ComfyUI Node: Create Sigma Schedule 🎭🅐🅓

Create Sigma Schedule 🎭🅐🅓:

The ADE_SigmaSchedule node is designed to manage and manipulate sigma schedules within the Animate Diff framework. Sigma schedules are crucial for controlling the noise levels during the diffusion process in generative models, which directly impacts the quality and characteristics of the generated images. This node allows you to create, combine, and interpolate sigma schedules, providing flexibility and control over the sampling process. By leveraging different beta schedules and interpolation methods, you can fine-tune the diffusion process to achieve desired artistic effects and improve the overall quality of the generated content.

Create Sigma Schedule 🎭🅐🅓 Input Parameters:

beta_schedule

The beta_schedule parameter specifies the beta schedule to be used for generating the sigma schedule. Beta schedules define the variance of noise added at each step of the diffusion process. This parameter accepts a predefined list of beta schedules, allowing you to choose the one that best fits your needs. The choice of beta schedule can significantly impact the noise characteristics and, consequently, the quality of the generated images.

schedule_A

The schedule_A parameter is one of the sigma schedules to be combined or interpolated. It represents a set of noise levels used during the diffusion process. This parameter is essential when you want to blend or interpolate between two different sigma schedules to achieve a specific effect.

schedule_B

The schedule_B parameter is the second sigma schedule to be combined or interpolated with schedule_A. Like schedule_A, it represents a set of noise levels used during the diffusion process. This parameter is used in conjunction with schedule_A to create a new sigma schedule that blends the characteristics of both input schedules.

weight_A

The weight_A parameter determines the weight of schedule_A when combining it with schedule_B. It is a float value ranging from 0.0 to 1.0, with a default value of 0.5. This parameter allows you to control the influence of each sigma schedule in the resulting combination, enabling fine-tuning of the noise characteristics.

weight_A_Start

The weight_A_Start parameter specifies the starting weight of schedule_A for interpolation. It is a float value ranging from 0.0 to 1.0, with a default value of 0.5. This parameter is used when interpolating between two sigma schedules over a range of weights, allowing for gradual transitions between the schedules.

weight_A_End

The weight_A_End parameter specifies the ending weight of schedule_A for interpolation. It is a float value ranging from 0.0 to 1.0, with a default value of 0.5. This parameter, along with weight_A_Start, defines the range of weights for interpolation, enabling smooth transitions between sigma schedules.

interpolation

The interpolation parameter defines the method used for interpolating between schedule_A and schedule_B. It accepts a predefined list of interpolation methods, allowing you to choose the one that best fits your needs. The choice of interpolation method can affect the smoothness and characteristics of the transition between sigma schedules.

raw_beta_schedule

The raw_beta_schedule parameter specifies the raw beta schedule to be used for generating the sigma schedule. It accepts a list of raw beta schedules, allowing you to choose the one that best fits your needs. This parameter is essential for creating custom sigma schedules based on specific beta schedules.

linear_start

The linear_start parameter defines the starting value for linear beta schedules. It is a float value with a default of 0.00085, and it can range from 0.0 to 1.0. This parameter is used to control the initial noise level in the diffusion process, impacting the overall quality of the generated images.

linear_end

The linear_end parameter defines the ending value for linear beta schedules. It is a float value with a default of 0.012, and it can range from 0.0 to 1.0. This parameter is used to control the final noise level in the diffusion process, impacting the overall quality of the generated images.

sampling

The sampling parameter specifies the sampling method to be used for generating the sigma schedule. It accepts a list of sampling methods, allowing you to choose the one that best fits your needs. The choice of sampling method can affect the noise characteristics and the quality of the generated images.

lcm_original_timesteps

The lcm_original_timesteps parameter defines the number of original timesteps for LCM sampling. It is an integer value with a default of 50, and it can range from 1 to 1000. This parameter is used to control the number of timesteps in the diffusion process, impacting the overall quality of the generated images.

lcm_zsnr

The lcm_zsnr parameter is a boolean value that specifies whether to apply ZSNR (Zero-Shot Noise Reduction) during LCM sampling. It has a default value of False. This parameter is used to control the application of noise reduction techniques, impacting the overall quality of the generated images.

Create Sigma Schedule 🎭🅐🅓 Output Parameters:

SIGMA_SCHEDULE

The SIGMA_SCHEDULE output parameter represents the resulting sigma schedule generated by the node. This schedule defines the noise levels used during the diffusion process, directly impacting the quality and characteristics of the generated images. The output sigma schedule can be used in subsequent nodes to control the diffusion process and achieve desired artistic effects.

Create Sigma Schedule 🎭🅐🅓 Usage Tips:

• Experiment with different beta schedules to find the one that best fits your artistic needs.
• Use the weight parameters to fine-tune the influence of each sigma schedule when combining or interpolating.
• Try different interpolation methods to achieve smooth transitions between sigma schedules.
• Adjust the linear start and end values to control the initial and final noise levels in the diffusion process.
• Utilize the LCM sampling parameters to control the number of timesteps and apply noise reduction techniques for improved image quality.

Create Sigma Schedule 🎭🅐🅓 Common Errors and Solutions:

Weighted Average cannot be taken of Sigma Schedules that do not have the same amount of sigmas

• Explanation: This error occurs when attempting to combine or interpolate sigma schedules with different numbers of sigmas.
• Solution: Ensure that the sigma schedules being combined or interpolated have the same number of sigmas.

Invalid beta schedule

• Explanation: This error occurs when an invalid beta schedule is provided as input.
• Solution: Verify that the beta schedule provided is from the predefined list of valid beta schedules.

Invalid interpolation method

• Explanation: This error occurs when an invalid interpolation method is provided as input.
• Solution: Verify that the interpolation method provided is from the predefined list of valid interpolation methods.

Invalid sampling method

• Explanation: This error occurs when an invalid sampling method is provided as input.
• Solution: Verify that the sampling method provided is from the predefined list of valid sampling methods.