ComfyUI Node: HY-Motion Sampler

HY-Motion Sampler:

The HYMotionSampler is a sophisticated node designed to generate motion data by sampling from a given model. It is particularly useful for AI artists who want to create dynamic and realistic motion sequences. The node leverages advanced techniques to produce motion data that can be used in various applications, such as animation and virtual reality. By focusing on generating motion through sampling, the HYMotionSampler provides a streamlined approach to creating complex motion patterns without requiring extensive manual input. This node is essential for those looking to enhance their projects with lifelike motion, offering a balance between automation and control.

HY-Motion Sampler Input Parameters:

dit_model

The dit_model parameter represents the model from which the motion data will be sampled. It is crucial for defining the characteristics and style of the generated motion. This parameter does not have a default value as it requires a specific model to function.

text_embeds

The text_embeds parameter allows you to input text embeddings that guide the motion generation process. This can be used to align the motion with specific textual descriptions or themes, enhancing the creative possibilities. There is no default value, as it depends on the user's input.

duration

The duration parameter specifies the length of the motion sequence to be generated, measured in seconds. It directly impacts the number of frames produced, with longer durations resulting in more extensive motion sequences. The minimum value is typically greater than zero, with no explicit maximum value provided.

seed

The seed parameter is used to initialize the random number generator, ensuring reproducibility of the motion sequences. By setting a specific seed, you can generate the same motion sequence across different runs. There is no default value, as it is user-defined.

cfg_scale

The cfg_scale parameter controls the scale of the configuration, affecting the intensity and variability of the generated motion. The default value is 5.0, with no specified minimum or maximum values.

num_samples

The num_samples parameter determines the number of motion samples to generate. This allows you to create multiple variations of motion sequences in a single run. The default value is 1, with no specified limits.

validation_steps

The validation_steps parameter defines the number of steps used for validation during the sampling process. This can influence the quality and accuracy of the generated motion. The default value is 50, with no specified range.

use_special_game_feat

The use_special_game_feat parameter is a boolean flag that, when enabled, incorporates special game features into the motion generation process. This can add unique characteristics to the motion. The default value is False.

special_game_prob

The special_game_prob parameter sets the probability of applying special game features when use_special_game_feat is enabled. It ranges from 0.0 to 1.0, with a default value of 1.0.

enable_ctxt_null_feat

The enable_ctxt_null_feat parameter is a boolean flag that, when enabled, allows the use of context null features in the motion generation process. This can affect the contextual alignment of the motion. The default value is True.

sampler_method

The sampler_method parameter specifies the method used for sampling, with "dopri5" as the default option. This choice can influence the efficiency and style of the motion generation.

atol

The atol parameter sets the absolute tolerance level for the sampling process, affecting the precision of the generated motion. The default value is 1e-4.

rtol

The rtol parameter defines the relative tolerance level for the sampling process, similar to atol, impacting the precision of the motion. The default value is 1e-4.

align_ground

The align_ground parameter is a boolean flag that, when enabled, aligns the generated motion with the ground plane. This ensures that the motion appears grounded and realistic. The default value is True.

ground_offset

The ground_offset parameter specifies the offset from the ground plane, allowing for adjustments in the vertical positioning of the motion. The default value is 0.0.

skip_smoothing

The skip_smoothing parameter is a boolean flag that, when enabled, skips the smoothing process for the generated motion. This can result in more raw and unrefined motion sequences. The default value is False.

body_chunk_size

The body_chunk_size parameter determines the size of the body chunks used in the motion generation process. This can affect the granularity and detail of the motion. The default value is 64.

first_frame

The first_frame parameter allows you to specify an initial frame for the motion sequence, providing a starting point for the generation process. It is optional and can be left undefined.

last_frame

The last_frame parameter allows you to specify a final frame for the motion sequence, providing an endpoint for the generation process. It is optional and can be left undefined.

latent

The latent parameter is an optional tensor that can be used to influence the latent space of the motion generation process. This can add variability and uniqueness to the motion. It is optional and can be left undefined.

denoise

The denoise parameter controls the level of denoising applied to the generated motion, affecting its smoothness and clarity. The default value is 1.0.

transition_frames

The transition_frames parameter specifies the number of frames used for transitions between different motion segments, ensuring smooth continuity. The default value is 5.

smoothing_sigma

The smoothing_sigma parameter sets the sigma value for the smoothing process, influencing the degree of smoothing applied to the motion. The default value is 1.0.

smoothing_window

The smoothing_window parameter defines the window size for the smoothing process, affecting the extent of smoothing applied to the motion. The default value is 11.

motion_data

The motion_data parameter is an optional dictionary that can be used to input existing motion data, allowing for further manipulation and refinement. It is optional and can be left undefined.

force_origin

The force_origin parameter is a boolean flag that, when enabled, forces the motion to originate from a specific point, ensuring consistency in the starting position. The default value is False.

momentum_guidance_scale

The momentum_guidance_scale parameter controls the scale of momentum guidance applied to the motion, affecting its flow and dynamics. The default value is 0.7.

HY-Motion Sampler Output Parameters:

motion_data_out

The motion_data_out parameter is the primary output of the HYMotionSampler, containing the generated motion data. This data can be used for various applications, such as animation and virtual reality, providing a detailed representation of the motion sequence. The output is crucial for visualizing and utilizing the generated motion in creative projects.

HY-Motion Sampler Usage Tips:

• Experiment with different dit_model and text_embeds combinations to achieve diverse motion styles and themes.
• Adjust the duration and num_samples parameters to create motion sequences of varying lengths and variations, allowing for greater creative flexibility.
• Utilize the first_frame and last_frame parameters to seamlessly integrate the generated motion with existing sequences, ensuring smooth transitions.

HY-Motion Sampler Common Errors and Solutions:

IndexError: list index out of range

• Explanation: This error may occur if the frame_index is set to a value outside the valid range of frames in the motion data.
• Solution: Ensure that the frame_index is within the bounds of the available frames, adjusting it as necessary to avoid negative or overly large values.

ValueError: Invalid configuration scale

• Explanation: This error can arise if the cfg_scale parameter is set to an inappropriate value that the system cannot handle.
• Solution: Verify that the cfg_scale is set to a reasonable value, typically around the default of 5.0, and adjust it if necessary to ensure compatibility with the model.