KSampler

KSampler node

The KSampler uses the provided model and positive and negative conditioning to generate a new version of the given latent. First the latent is noised up according to the given seed and denoise strength, erasing some of the latent image. then this noise is removed using the given Model and the positive and negative conditioning as guidance, "dreaming" up new details in places where the image was erased by noise.

inputs

Model

The model used for denoising

Positive

The positive conditioning.

Negative

The negative conditioning.

latent_image

The latent that will be denoised.

seed

The random seed used in creating the noise.

control_after_generate

Provides the ability to change the seed number described above after each prompt. the node can randomize, increment, decrement or keep the seed number fixed.

steps

The number of steps to use during denoising. The more steps the sampler is allowed to make the more accurate the result will be. See the samplers page for good guidelines on how to pick an appropriate number of steps.

cfg

The classifier free guidance(cfg) scale determines how aggressive the sampler should be in realizing the content of the prompts in the final image. Higher scales force the image to better represent the prompt, but a scale that is set too high will negatively impact the quality of the image.

sampler_name

Which sampler to use, see the samplers page for more details on the available samplers.

scheduler

The type of schedule to use, see the samplers page for more details on the available schedules.

denoise

How much information of the latents should be erased by noise.

outputs

LATENT

the denoised latent.

example

The KSampler is the core of any workflow and can be used to perform text to image and image to image generation tasks. The example below shows how to use the KSampler in an image to image task, by connecting a model, a positive and negative embedding, and a latent image. Note that we use a denoise value of less than 1.0. This way parts of the original image are preserved when it is noised up, guiding the denoising process to similar looking images.