ComfyUI Node: RestartSampler

RestartSampler:

The RestartSampler node is designed to enhance the sampling process by allowing for restarts, which can improve the quality and consistency of generated images. This node is particularly useful in scenarios where the sampling process needs to be broken down into chunks or when specific custom sampling techniques are required. By integrating the RestartSampler into your workflow, you can leverage advanced sampling strategies that can lead to more refined and controlled outputs. The main goal of this node is to provide flexibility and robustness in the sampling process, ensuring that you can achieve the desired results with greater precision.

RestartSampler Input Parameters:

sampler

The sampler parameter specifies the sampling method to be used. This can be any predefined or custom sampler that you want to apply to your image generation process. The choice of sampler can significantly impact the style and quality of the output, as different samplers have unique characteristics and behaviors. There are no specific minimum or maximum values for this parameter, but it must be a valid sampler object.

chunked_mode

The chunked_mode parameter is a boolean that determines whether the sampling process should be executed in chunks. When set to True, the sampling process is divided into smaller segments, which can help manage memory usage and improve performance for large or complex images. The default value for this parameter is True. Setting it to False will execute the sampling process in a single pass, which might be faster but could also be more resource-intensive.

RestartSampler Output Parameters:

SAMPLER

The output of the RestartSampler node is a modified sampler object that incorporates the restart capabilities. This enhanced sampler can be used in subsequent steps of your image generation process, providing the benefits of the restart functionality. The output sampler retains all the properties of the original sampler but with added options for chunked execution and custom noise handling, making it a powerful tool for achieving high-quality results.

RestartSampler Usage Tips:

• To optimize performance for large images, enable the chunked_mode parameter to manage memory usage effectively.
• Experiment with different samplers to find the one that best suits your artistic style and desired output quality.
• Use the RestartSampler in combination with other nodes to create complex and refined image generation workflows.

RestartSampler Common Errors and Solutions:

RestartSampler: missing restart_sampler option!

• Explanation: This error occurs when the restart_wrapped_sampler option is not provided or is invalid.
• Solution: Ensure that you pass a valid sampler object to the sampler parameter. Double-check that the sampler is correctly defined and accessible in your workflow.

ValueError: Invalid chunked_mode value

• Explanation: This error indicates that the chunked_mode parameter has been set to an invalid value.
• Solution: Verify that the chunked_mode parameter is set to either True or False. Correct any typos or incorrect values in your configuration.

TypeError: sampler_function() missing required positional argument

• Explanation: This error suggests that the sampler_function is missing one or more required arguments.
• Solution: Ensure that all necessary arguments are provided when calling the sampler_function. Check the function signature and provide all required parameters.

MemoryError: Unable to allocate memory for sampling

• Explanation: This error occurs when the system runs out of memory during the sampling process.
• Solution: Enable chunked_mode to break the sampling process into smaller, more manageable chunks. Additionally, consider reducing the image size or complexity to fit within available memory resources.