the long edge of reference\_video and reference\_images. Resized with preserved aspect ratio and snapped to 16px (default: 848).                                                        | INT          | No       | 16 to 8192 (step: 16) |

**Note:** The task is inferred from which inputs are connected:

* No inputs connected → text-to-video (t2v)
* `source_video` only → video-to-video (v2v)
* `source_video` + `reference_images` → reference-guided video editing (rv2v)
* `reference_images` only → reference-to-video (r2v)
* `source_video` + `reference_video` → insert image/video into video (ads2v)

Context streams are attached in a fixed order: `source_video` (source ID 1), then `reference_video` (source ID 2), then each reference image (source IDs 3, 4, ...). A reference image input containing multiple images contributes one stream per image.

## Outputs

| Output Name | Description                                                                                      | Data Type    |
| ----------- | ------------------------------------------------------------------------------------------------ | ------------ |
| `positive`  | Positive conditioning with context latents attached                                              | CONDITIONING |
| `negative`  | Negative conditioning with context latents attached                                              | CONDITIONING |
| `latent`    | Empty latent tensor with dimensions matching the specified width, height, length, and batch size | LATENT       |

> 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/BerniniConditioning/en.md)

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