> ## Documentation Index
> Fetch the complete documentation index at: https://docs.comfy.org/llms.txt
> Use this file to discover all available pages before exploring further.

# KSampler - ComfyUI Built-in Node Documentation

> Complete documentation for the KSampler node in ComfyUI. Learn its inputs, outputs, parameters and usage.

The KSampler works like this: it modifies the provided original latent image information based on a specific model and both positive and negative conditions.
First, it adds noise to the original image data according to the set **seed** and **denoise strength**, then inputs the preset **Model** combined with **positive** and **negative** guidance conditions to generate the image.

## Inputs

| Parameter Name           | Data Type    | Required | Default | Range/Options             | Description                                                                        |
| ------------------------ | ------------ | -------- | ------- | ------------------------- | ---------------------------------------------------------------------------------- |
| Model                    | checkpoint   | Yes      | None    | -                         | Input model used for the denoising process                                         |
| seed                     | Int          | Yes      | 0       | 0 \~ 18446744073709551615 | Used to generate random noise, using the same "seed" generates identical images    |
| steps                    | Int          | Yes      | 20      | 1 \~ 10000                | Number of steps to use in denoising process, more steps mean more accurate results |
| cfg                      | float        | Yes      | 8.0     | 0.0 \~ 100.0              | Controls how closely the generated image matches input conditions, 6-8 recommended |
| sampler\_name            | UI Option    | Yes      | None    | Multiple algorithms       | Choose sampler for denoising, affects generation speed and style                   |
| scheduler                | UI Option    | Yes      | None    | Multiple schedulers       | Controls how noise is removed, affects generation process                          |
| Positive                 | conditioning | Yes      | None    | -                         | Positive conditions guiding denoising, what you want to appear in the image        |
| Negative                 | conditioning | Yes      | None    | -                         | Negative conditions guiding denoising, what you don't want in the image            |
| Latent\_Image            | Latent       | Yes      | None    | -                         | Latent image used for denoising                                                    |
| denoise                  | float        | No       | 1.0     | 0.0 \~ 1.0                | Determines noise removal ratio, lower values mean less connection to input image   |
| control\_after\_generate | UI Option    | No       | None    | Random/Inc/Dec/Keep       | Provides ability to change seed after each prompt                                  |

## Output

| Parameter | Function                                   |
| --------- | ------------------------------------------ |
| Latent    | Outputs the latent after sampler denoising |

## Source Code

\[Updated on May 15, 2025]

```Python theme={null}

def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
    latent_image = latent["samples"]
    latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)

    if disable_noise:
        noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
    else:
        batch_inds = latent["batch_index"] if "batch_index" in latent else None
        noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds)

    noise_mask = None
    if "noise_mask" in latent:
        noise_mask = latent["noise_mask"]

    callback = latent_preview.prepare_callback(model, steps)
    disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
    samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image,
                                  denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step,
                                  force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
    out = latent.copy()
    out["samples"] = samples
    return (out, )
class KSampler:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "model": ("MODEL", {"tooltip": "The model used for denoising the input latent."}),
                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True, "tooltip": "The random seed used for creating the noise."}),
                "steps": ("INT", {"default": 20, "min": 1, "max": 10000, "tooltip": "The number of steps used in the denoising process."}),
                "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01, "tooltip": "The Classifier-Free Guidance scale balances creativity and adherence to the prompt. Higher values result in images more closely matching the prompt however too high values will negatively impact quality."}),
                "sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"tooltip": "The algorithm used when sampling, this can affect the quality, speed, and style of the generated output."}),
                "scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"tooltip": "The scheduler controls how noise is gradually removed to form the image."}),
                "positive": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to include in the image."}),
                "negative": ("CONDITIONING", {"tooltip": "The conditioning describing the attributes you want to exclude from the image."}),
                "latent_image": ("LATENT", {"tooltip": "The latent image to denoise."}),
                "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The amount of denoising applied, lower values will maintain the structure of the initial image allowing for image to image sampling."}),
            }
        }

    RETURN_TYPES = ("LATENT",)
    OUTPUT_TOOLTIPS = ("The denoised latent.",)
    FUNCTION = "sample"

    CATEGORY = "sampling"
    DESCRIPTION = "Uses the provided model, positive and negative conditioning to denoise the latent image."

    def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0):
        return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)

```