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KSampler K采样器 |ComfyUI组件节点

comfyUI节点-KSampler K采样器 |ComfyUI组件节点

文档说明

  • 类名:KSampler
  • 类别:采样
  • 输出节点: KSampler这个采样器,是这样工作的:它会根据提供的特定的模型和正、负两种条件,来改造提供的原始潜在图像信息。 首先,它会根据设定好的seed随机种子denoise降噪强度,给原始图像数据加入一些噪声,然后输入预设的Model模型结合positive正向negative负向的引导条件,去生成图像

Input 输入

参数名称数据类型必填默认值取值范围/选项说明
Model模型checkpoint模型-输入用于降噪过程的模型
seed随机种子Int整数00 ~ 18446744073709551615用于生成随机噪声,使用同样的“种子”可以生成相同的画面
steps步数Int整数201 ~ 10000去噪过程中要使用的步骤数,步数越多,结果越准确
cfgfloat浮点数8.00.0 ~ 100.0控制生成的图像与输入条件的贴合程度,通常建议6-8
sampler_name采样器界面选项多种采样算法选择用来降噪的采样器,不同采样器影响生成速度和风格
scheduler调度器界面选项多种调度器控制噪声去除的方式,不同调度器会影响生成过程
Positive正向条件conditioning条件-用于引导降噪的正向条件,可理解为想要在画面中出现的内容
Negative负向条件conditioning条件-用于引导降噪的负向条件,可理解为不想要在画面中出现的内容
Latent_ImageLatent-用于降噪的潜像
denoise降噪float浮点数1.00.0 ~ 1.0决定去除多少比例的噪声,值越小生成图像与输入图像关联越小,值越大越像输入图像
control_after_generate界面选项随机/增量/减量/保持提供在每次提示后更改种子数的能力,节点可以随机、增量、减量或保持种子数不变

Output 输出

参数名称作用
Latent输出经过采样器降噪后的潜像

源码

[更新于2025年5月15日]


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)