144 lines
4.7 KiB
Python
144 lines
4.7 KiB
Python
# Based on the concept from https://github.com/muerrilla/sd-webui-detail-daemon
|
|
# Original code courtesy from https://github.com/blepping/
|
|
from __future__ import annotations
|
|
|
|
from typing import TYPE_CHECKING
|
|
import inspect
|
|
|
|
from comfy.samplers import KSAMPLER
|
|
import numpy as np
|
|
|
|
if TYPE_CHECKING:
|
|
import torch
|
|
|
|
def advanced_lying_sigma_sampler(
|
|
model: object,
|
|
x: torch.Tensor,
|
|
sigmas: torch.Tensor,
|
|
*,
|
|
sampler: object,
|
|
dishonesty_factor: float,
|
|
start_percent: float,
|
|
end_percent: float,
|
|
smooth_factor: float = 0.5,
|
|
**kwargs: dict,
|
|
) -> torch.Tensor:
|
|
start_sigma = round(model.inner_model.inner_model.model_sampling.percent_to_sigma(start_percent), 4)
|
|
end_sigma = round(model.inner_model.inner_model.model_sampling.percent_to_sigma(end_percent), 4)
|
|
|
|
def model_wrapper(x: torch.Tensor, sigma: torch.Tensor, **extra_args: dict):
|
|
sigma_float = float(sigma.max().detach().cpu())
|
|
if end_sigma <= sigma_float <= start_sigma:
|
|
adjustment = dishonesty_factor * (0.5 * (1 - np.cos(smooth_factor * np.pi)))
|
|
sigma = sigma * (1.0 + adjustment)
|
|
return model(x, sigma, **extra_args)
|
|
|
|
for k in (
|
|
"inner_model",
|
|
"sigmas",
|
|
):
|
|
if hasattr(model, k):
|
|
setattr(model_wrapper, k, getattr(model, k))
|
|
|
|
# Check if denoise_mask is supported by the sampler function
|
|
if 'denoise_mask' in inspect.signature(sampler.sampler_function).parameters:
|
|
return sampler.sampler_function(
|
|
model_wrapper,
|
|
x,
|
|
sigmas,
|
|
denoise_mask=kwargs.get('denoise_mask'),
|
|
**kwargs,
|
|
**sampler.extra_options,
|
|
)
|
|
else:
|
|
return sampler.sampler_function(
|
|
model_wrapper,
|
|
x,
|
|
sigmas,
|
|
**kwargs,
|
|
**sampler.extra_options,
|
|
)
|
|
|
|
class AdvancedLyingSigmaSamplerNode:
|
|
DESCRIPTION = "高级sigma控制器，使用前最好关闭float rounding并重启comfui,以达到最佳控制效果|For advanced sigma controllers, it is best to turn off float rounding and restart comfui before use for optimal control."
|
|
CATEGORY = "KY Nodes/advanced_samplers"
|
|
RETURN_TYPES = ("SAMPLER",)
|
|
FUNCTION = "initialize"
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(cls) -> dict:
|
|
return {
|
|
"required": {
|
|
"sampler": ("SAMPLER",),
|
|
"dishonesty_factor": (
|
|
"FLOAT",
|
|
{
|
|
"default": -0.1,
|
|
"min": -1.0,
|
|
"max": 1.0,
|
|
"step": 0.001,
|
|
"tooltip": "默认值-0.1，一般来说这是一个较大值|Default value -0.1, which is generally a larger value."
|
|
}
|
|
),
|
|
"start_percent": (
|
|
"FLOAT",
|
|
{
|
|
"default": 0.1,
|
|
"min": 0.0,
|
|
"max": 1.0,
|
|
"step": 0.01,
|
|
"tooltip": "bilibili@深深蓝hana."
|
|
}
|
|
),
|
|
"end_percent": (
|
|
"FLOAT",
|
|
{
|
|
"default": 0.9,
|
|
"min": 0.0,
|
|
"max": 1.0,
|
|
"step": 0.01,
|
|
"tooltip": "bilibili@深深蓝hana."
|
|
}
|
|
),
|
|
"smooth_factor": (
|
|
"FLOAT",
|
|
{
|
|
"default": 0.5,
|
|
"min": 0.0,
|
|
"max": 1.0,
|
|
"step": 0.01,
|
|
"tooltip": "bilibili@深深蓝hana."
|
|
}
|
|
),
|
|
}
|
|
}
|
|
|
|
@classmethod
|
|
def initialize(
|
|
cls,
|
|
sampler: object,
|
|
dishonesty_factor: float,
|
|
start_percent: float,
|
|
end_percent: float,
|
|
smooth_factor: float,
|
|
) -> tuple:
|
|
return (
|
|
KSAMPLER(
|
|
advanced_lying_sigma_sampler,
|
|
extra_options={
|
|
"sampler": sampler,
|
|
"dishonesty_factor": dishonesty_factor,
|
|
"start_percent": start_percent,
|
|
"end_percent": end_percent,
|
|
"smooth_factor": smooth_factor,
|
|
},
|
|
),
|
|
)
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"AdvancedLyingSigmaSampler": AdvancedLyingSigmaSamplerNode,
|
|
}
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"AdvancedLyingSigmaSampler": "Advanced Lying Sigma Sampler"
|
|
}
|