ComfyUI Node: MimicMotion Sampler

MimicMotion Sampler:

The MimicMotionSampler node is designed to facilitate the generation of motion sequences by sampling from a pre-trained MimicMotion model. This node is particularly useful for AI artists who want to create dynamic and realistic motion sequences from static images or poses. By leveraging advanced machine learning techniques, the MimicMotionSampler can generate high-quality motion frames that seamlessly transition from one pose to another, providing a smooth and natural animation. This node is essential for tasks that require the synthesis of motion, such as animation, video generation, and other creative projects that involve dynamic visual content.

MimicMotion Sampler Input Parameters:

mimic_pipeline

The mimic_pipeline parameter is a dictionary containing the pre-trained MimicMotion model pipeline. This pipeline includes various components such as the VAE (Variational Autoencoder), image encoder, UNet, scheduler, feature extractor, and pose network. These components work together to generate motion frames from the input poses or images. The pipeline is essential for the node's operation as it defines the model and its configuration.

ref_image

The ref_image parameter is the reference image from which the motion sequence will be generated. This image serves as the starting point for the animation and provides the initial visual context for the motion frames. The quality and content of the reference image significantly impact the resulting motion sequence.

pose_images

The pose_images parameter consists of a series of images that represent the desired poses for the motion sequence. These images guide the motion generation process, ensuring that the resulting frames follow the specified poses. The number and quality of pose images can affect the smoothness and accuracy of the generated motion.

cfg_min

The cfg_min parameter sets the minimum guidance scale for the motion generation process. This value influences the strength of the guidance applied to the model during sampling, affecting the adherence to the input poses. The minimum value is 0, and the default value is typically set to a low number to allow some flexibility in the generated motion.

cfg_max

The cfg_max parameter sets the maximum guidance scale for the motion generation process. This value determines the upper limit of the guidance strength, ensuring that the generated frames closely follow the input poses. The maximum value is typically set to a higher number to enforce strict adherence to the poses.

steps

The steps parameter defines the number of inference steps used during the motion generation process. More steps generally result in higher quality and more detailed motion sequences, but they also increase the computational time. The default value is usually set to balance quality and performance.

seed

The seed parameter is used to initialize the random number generator for the motion generation process. Setting a specific seed ensures reproducibility, allowing you to generate the same motion sequence multiple times. If not specified, a random seed is used.

noise_aug_strength

The noise_aug_strength parameter controls the strength of noise augmentation applied during the motion generation process. This value affects the variability and diversity of the generated frames, with higher values introducing more randomness. The default value is typically set to a moderate level to balance consistency and diversity.

fps

The fps parameter specifies the frames per second for the generated motion sequence. This value determines the playback speed of the animation, with higher values resulting in smoother and faster motion. The default value is usually set to a standard frame rate, such as 24 or 30 fps.

keep_model_loaded

The keep_model_loaded parameter is a boolean flag that determines whether the model should remain loaded in memory after the motion generation process. Setting this flag to True can save time if you plan to generate multiple sequences, while setting it to False can free up memory resources.

context_size

The context_size parameter defines the size of the context window used during the motion generation process. This value affects the amount of information considered from the input poses, with larger values providing more context. The default value is typically set to a moderate size to balance context and performance.

context_overlap

The context_overlap parameter specifies the amount of overlap between consecutive context windows during the motion generation process. This value affects the smoothness of the transitions between frames, with higher values resulting in smoother motion. The default value is usually set to a moderate level to balance smoothness and performance.

optional_scheduler

The optional_scheduler parameter allows you to specify a custom scheduler for the motion generation process. This scheduler can be used to control the sampling process, providing more flexibility and customization. If not specified, the default scheduler is used.

pose_strength

The pose_strength parameter controls the influence of the input poses on the generated motion sequence. Higher values result in frames that closely follow the input poses, while lower values allow for more flexibility and creativity. The default value is typically set to 1.0 to ensure accurate pose adherence.

image_embed_strength

The image_embed_strength parameter determines the influence of the reference image on the generated motion sequence. Higher values result in frames that closely resemble the reference image, while lower values allow for more variation. The default value is usually set to 1.0 to ensure consistency with the reference image.

pose_start_percent

The pose_start_percent parameter specifies the starting point of the pose influence in the motion sequence, as a percentage of the total sequence length. This value allows you to control when the input poses begin to affect the generated frames. The default value is typically set to 0.0 to start from the beginning.

pose_end_percent

The pose_end_percent parameter specifies the ending point of the pose influence in the motion sequence, as a percentage of the total sequence length. This value allows you to control when the input poses stop affecting the generated frames. The default value is usually set to 1.0 to continue until the end.

MimicMotion Sampler Output Parameters:

samples

The samples parameter is the output of the MimicMotionSampler node, containing the generated motion frames. These frames represent the motion sequence created based on the input reference image and poses. The output is typically in a latent format, which can be further processed or decoded into visual frames. The quality and accuracy of the samples depend on the input parameters and the configuration of the MimicMotion model.

MimicMotion Sampler Usage Tips:

• Ensure that the reference image and pose images are of high quality and relevant to the desired motion sequence to achieve the best results.
• Experiment with different values for cfg_min and cfg_max to find the optimal guidance scale for your specific use case.
• Use a consistent seed value if you need to generate the same motion sequence multiple times for comparison or refinement.
• Adjust the steps parameter to balance the quality and computational time of the motion generation process.
• Consider setting keep_model_loaded to True if you plan to generate multiple sequences in a single session to save time.

MimicMotion Sampler Common Errors and Solutions:

"Pipeline component not found"

• Explanation: This error occurs when one or more components of the MimicMotion pipeline are missing or not properly loaded.
• Solution: Ensure that all required components (VAE, image encoder, UNet, scheduler, feature extractor, pose network) are correctly loaded and included in the mimic_pipeline parameter.

"Invalid input shape for pose images"

• Explanation: This error occurs when the shape of the input pose images does not match the expected format.
• Solution: Verify that the pose images are correctly formatted and match the expected dimensions required by the MimicMotion model.

"Out of memory"

• Explanation: This error occurs when the system runs out of memory during the motion generation process.
• Solution: Reduce the size of the input images, decrease the number of inference steps, or set keep_model_loaded to False to free up memory resources.

"Invalid guidance scale values"

• Explanation: This error occurs when the cfg_min or cfg_max values are outside the acceptable range.
• Solution: Ensure that the cfg_min and cfg_max values are within the valid range and adjust them as needed.

"Scheduler not found"

• Explanation: This error occurs when the specified scheduler is not recognized or not properly loaded.
• Solution: Verify that the optional_scheduler parameter is correctly specified and that the scheduler is available in the system. If not, use the default scheduler provided by the MimicMotion model.