ComfyUI Node: Timestep Keyframe From List 🛂🅐🅒🅝

Timestep Keyframe From List 🛂🅐🅒🅝:

The ACN_TimestepKeyframeFromStrengthList node is designed to generate a sequence of keyframes based on specified strength values over a given range of timesteps. This node is particularly useful for creating smooth transitions and animations by interpolating between different strength values, allowing for precise control over the progression of effects or transformations. By leveraging this node, you can automate the creation of keyframes, ensuring consistent and predictable changes over time, which is essential for complex animations and effects in AI art projects.

Timestep Keyframe From List 🛂🅐🅒🅝 Input Parameters:

float_strengths

This parameter accepts either a single float value or a list of float values that represent the strength of each keyframe. If a single float is provided, it will be converted into a list with one element. The strengths determine the intensity or influence of each keyframe in the sequence. The values should be within the range of valid float numbers, and the default is -1 if not specified.

start_percent

This parameter defines the starting percentage of the timeline where the first keyframe will be placed. It is a float value that should be between 0 and 100, representing the start point of the keyframe sequence.

end_percent

This parameter sets the ending percentage of the timeline for the last keyframe. Similar to start_percent, it is a float value between 0 and 100, indicating the endpoint of the keyframe sequence.

null_latent_kf_strength

This optional float parameter specifies the strength of the null latent keyframe, which can be used to control the influence of keyframes that do not have a specified strength. The default value is 0.0.

cn_weights

This optional parameter allows you to provide control weights for the keyframes. It can be used to fine-tune the influence of each keyframe on the overall effect.

latent_keyframe

This optional parameter accepts a LatentKeyframeGroup object, which can be used to inherit or modify existing latent keyframes.

inherit_missing

This boolean parameter determines whether missing keyframe values should be inherited from previous keyframes. The default value is True.

guarantee_steps

This parameter ensures that a minimum number of steps are guaranteed for the keyframe sequence. It is an integer value, and the default is 0.

mask_optional

This optional parameter allows you to provide a mask hint for the keyframes, which can be used to control the application of effects.

print_keyframes

This boolean parameter, when set to True, enables the printing of keyframe information to the logger for debugging purposes. The default value is False.

Timestep Keyframe From List 🛂🅐🅒🅝 Output Parameters:

LATENT_KF

This output parameter returns a LatentKeyframeGroup object that contains the generated keyframes. Each keyframe in the group represents a specific point in the timeline with the corresponding strength and other attributes defined by the input parameters. This output is essential for further processing or applying the keyframes in your AI art project.

Timestep Keyframe From List 🛂🅐🅒🅝 Usage Tips:

• To create a smooth transition between keyframes, ensure that the float_strengths list contains values that gradually change from the start to the end of the sequence.
• Use the print_keyframes parameter to debug and verify the generated keyframes, especially when fine-tuning the strength values and other attributes.
• Experiment with different start_percent and end_percent values to control the duration and placement of the keyframe sequence within your timeline.

Timestep Keyframe From List 🛂🅐🅒🅝 Common Errors and Solutions:

ValueError: strengths_float must be either an iterable input or a float

• Explanation: This error occurs when the float_strengths parameter is neither a float nor an iterable.
• Solution: Ensure that float_strengths is provided as either a single float value or a list of float values.

ValueError: batch_index_from must be less than or equal to batch_index_to

• Explanation: This error indicates that the starting batch index is greater than the ending batch index.
• Solution: Verify that the batch_index_from parameter is less than or equal to the batch_index_to parameter.

ValueError: batch_index_from and batch_index_to must be either both positive or both negative

• Explanation: This error occurs when the batch indices have mixed signs (one positive and one negative).
• Solution: Ensure that both batch_index_from and batch_index_to are either positive or negative.

Exception: strengths_float must be either an iterable input or a float, but was <type>

• Explanation: This error occurs when the float_strengths parameter is of an unsupported type.
• Solution: Check the type of float_strengths and make sure it is either a float or an iterable (e.g., list of floats).