> ## Documentation Index
> Fetch the complete documentation index at: https://dripart-docs-recommend-assets-api.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Ksampler - ComfyUI 原生节点文档
> Ksampler 节点是 ComfyUI 中常用的采样节点。
KSampler 节点用于对潜在图像（latent image）进行多步去噪采样。它结合正向和负向条件（prompt），通过指定的采样算法和调度器，生成高质量的潜在图像。常用于文生图、图生图等 AI 图像生成流程中。
## 参数说明
### 输入参数
| 参数名 | 类型 | 是否必填 | 默认值 | 说明 |
| ------------- | ------------ | ---- | --- | ---------------------------------------------- |
| model | MODEL | 是 | 无 | 用于去噪的模型（如 Stable Diffusion 模型） |
| seed | INT | 是 | 0 | 随机种子，保证生成结果可复现 |
| steps | INT | 是 | 20 | 去噪步数，步数越多图像越精细，速度越慢 |
| cfg | FLOAT | 是 | 8.0 | Classifier-Free Guidance 系数，数值越高越贴合提示词，过高会影响质量 |
| sampler\_name | 枚举 | 是 | 无 | 采样算法名称，影响生成速度、风格和质量 |
| scheduler | 枚举 | 是 | 无 | 调度器，控制噪声去除的过程 |
| positive | CONDITIONING | 是 | 无 | 正向条件，描述希望图像包含的内容 |
| negative | CONDITIONING | 是 | 无 | 负向条件，描述希望图像排除的内容 |
| latent\_image | LATENT | 是 | 无 | 待去噪的潜在图像，通常为噪声或上一步输出 |
| denoise | FLOAT | 是 | 1.0 | 去噪强度，1.0为完全去噪，数值越低越保留原始结构，适合图生图 |
### 输出参数
| 输出名 | 类型 | 说明 |
| ------- | ------ | --------------------- |
| samples | LATENT | 去噪后的潜在图像，可用于后续解码为最终图像 |
## 使用示例
Stable diffusion 1.5 文生图工作流示例
Stable diffusion 1.5 图生图工作流示例
## 源码
\[更新于2025年5月15日]
```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)
```