KSampler - ComfyUI Built-in Node Documentation

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

Output

Parameter	Function
Latent	Outputs the latent after sampler denoising

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)

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​Inputs

​Output

​Source Code

Inputs

Output

Source Code