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import os
import nodes
import comfy.samplers
import random
from nodes import common_ksampler
#wd = os.getcwd()
#print("working directory is ", wd)
#
#filePath = __file__
#print("This script file path is ", filePath)
#
#absFilePath = os.path.abspath(__file__)
#print("This script absolute path is ", absFilePath)
#
#path, filename = os.path.split(absFilePath)
#print("Script file path is {}, filename is {}".format(path, filename))
class Random_Sampler:
def __init__(self):
print(f"Random_Sampler __init__")
pass
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"model": ("MODEL",),
"positive": ("CONDITIONING", ),
"negative": ("CONDITIONING", ),
"LATENT": ("LATENT", ),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, ),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, ),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
#"Random": (["enable", "disable"],),
"steps_min": ("INT", {"default": 20, "min": 1,"max": 10000, "step": 1 }),
"steps_max": ("INT", {"default": 30, "min": 1,"max": 10000, "step": 1 }),
"cfg_min": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 100.0, "step": 0.5}),
"cfg_max": ("FLOAT", {"default": 9.0, "min": 0.0, "max": 100.0, "step": 0.5}),
"denoise_min": ("FLOAT", {"default": 0.50, "min": 0.01, "max": 1.0, "step": 0.01}),
"denoise_max": ("FLOAT", {"default": 1.00, "min": 0.01, "max": 1.0, "step": 0.01}),
},
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "test"
OUTPUT_NODE = False
CATEGORY = "sampling"
def test(self,
model,
positive,
negative,
LATENT,
sampler_name,
scheduler,
seed,
#Random,
steps_min,
steps_max,
cfg_min,
cfg_max,
denoise_min,
denoise_max,
):
print(f"""
model : {model} ;
positive : {positive} ;
negative : {negative} ;
LATENT: {LATENT} ;
sampler_name : {sampler_name} ;
scheduler: {scheduler} ;
{seed} ;
{steps_min} ;
{steps_max} ;
{cfg_min} ;
{cfg_max} ;
{denoise_min} ;
{denoise_max} ;
""")
#if Random == "enable":
# print(f"Random enable")
# return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise)
return common_ksampler(
model,
seed,
random.randint( min(steps_min,steps_max), max(steps_min,steps_max) ),
random.randint( int(cfg_min*2) , int(cfg_max*2) ) / 2 ,
sampler_name,
scheduler,
positive,
negative,
LATENT,
denoise=random.uniform(min(denoise_min,denoise_max),max(denoise_min,denoise_max))
)
#return (LATENT,)
#NODE_CLASS_MAPPINGS = {
# "Random_Sampler": Random_Sampler
#} |