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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.

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Inputs

Parameter NameData TypeRequiredDefaultRange/OptionsDescription
ModelcheckpointYesNone-Input model used for the denoising process
seedIntYes00 ~ 18446744073709551615Used to generate random noise, using the same “seed” generates identical images
stepsIntYes201 ~ 10000Number of steps to use in denoising process, more steps mean more accurate results
cfgfloatYes8.00.0 ~ 100.0Controls how closely the generated image matches input conditions, 6-8 recommended
sampler_nameUI OptionYesNoneMultiple algorithmsChoose sampler for denoising, affects generation speed and style
schedulerUI OptionYesNoneMultiple schedulersControls how noise is removed, affects generation process
PositiveconditioningYesNone-Positive conditions guiding denoising, what you want to appear in the image
NegativeconditioningYesNone-Negative conditions guiding denoising, what you don’t want in the image
Latent_ImageLatentYesNone-Latent image used for denoising
denoisefloatNo1.00.0 ~ 1.0Determines noise removal ratio, lower values mean less connection to input image
control_after_generateUI OptionNoNoneRandom/Inc/Dec/KeepProvides ability to change seed after each prompt

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Output

ParameterFunction
LatentOutputs the latent after sampler denoising

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Source Code

[Updated on May 15, 2025] 

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)