ComfyUI Node: MotionScale (temporal RoPE scale) ⚡

MotionScale (temporal RoPE scale) ⚡:

IAMCCS_MotionScale is a specialized node designed to enhance temporal RoPE (Rotary Position Embedding) position scaling, specifically tailored for Wan 2.2. This node replaces the previous WanMotionScale, offering improved functionality and precision in handling temporal data. The primary goal of IAMCCS_MotionScale is to provide a more refined and accurate scaling mechanism for temporal embeddings, which is crucial in applications involving time-series data or sequences where the temporal aspect is significant. By leveraging advanced scaling techniques, this node ensures that the temporal dynamics are preserved and accurately represented, leading to better performance in tasks that require temporal awareness. This makes it an essential tool for AI artists and developers working with temporal data, as it simplifies the process of managing and scaling temporal embeddings, ultimately enhancing the quality and reliability of the results.

MotionScale (temporal RoPE scale) ⚡ Input Parameters:

latent_refresh

The latent_refresh parameter is a floating-point value that controls the strength of DC drift correction. It ranges from 0.0 to 1.0, with a default value of 0.0. A value of 0 disables the correction, while a value of 1 applies full correction. This parameter is crucial for correcting the per-channel mean shift between the motion tail and anchor latent without affecting the spatial structure or motion. It is particularly useful in scenarios involving chains of three or more segments, where starting with a value of 0.5 is recommended. Enabling diagnostic logging can help verify the effectiveness of this correction.

delta_max

The delta_max parameter is a floating-point value that applies a soft clamp on the DC drift itself. It ranges from 0.0 to 1.0, with 0 disabling the clamp. Values between 0.5 and 1.0 are recommended to prevent over-correction on large motion segments. This parameter helps maintain stability and prevents excessive adjustments that could distort the temporal dynamics.

MotionScale (temporal RoPE scale) ⚡ Output Parameters:

motion_mean

The motion_mean output parameter represents the mean value of the motion latent across specified dimensions. It provides a summary statistic that captures the average behavior of the motion data, which is essential for understanding the overall temporal dynamics and ensuring that the scaling adjustments are accurately applied.

anchor_mean

The anchor_mean output parameter represents the mean value of the anchor latent across specified dimensions. Similar to motion_mean, it provides a baseline statistic that helps in comparing and correcting the motion data. This output is crucial for identifying and correcting any discrepancies between the motion and anchor latents, ensuring that the temporal scaling is consistent and accurate.

MotionScale (temporal RoPE scale) ⚡ Usage Tips:

• Start with a latent_refresh value of 0.5 for projects involving multiple segments to ensure balanced correction without over-adjustment.
• Use delta_max values between 0.5 and 1.0 to prevent excessive corrections that could lead to instability in large motion segments.
• Enable diagnostic logging to monitor the effectiveness of the DC drift correction and make adjustments as necessary.

MotionScale (temporal RoPE scale) ⚡ Common Errors and Solutions:

"DC drift correction not applied"

• Explanation: This error occurs when the latent_refresh parameter is set to 0, disabling the drift correction.
• Solution: Increase the latent_refresh value to enable drift correction, starting with a value of 0.5 for optimal results.

"Over-correction detected in motion segments"

• Explanation: This error indicates that the delta_max value is too high, leading to excessive correction.
• Solution: Reduce the delta_max value to a range between 0.5 and 1.0 to prevent over-correction and maintain stability.