ComfyUI Node: pipeKSamplerAdvanced v1 (Legacy)

pipeKSamplerAdvanced v1 (Legacy):

The ttN pipeKSamplerAdvanced node is designed to provide advanced sampling capabilities for AI-generated images, leveraging the power of K-Sampling techniques. This node allows you to fine-tune various parameters to achieve high-quality and customized outputs. It supports the integration of LoRA (Low-Rank Adaptation) models, noise addition, and upscaling methods, making it a versatile tool for AI artists looking to enhance their creative workflows. The node's primary goal is to offer a flexible and powerful sampling process that can be tailored to specific artistic needs, ensuring that the generated images meet the desired aesthetic and technical standards.

pipeKSamplerAdvanced v1 (Legacy) Input Parameters:

pipe

The pipe parameter represents the pipeline configuration used for the sampling process. It includes various settings and models required for generating the output images. This parameter is essential for defining the overall structure and behavior of the sampling process.

lora_name

The lora_name parameter specifies the name of the LoRA model to be used. LoRA models help in adapting the base model to specific tasks or styles. If set to None, no LoRA model will be applied. This parameter allows you to customize the output by incorporating specialized models.

lora_strength

The lora_strength parameter controls the strength of the LoRA model's influence on the sampling process. It typically ranges from 0.0 to 1.0, where 0.0 means no influence and 1.0 means full influence. Adjusting this parameter helps in fine-tuning the output to achieve the desired effect.

add_noise

The add_noise parameter determines whether noise should be added to the sampling process. It can be set to enable or disable. Adding noise can help in generating more diverse and creative outputs, while disabling it can produce cleaner images.

steps

The steps parameter defines the number of sampling steps to be performed. Higher values generally result in better quality images but require more computational resources. This parameter allows you to balance between quality and performance.

cfg

The cfg parameter stands for Configuration and controls various settings related to the sampling process. It includes options like the number of iterations, learning rate, and other hyperparameters that influence the output quality and style.

sampler_name

The sampler_name parameter specifies the name of the sampler to be used. Different samplers have unique characteristics and can produce varying results. This parameter allows you to choose the most suitable sampler for your artistic needs.

scheduler

The scheduler parameter defines the scheduling strategy for the sampling process. It helps in managing the computational resources and optimizing the sampling steps to achieve the best possible results.

image_output

The image_output parameter determines how the generated images will be handled. Options include displaying the images, saving them to disk, or both. This parameter provides flexibility in managing the output based on your workflow requirements.

save_prefix

The save_prefix parameter specifies the prefix to be used when saving the generated images. It helps in organizing and identifying the output files, especially when generating multiple images in a single session.

file_type

The file_type parameter defines the format in which the generated images will be saved. Common options include PNG and JPEG. This parameter allows you to choose the most suitable format based on your needs for quality and file size.

embed_workflow

The embed_workflow parameter determines whether the workflow settings should be embedded in the output images. This can be useful for documentation and reproducibility purposes, ensuring that the settings used to generate the images are preserved.

noise

The noise parameter specifies the type and amount of noise to be added to the sampling process. It can help in creating more diverse and interesting outputs by introducing randomness into the generation process.

noise_seed

The noise_seed parameter sets the seed for the noise generation process. Using a fixed seed ensures that the noise pattern is reproducible, allowing you to generate the same output multiple times. If set to None, a random seed will be used.

optional_model

The optional_model parameter allows you to specify an additional model to be used in the sampling process. This can be useful for combining multiple models to achieve unique and complex outputs.

optional_positive

The optional_positive parameter provides additional positive embeddings to be used in the sampling process. These embeddings can help in guiding the generation towards specific features or styles.

optional_negative

The optional_negative parameter provides additional negative embeddings to be used in the sampling process. These embeddings can help in avoiding certain features or styles in the generated output.

optional_latent

The optional_latent parameter allows you to specify a latent representation to be used in the sampling process. This can be useful for initializing the generation with a specific latent code.

optional_vae

The optional_vae parameter specifies an additional Variational Autoencoder (VAE) model to be used in the sampling process. VAEs can help in improving the quality and diversity of the generated images.

optional_clip

The optional_clip parameter allows you to specify an additional CLIP model to be used in the sampling process. CLIP models can help in aligning the generated images with specific textual descriptions or styles.

input_image_override

The input_image_override parameter allows you to provide an input image that will override the default input. This can be useful for tasks like image-to-image translation or style transfer.

adv_xyPlot

The adv_xyPlot parameter enables advanced XY plotting capabilities. This can be useful for visualizing the sampling process and understanding how different parameters affect the output.

upscale_method

The upscale_method parameter specifies the method to be used for upscaling the generated images. Common options include nearest-neighbor, bilinear, and bicubic interpolation. This parameter allows you to enhance the resolution of the output images.

upscale_model_name

The upscale_model_name parameter specifies the name of the model to be used for upscaling. Different models have unique characteristics and can produce varying results. This parameter allows you to choose the most suitable model for your needs.

factor

The factor parameter defines the scaling factor for upscaling the generated images. Higher values result in larger images but require more computational resources. This parameter allows you to balance between resolution and performance.

rescale

The rescale parameter determines whether the generated images should be rescaled to a specific size. This can be useful for ensuring that the output images meet certain size requirements.

percent

The percent parameter specifies the percentage by which the generated images should be scaled. This can be useful for fine-tuning the size of the output images.

width

The width parameter defines the width of the generated images. This parameter allows you to specify the exact dimensions of the output.

height

The height parameter defines the height of the generated images. This parameter allows you to specify the exact dimensions of the output.

longer_side

The longer_side parameter specifies the length of the longer side of the generated images. This can be useful for maintaining the aspect ratio while resizing the images.

crop

The crop parameter determines whether the generated images should be cropped to a specific size. This can be useful for focusing on certain parts of the image.

prompt

The prompt parameter provides a textual description that guides the sampling process. This can be useful for generating images that align with specific themes or styles.

extra_pnginfo

The extra_pnginfo parameter allows you to embed additional information in the PNG metadata. This can be useful for documentation and reproducibility purposes.

my_unique_id

The my_unique_id parameter provides a unique identifier for the sampling process. This can be useful for tracking and managing multiple sampling sessions.

start_at_step

The start_at_step parameter specifies the step at which the sampling process should start. This can be useful for resuming interrupted sessions or fine-tuning specific parts of the generation process.

end_at_step

The end_at_step parameter specifies the step at which the sampling process should end. This can be useful for controlling the duration and complexity of the generation process.

return_with_leftover_noise

The return_with_leftover_noise parameter determines whether the output should include any leftover noise. This can be useful for certain artistic effects or for further processing.

pipeKSamplerAdvanced v1 (Legacy) Output Parameters:

images

The images parameter provides the generated images as the output of the sampling process. These images are the final result of the node's execution and can be used for various artistic and creative purposes.

latent

The latent parameter provides the latent representation used in the sampling process. This can be useful for further processing or for generating additional variations of the output images.

pipe_line

The pipe_line parameter provides the updated pipeline configuration after the sampling process. This can be useful for tracking the changes made during the generation and for reusing the configuration in future sessions.

results

The results parameter provides a summary of the sampling process, including the generated images, latent representations, and any additional information. This can be useful for documentation and analysis purposes.

pipeKSamplerAdvanced v1 (Legacy) Usage Tips:

• Experiment with different lora_strength values to find the optimal balance between the base model and the LoRA model's influence on the output.
• Use the add_noise parameter to introduce randomness and creativity into the generated images, but disable it if you need cleaner and more precise outputs.
• Adjust the steps parameter to balance between image quality and computational resources. Higher steps generally produce better results but require more time and processing power.
• Utilize the upscale_method and upscale_model_name parameters to enhance the resolution of your images, especially if you need high-quality outputs for printing or detailed analysis.

pipeKSamplerAdvanced v1 (Legacy) Common Errors and Solutions:

"Invalid LoRA model name"

• Explanation: The specified lora_name does not correspond to a valid LoRA model.
• Solution: Ensure that the lora_name parameter is set to a valid and existing LoRA model name. Check for any typos or incorrect names.

"Noise seed must be an integer"

• Explanation: The noise_seed parameter is not set to an integer value.
• Solution: Ensure that the noise_seed parameter is set to a valid integer. If you want to use a random seed, set the parameter to None.

"Invalid image output option"

• Explanation: The image_output parameter is set to an unsupported option.
• Solution: Ensure that the image_output parameter is set to one of the supported options, such as Display, Save, or Both.

"Upscale model not found"

• Explanation: The specified upscale_model_name does not correspond to a valid model.
• Solution: Ensure that the upscale_model_name parameter is set to a valid and existing model name. Check for any typos or incorrect names.