ComfyUI  >  Nodes  >  KJNodes for ComfyUI >  SV3D Batch Schedule

ComfyUI Node: SV3D Batch Schedule

Class Name

SV3D_BatchSchedule

Category
KJNodes/experimental
Author
kijai (Account age: 2192 days)
Extension
KJNodes for ComfyUI
Latest Updated
6/25/2024
Github Stars
0.3K

How to Install KJNodes for ComfyUI

Install this extension via the ComfyUI Manager by searching for  KJNodes for ComfyUI
  • 1. Click the Manager button in the main menu
  • 2. Select Custom Nodes Manager button
  • 3. Enter KJNodes for ComfyUI in the search bar
After installation, click the  Restart button to restart ComfyUI. Then, manually refresh your browser to clear the cache and access the updated list of nodes.

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SV3D Batch Schedule Description

Facilitates scheduling azimuth and elevation conditions for SV3D video model, enabling dynamic visual effects in AI-generated videos.

SV3D Batch Schedule:

The SV3D_BatchSchedule node is designed to facilitate the scheduling of azimuth and elevation conditions for the SV3D video model. This node allows you to define how these conditions change over time, ensuring that the schedule always progresses forward. By using this node, you can create intricate and dynamic visual effects in your AI-generated videos, leveraging the capabilities of the SV3D model. This node is particularly useful for artists looking to add a layer of controlled motion and perspective changes to their video outputs, enhancing the overall visual storytelling.

SV3D Batch Schedule Input Parameters:

clip_vision

This parameter expects a CLIP_VISION input, which is used to process and understand the visual content of the input images. It is essential for the node to function correctly as it provides the necessary visual context.

init_image

The init_image parameter requires an IMAGE input, which serves as the initial frame or starting point for the video sequence. This image is the basis upon which the azimuth and elevation conditions will be applied.

vae

The vae parameter expects a VAE (Variational Autoencoder) input, which is used to encode and decode the images during the processing. This helps in maintaining the quality and consistency of the generated frames.

width

This parameter defines the width of the output images. It is an integer value with a default of 576, a minimum of 16, and a maximum defined by MAX_RESOLUTION. The step size is 8, allowing for granular control over the image width.

height

Similar to the width parameter, the height parameter sets the height of the output images. It also has a default value of 576, a minimum of 16, and a maximum defined by MAX_RESOLUTION, with a step size of 8.

batch_size

The batch_size parameter determines the number of frames to be processed in each batch. It is an integer value with a default of 21, a minimum of 1, and a maximum of 4096. This parameter impacts the processing time and memory usage.

interpolation

This parameter allows you to choose the interpolation method for the azimuth and elevation points. The available options are linear, ease_in, ease_out, and ease_in_out. Each method affects how smoothly the transitions between points occur.

azimuth_points_string

The azimuth_points_string parameter is a multiline string that defines the azimuth points and their corresponding values over time. The default value is "0:(0.0),\n9:(180.0),\n20:(360.0)\n". This parameter controls the horizontal rotation of the camera.

elevation_points_string

Similar to the azimuth points, the elevation_points_string parameter is a multiline string that specifies the elevation points and their values over time. The default value is "0:(0.0),\n9:(0.0),\n20:(0.0)\n". This parameter controls the vertical rotation of the camera.

SV3D Batch Schedule Output Parameters:

positive

The positive output is a CONDITIONING type that represents the positive conditioning applied to the input image sequence. This output is used to guide the model towards generating the desired visual effects based on the specified azimuth and elevation conditions.

negative

The negative output is also a CONDITIONING type, representing the negative conditioning applied to the input image sequence. This output helps in refining the generated frames by providing a counterbalance to the positive conditioning.

latent

The latent output is a LATENT type that contains the latent representations of the processed frames. These latent representations are crucial for further processing and refinement in the video generation pipeline.

SV3D Batch Schedule Usage Tips:

  • Ensure that the init_image is of high quality and relevant to the desired video output to achieve the best results.
  • Experiment with different interpolation methods to find the one that best suits your artistic vision.
  • Adjust the batch_size according to your system's capabilities to balance between processing time and memory usage.

SV3D Batch Schedule Common Errors and Solutions:

"Invalid input type for clip_vision"

  • Explanation: The input provided for the clip_vision parameter is not of type CLIP_VISION.
  • Solution: Ensure that the input connected to the clip_vision parameter is a valid CLIP_VISION type.

"Invalid input type for init_image"

  • Explanation: The input provided for the init_image parameter is not of type IMAGE.
  • Solution: Ensure that the input connected to the init_image parameter is a valid IMAGE type.

"Width or height out of range"

  • Explanation: The values provided for the width or height parameters are outside the allowed range.
  • Solution: Adjust the width and height values to be within the specified range (16 to MAX_RESOLUTION).

"Batch size out of range"

  • Explanation: The value provided for the batch_size parameter is outside the allowed range.
  • Solution: Set the batch_size to a value between 1 and 4096.

"Invalid interpolation method"

  • Explanation: The selected interpolation method is not one of the allowed options.
  • Solution: Choose an interpolation method from the available options: linear, ease_in, ease_out, ease_in_out.

SV3D Batch Schedule Related Nodes

Go back to the extension to check out more related nodes.
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