ComfyUI Node: Image2Image Sampler v2 (Searge)

Image2Image Sampler v2 (Searge):

The SeargeSDXLImage2ImageSampler2 node is designed to facilitate the process of transforming an existing image into a new one using advanced sampling techniques. This node is particularly useful for AI artists who want to apply high-resolution fixes and other refinements to their images. By leveraging state-of-the-art models and algorithms, the node ensures that the output images are of high quality and meet the desired artistic standards. The primary goal of this node is to provide a seamless and efficient way to enhance images, making it an essential tool for anyone looking to improve their digital artwork.

Image2Image Sampler v2 (Searge) Input Parameters:

data

This parameter holds the initial data required for the sampling process. It is a dictionary that can include various elements such as the base model, positive and negative prompts, and other configurations. If not provided, the node will initialize it as an empty dictionary. This parameter is crucial as it forms the foundation upon which the sampling process is built.

sampler_input

This parameter is another dictionary that contains specific inputs for the sampler. It includes elements like the base model, positive and negative prompts, latent image, noise seed, steps, CFG (Classifier-Free Guidance), sampler name, scheduler, base ratio, denoise level, and dynamic CFG method. If not provided, the node will attempt to retrieve these parameters from the data dictionary. This parameter significantly impacts the quality and characteristics of the output image.

base_model

This parameter specifies the base model to be used for the sampling process. It is retrieved from the sampler_input dictionary. The base model serves as the primary framework for generating the new image, and its selection can greatly influence the final result.

base_positive

This parameter contains the positive prompts for the base model. Positive prompts guide the model towards desired features and characteristics in the output image. It is essential for achieving the intended artistic effect.

base_negative

This parameter contains the negative prompts for the base model. Negative prompts help the model avoid unwanted features and characteristics in the output image. It is useful for refining the image and ensuring it meets the desired standards.

refiner_model

This parameter specifies the refiner model to be used for additional enhancements. The refiner model works in conjunction with the base model to further improve the quality of the output image.

refiner_positive

This parameter contains the positive prompts for the refiner model. Similar to the base positive prompts, these guide the refiner model towards desired features in the output image.

refiner_negative

This parameter contains the negative prompts for the refiner model. These prompts help the refiner model avoid unwanted features, ensuring the final image is of high quality.

latent_image

This parameter holds the latent image data, which serves as the starting point for the sampling process. The latent image is transformed and refined to produce the final output.

noise_seed

This parameter specifies the seed for noise generation. It is used to introduce controlled randomness into the sampling process. The default value is 4815162342, but it can be adjusted to achieve different artistic effects.

steps

This parameter defines the number of steps to be taken during the sampling process. More steps generally result in higher quality images but require more computational resources. The default value is 25.

cfg

This parameter stands for Classifier-Free Guidance and controls the strength of the guidance applied during sampling. The default value is 7.0, but it can be adjusted to balance between creativity and adherence to the prompts.

sampler_name

This parameter specifies the name of the sampler to be used. The default value is "dpmpp_2m". Different samplers can produce varying artistic effects, so choosing the right one is important.

scheduler

This parameter defines the scheduler to be used during the sampling process. The default value is "karras". The scheduler influences the timing and sequence of the sampling steps.

base_ratio

This parameter specifies the ratio of the base model's influence in the final image. The default value is 0.8. Adjusting this ratio can help balance the contributions of the base and refiner models.

denoise

This parameter controls the level of denoising applied during the sampling process. The default value is 1.0. Denoising helps in reducing artifacts and improving the overall quality of the image.

cfg_method

This parameter specifies the method for dynamic Classifier-Free Guidance. It allows for more advanced control over the guidance process, enabling finer adjustments to the output image.

dynamic_base_cfg

This parameter sets the base value for dynamic Classifier-Free Guidance. The default value is 0.0. Adjusting this value can help achieve the desired balance between creativity and adherence to the prompts.

Image2Image Sampler v2 (Searge) Output Parameters:

data

This output parameter returns the modified data dictionary after the sampling process. It includes all the initial inputs along with any changes made during the sampling.

out

This output parameter contains the final sampled image. It is the primary result of the node's operation and represents the transformed and refined version of the input image.

out_denoised

This output parameter provides the denoised version of the final sampled image. It is useful for obtaining a cleaner and higher quality result, free from artifacts and noise.

Image2Image Sampler v2 (Searge) Usage Tips:

• Experiment with different base and refiner models to achieve unique artistic effects.
• Adjust the number of steps to balance between image quality and computational resources.
• Use positive and negative prompts effectively to guide the model towards desired features and away from unwanted ones.
• Fine-tune the Classifier-Free Guidance (CFG) value to control the strength of the guidance during sampling.

Image2Image Sampler v2 (Searge) Common Errors and Solutions:

"sampler_input is None"

• Explanation: This error occurs when the sampler_input parameter is not provided and cannot be retrieved from the data dictionary.
• Solution: Ensure that the sampler_input parameter is provided or that the necessary data is available in the data dictionary.

"base_model is None"

• Explanation: This error occurs when the base model is not specified in the sampler_input dictionary.
• Solution: Make sure to include the base model in the sampler_input dictionary.

"latent_image is None"

• Explanation: This error occurs when the latent image data is not provided in the sampler_input dictionary.
• Solution: Ensure that the latent image data is included in the sampler_input dictionary.

"Invalid noise seed"

• Explanation: This error occurs when the noise seed is not a valid integer.
• Solution: Verify that the noise seed is a valid integer and adjust it if necessary.

"Invalid steps value"

• Explanation: This error occurs when the number of steps is not within the acceptable range.
• Solution: Ensure that the steps value is within a reasonable range, typically between 1 and 100. Adjust the value accordingly.