ComfyUI Node: TripleKSampler Advanced (Wan2.2-Lightning)

TripleKSampler Advanced (Wan2.2-Lightning):

The TripleKSamplerWan22LightningAdvanced node is a sophisticated tool designed for advanced AI art generation using the Wan2.2 split models with Lightning LoRA integration. This node implements a triple-stage sampling process that enhances the quality and detail of generated images. It operates through three distinct stages: base denoising, lightning high-model processing, and lightning low-model refinement. This approach allows for a more nuanced and refined output by leveraging different model strengths at each stage. The advanced node offers full parameter control, enabling you to fine-tune the sampling process to achieve the desired artistic effect. This flexibility makes it an invaluable tool for artists looking to push the boundaries of AI-generated art, providing a balance between automation and creative control.

TripleKSampler Advanced (Wan2.2-Lightning) Input Parameters:

base_high

This parameter represents the high-level model used during the base denoising stage. It influences the initial quality and detail of the generated image. Adjusting this can impact the overall texture and clarity of the output.

lightning_high

The high-level model used during the lightning processing stage. It enhances the image by adding finer details and improving the overall quality. This parameter is crucial for achieving a polished and professional look.

lightning_low

This parameter refers to the low-level model used during the lightning refinement stage. It is responsible for subtle adjustments and refinements, ensuring the final image is cohesive and well-balanced.

positive

This input is used for positive conditioning, guiding the model towards desired features or styles in the generated image. It helps in emphasizing certain aspects of the artwork.

negative

Negative conditioning input, which helps in suppressing unwanted features or styles in the generated image. It is useful for avoiding specific elements that may detract from the intended artistic vision.

latent_image

A dictionary containing the latent image tensor, which serves as the starting point for the sampling process. This parameter is essential for initializing the image generation process.

seed

An integer value used to initialize the random number generator, ensuring reproducibility of results. Different seeds will produce different variations of the generated image.

sigma_shift

A float value that adjusts the noise level during the sampling process. It can be used to control the amount of randomness and variation in the output.

base_steps

The number of steps to perform during the base denoising stage. More steps can lead to higher quality but may increase processing time.

base_quality_threshold

An integer that sets the quality threshold for the base stage. It determines the minimum acceptable quality level before proceeding to the next stage.

base_cfg

A float value representing the configuration for the base stage. It influences the strength and impact of the base model on the final output.

lightning_start

An integer indicating the starting step within the lightning schedule. Setting it to 0 skips the first stage entirely, allowing for more focused processing in subsequent stages.

lightning_steps

The number of steps to perform during the lightning stages. This parameter controls the duration and intensity of the lightning processing.

lightning_cfg

A float value for configuring the lightning stages. It affects how strongly the lightning models influence the final image.

sampler_name

The name of the sampler to be used during the process. Different samplers can produce varying artistic effects and styles.

scheduler

The scheduling strategy for the sampling process. It determines the order and timing of operations within the node.

switch_strategy

A string that specifies the strategy for switching between lightning high and low models. Options include "50% of steps", "T2V boundary", and "I2V boundary", each offering different transition dynamics.

switch_boundary

A float value that sets the boundary for model switching. It defines the point at which the node transitions between different models.

switch_step

An integer that specifies the exact step for switching models. A value of -1 indicates automatic switching based on the chosen strategy.

dry_run

A boolean flag indicating whether to perform a dry run. When enabled, calculations are performed without executing the sampling, useful for testing configurations.

TripleKSampler Advanced (Wan2.2-Lightning) Output Parameters:

latent_image

The output is a dictionary containing the final latent image tensor. This tensor represents the completed image after all stages of processing, ready for further refinement or display. It encapsulates the cumulative effects of the base, lightning high, and lightning low models, providing a high-quality and artistically refined result.

TripleKSampler Advanced (Wan2.2-Lightning) Usage Tips:

• Experiment with different seeds to explore a variety of artistic outcomes from the same initial conditions.
• Use the positive and negative conditioning inputs to guide the model towards or away from specific styles or features, allowing for more targeted artistic expression.
• Adjust the lightning_steps and lightning_cfg parameters to fine-tune the level of detail and refinement in the final image, balancing processing time with quality.

TripleKSampler Advanced (Wan2.2-Lightning) Common Errors and Solutions:

"Model not found"

• Explanation: This error occurs when the specified model for one of the stages is not available or incorrectly specified.
• Solution: Ensure that all model paths are correctly set and that the required models are installed and accessible.

"Invalid parameter value"

• Explanation: This error indicates that one or more input parameters have values outside the acceptable range.
• Solution: Double-check the parameter values against their specified ranges and adjust them accordingly to fall within valid limits.

"Sampling process failed"

• Explanation: This error can occur if there is an issue during the sampling stages, possibly due to incompatible configurations or resource limitations.
• Solution: Review the configuration settings for compatibility and ensure that your system meets the necessary resource requirements for the node's operation.