KSampler | ComfyUI Component Node
Documentation
- Class Name:
KSampler
- Category:
Sampling
- Output Node:
No
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.
Input Parameters
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 Name | 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)