Documentation

Class name: IterativeMixingKSampler
Category: test
Output node: False
Repo Ref: https://github.com/deroberon/demofusion-comfyui

该节点通过逐步引入一组参考潜在表示的指导，来细化一批潜在表示，旨在通过迭代混合提高生成样本的质量。

Input types

Required

model
- 用于去噪和细化潜在表示的生成模型。
- Comfy dtype: MODEL
- Python dtype: torch.nn.Module
seed
- 用于初始化噪声生成的随机数生成器的种子值。
- Comfy dtype: INT
- Python dtype: int
cfg
- 影响去噪过程的配置参数。
- Comfy dtype: FLOAT
- Python dtype: float
sampler_name
- 用于迭代去噪过程的采样器名称。
- Comfy dtype: ENUM
- Python dtype: str
scheduler
- 用于随时间调整去噪过程的调度策略。
- Comfy dtype: ENUM
- Python dtype: str
step_increment
- 在每次去噪过程迭代中增加的步数。
- Comfy dtype: INT
- Python dtype: int
positive
- 用于指导去噪过程的正向调节数据。
- Comfy dtype: CONDITIONING
- Python dtype: dict
negative
- 用于进一步细化去噪过程的负向调节数据。
- Comfy dtype: CONDITIONING
- Python dtype: dict
latent_image_batch
- 需要被去噪和细化的潜在表示批次。
- Comfy dtype: LATENT
- Python dtype: torch.Tensor
denoise
- 控制每一步应用的去噪程度的参数。
- Comfy dtype: FLOAT
- Python dtype: float
alpha_1
- 影响参考潜在表示与去噪样本之间混合速率的参数。
- Comfy dtype: FLOAT
- Python dtype: float
reverse_batch
- 一个标志，指示在处理之前是否应反转批次。
- Comfy dtype: BOOLEAN
- Python dtype: bool

Output types

latent_image_batch
- 经过迭代去噪过程后细化的潜在表示批次。
- Comfy dtype: LATENT
- Python dtype: torch.Tensor

Usage tips

Infra type: CPU

Source code

class IterativeMixingKSampler:
    """
    Take a batch of latents, z_prime, and progressively de-noise them
    step by step from z_prime[0] to z_prime[steps], mixing in a weighted
    fraction of z_prime[i] at each step so that de-noising is guided by
    the z_prime latents. This batch sampler assumes that the number of steps
    is just the length of z_prime, so there is no steps parameter. The parameter
    latent_image_batch should come from the Batch Unsampler node.
    """

    @classmethod
    def INPUT_TYPES(s):
        return {'required': {'model': ('MODEL',), 'seed': ('INT', {'default': 0, 'min': 0, 'max': 18446744073709551615}), 'cfg': ('FLOAT', {'default': 8.0, 'min': 0.0, 'max': 100.0, 'step': 0.1, 'round': 0.01}), 'sampler_name': (comfy.samplers.KSampler.SAMPLERS,), 'scheduler': (comfy.samplers.KSampler.SCHEDULERS,), 'step_increment': ('INT', {'default': 1, 'min': 1, 'max': 10000}), 'positive': ('CONDITIONING',), 'negative': ('CONDITIONING',), 'latent_image_batch': ('LATENT',), 'denoise': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.01}), 'alpha_1': ('FLOAT', {'default': 3.0, 'min': 0.1, 'max': 10.0}), 'reverse_batch': ('BOOLEAN', {'default': True})}}
    RETURN_TYPES = ('LATENT',)
    FUNCTION = 'sample'
    CATEGORY = 'test'

    def sample(self, model, seed, cfg, sampler_name, scheduler, step_increment, positive, negative, latent_image_batch, denoise=1.0, alpha_1=3.0, reverse_batch=True):
        if reverse_batch:
            latent_image_batch['samples'] = torch.flip(latent_image_batch['samples'], [0])
        return batched_ksampler(model, seed, cfg, sampler_name, scheduler, step_increment, positive, negative, latent_image_batch, denoise=denoise, alpha_1=alpha_1)

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Documentation

Input types

Required

Output types

Usage tips

Source code

3.7 KiB

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