ComfyUI Node: Sample Trajectories

Sample Trajectories:

The TrajectoryNode is designed to generate trajectories from a sequence of images, which can be particularly useful in AI art and video processing tasks. This node leverages advanced optical flow models to analyze the movement within a series of images, creating a detailed map of trajectories that represent the motion patterns. By breaking down the image sequence into manageable windows and processing them with a pre-trained model, the node can efficiently compute and return the trajectories. This functionality is essential for tasks that require understanding and manipulating motion, such as video editing, animation, and dynamic scene generation.

Sample Trajectories Input Parameters:

images

This parameter expects a sequence of images that the node will process to generate trajectories. The images should be provided in a format that the node can interpret, typically as a tensor or array. The quality and resolution of the images can impact the accuracy and detail of the generated trajectories.

context_length

This integer parameter defines the length of the context window used to analyze the images. It determines how many images are considered in each window for trajectory computation. The default value is 20, with a minimum of 0 and a maximum of 40. Adjusting this value can affect the granularity and scope of the motion analysis.

context_overlap

This integer parameter specifies the overlap between consecutive context windows. It helps in maintaining continuity and smoothness in the trajectory generation by reusing parts of the image sequence. The default value is 10, with a minimum of 0. Increasing the overlap can lead to more consistent trajectories but may also increase computational load.

Sample Trajectories Output Parameters:

TRAJECTORY

The output is a dictionary containing the computed trajectories. This dictionary includes trajectory_windows, which maps each window to its corresponding trajectories, and context_windows, which provides the context windows used in the analysis. Additionally, it includes the height and width of the images, ensuring that the trajectories are correctly aligned with the original image dimensions. This output is crucial for further processing and analysis in tasks that involve motion understanding and manipulation.

Sample Trajectories Usage Tips:

• To achieve the best results, ensure that the input images are of high quality and have consistent resolution.
• Experiment with different context_length and context_overlap values to find the optimal balance between computational efficiency and trajectory accuracy.
• Use the generated trajectories as input for subsequent nodes or processes that require detailed motion information, such as animation or video editing tools.

Sample Trajectories Common Errors and Solutions:

"Invalid image format"

• Explanation: The input images are not in a format that the node can process.
• Solution: Ensure that the images are provided as a tensor or array in a compatible format.

"Context length out of range"

• Explanation: The context_length parameter is set to a value outside the allowed range.
• Solution: Adjust the context_length to be within the range of 0 to 40.

"Context overlap out of range"

• Explanation: The context_overlap parameter is set to a value less than 0.
• Solution: Ensure that the context_overlap is set to a non-negative integer.

"Model loading error"

• Explanation: There was an issue loading the optical flow model.
• Solution: Verify that the required model weights are available and correctly specified in the node configuration.