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KSampler (pipe)

Documentation

  • Class name: ImpactKSamplerBasicPipe
  • Category: sampling
  • Output node: False

The ImpactKSamplerBasicPipe node is designed for sampling operations within a basic pipeline, utilizing a variety of samplers and schedulers to process and transform latent images. It encapsulates the complexity of sampling algorithms, providing a streamlined interface for generating or modifying latent representations based on specified configurations and inputs.

Input types

Required

  • basic_pipe
    • Represents the core components required for the sampling process, including models and configurations essential for the operation.
    • Comfy dtype: BASIC_PIPE
    • Python dtype: tuple
  • seed
    • Determines the randomness seed for sampling, affecting the reproducibility and variation of the output.
    • Comfy dtype: INT
    • Python dtype: int
  • steps
    • Specifies the number of steps to perform in the sampling process, impacting the detail and quality of the generated latent image.
    • Comfy dtype: INT
    • Python dtype: int
  • cfg
    • Controls the configuration for the sampling algorithm, influencing the behavior and outcomes of the sampling process.
    • Comfy dtype: FLOAT
    • Python dtype: float
  • sampler_name
    • Selects the specific sampler to use, allowing for customization of the sampling technique.
    • Comfy dtype: COMBO[STRING]
    • Python dtype: str
  • scheduler
    • Chooses the scheduler for controlling the sampling process, affecting the progression and adaptation of sampling steps.
    • Comfy dtype: COMBO[STRING]
    • Python dtype: str
  • latent_image
    • The initial latent image to be processed or transformed by the sampling operation.
    • Comfy dtype: LATENT
    • Python dtype: torch.Tensor
  • denoise
    • Adjusts the level of denoising applied to the latent image, fine-tuning the clarity and quality of the output.
    • Comfy dtype: FLOAT
    • Python dtype: float

Output types

  • basic_pipe
    • Comfy dtype: BASIC_PIPE
    • Returns the basic pipeline components, including the model and configurations used in the sampling process.
    • Python dtype: tuple
  • latent
    • Comfy dtype: LATENT
    • The processed or transformed latent image resulting from the sampling operation.
    • Python dtype: torch.Tensor
  • vae
    • Comfy dtype: VAE
    • The variational autoencoder used in the process, essential for encoding and decoding latent images.
    • Python dtype: torch.nn.Module

Usage tips

Source code

class KSamplerBasicPipe:
    @classmethod
    def INPUT_TYPES(s):
        return {"required":
                    {"basic_pipe": ("BASIC_PIPE",),
                     "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0}),
                     "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
                     "scheduler": (core.SCHEDULERS, ),
                     "latent_image": ("LATENT", ),
                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                     }
                }

    RETURN_TYPES = ("BASIC_PIPE", "LATENT", "VAE")
    FUNCTION = "sample"

    CATEGORY = "sampling"

    def sample(self, basic_pipe, seed, steps, cfg, sampler_name, scheduler, latent_image, denoise=1.0):
        model, clip, vae, positive, negative = basic_pipe
        latent = impact_sample(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise)
        return basic_pipe, latent, vae