Mikey Sampler Base Only Advanced¶
Documentation¶
- Class name:
Mikey Sampler Base Only Advanced - Category:
Mikey/Sampling - Output node:
False
The MikeySamplerBaseOnlyAdvanced node is designed for advanced sampling operations, focusing on providing a base-only approach to sampling tasks. It aims to offer enhanced control and flexibility in generating samples, catering to specific needs without the complexity of additional layers or processes.
Input types¶
Required¶
base_model- Specifies the base model used for sampling, serving as the foundation for generating new samples.
- Comfy dtype:
MODEL - Python dtype:
str
positive_cond_base- Defines the positive conditioning to guide the sampling process towards desired attributes or content.
- Comfy dtype:
CONDITIONING - Python dtype:
str
negative_cond_base- Specifies the negative conditioning to avoid certain attributes or content in the generated samples.
- Comfy dtype:
CONDITIONING - Python dtype:
str
samples- Represents the initial set of samples or latent space from which the sampling process begins.
- Comfy dtype:
LATENT - Python dtype:
str
vae- The variational autoencoder used in conjunction with the base model to refine and generate samples.
- Comfy dtype:
VAE - Python dtype:
str
add_noise- Determines whether noise should be added to the sampling process, enhancing diversity or realism in the generated samples.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
denoise- Controls the level of denoising applied to the samples, affecting the clarity and quality of the output.
- Comfy dtype:
FLOAT - Python dtype:
float
steps- The number of steps to perform in the sampling process, affecting the detail and quality of the generated samples.
- Comfy dtype:
INT - Python dtype:
int
smooth_step- Adjusts the smoothness of the sampling steps, potentially enhancing the transition or variation between samples.
- Comfy dtype:
INT - Python dtype:
int
cfg_i- unknown
- Comfy dtype:
FLOAT - Python dtype:
unknown
sampler_name- Specifies the sampler algorithm used, influencing the diversity and quality of the generated samples.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
scheduler- Determines the scheduling algorithm for sampling, affecting the progression and variation of samples.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
upscale_model- The model used for upscaling the generated samples, enhancing their resolution or detail.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
seed- A seed value for the random number generator, ensuring reproducibility of the sampling process.
- Comfy dtype:
INT - Python dtype:
int
upscale_by- Defines the factor by which the samples are upscaled, affecting their final size and detail.
- Comfy dtype:
FLOAT - Python dtype:
float
hires_denoise- Controls the level of denoising applied to high-resolution samples, affecting their clarity and quality.
- Comfy dtype:
FLOAT - Python dtype:
float
hires_steps- unknown
- Comfy dtype:
INT - Python dtype:
unknown
Output types¶
latent- Comfy dtype:
LATENT - Represents the final generated samples after the sampling process, in a latent form.
- Python dtype:
str
- Comfy dtype:
Usage tips¶
- Infra type:
CPU - Common nodes:
Source code¶
class MikeySamplerBaseOnlyAdvanced:
@classmethod
def INPUT_TYPES(s):
s.image_scaler = ImageScaleBy()
s.upscale_models = folder_paths.get_filename_list("upscale_models")
s.all_upscale_models = s.upscale_models + s.image_scaler.upscale_methods
try:
default_model = 'lanczos' #'4x-UltraSharp.pth' if '4x-UltraSharp.pth' in s.upscale_models else s.upscale_models[0]
um = (s.all_upscale_models, {'default': default_model})
except:
um = (folder_paths.get_filename_list("upscale_models"), )
return {"required": {"base_model": ("MODEL",),
"positive_cond_base": ("CONDITIONING",),
"negative_cond_base": ("CONDITIONING",),
"samples": ("LATENT",),
"vae": ("VAE",),
"add_noise": (["enable","disable"], {"default": "enable"}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"steps": ("INT", {"default": 31, "min": 1, "max": 1000}),
'smooth_step': ("INT", {"default": 0, "min": -1, "max": 100}),
"cfg_1": ("FLOAT", {"default": 5.0, "min": 0.1, "max": 100.0, "step": 0.1}),
"cfg_2": ("FLOAT", {"default": 9.5, "min": 0.1, "max": 100.0, "step": 0.1}),
"sampler_name": (comfy.samplers.KSampler.SAMPLERS, {'default': 'dpmpp_3m_sde_gpu'}),
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, {'default': 'exponential'}),
"upscale_model": um,
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"upscale_by": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1}),
"hires_denoise": ("FLOAT", {"default": 0.4, "min": 0.0, "max": 1.0, "step": 0.01}),
"hires_steps": ("INT", {"default": 31, "min": 1, "max": 1000}),
}}
RETURN_TYPES = ('LATENT',)
FUNCTION = 'run'
CATEGORY = 'Mikey/Sampling'
def run(self, seed, base_model, positive_cond_base, negative_cond_base,
samples, vae, add_noise, denoise, steps, cfg_1, cfg_2, sampler_name,
scheduler, upscale_model, upscale_by, hires_denoise, hires_steps, smooth_step):
image_scaler = ImageScale()
vaeencoder = VAEEncode()
vaedecoder = VAEDecode()
uml = UpscaleModelLoader()
if upscale_model in image_scaler.upscale_methods:
upscale_model = upscale_model
else:
upscale_model = uml.load_model(upscale_model)[0]
iuwm = ImageUpscaleWithModel()
# step 1 run base model low cfg
start_step = int(steps - (steps * denoise))
if start_step > steps // 2:
last_step = steps - 1
else:
if start_step % 2 == 0:
last_step = steps // 2 - 1
else:
last_step = steps // 2
#print(f'base model start_step: {start_step}, last_step: {last_step}')
sample1 = common_ksampler(base_model, seed, steps, cfg_1, sampler_name, scheduler,
positive_cond_base, negative_cond_base, samples,
start_step=start_step, last_step=last_step, force_full_denoise=False)[0]
# step 2 run base model high cfg
start_step = last_step + 1
total_steps = steps + smooth_step
sample2 = common_ksampler(base_model, seed+1, total_steps, cfg_2, sampler_name, scheduler,
positive_cond_base, negative_cond_base, sample1,
disable_noise=True, start_step=start_step, force_full_denoise=True)
if upscale_by == 0:
return sample2
else:
sample2 = sample2[0]
# step 3 upscale
pixels = vaedecoder.decode(vae, sample2)[0]
org_width, org_height = pixels.shape[2], pixels.shape[1]
if isinstance(upscale_model, str):
img = self.image_scaler.upscale(pixels, upscale_model, upscale_by)[0]
else:
img = iuwm.upscale(upscale_model, image=pixels)[0]
upscaled_width, upscaled_height = int(org_width * upscale_by // 8 * 8), int(org_height * upscale_by // 8 * 8)
img = image_scaler.upscale(img, 'nearest-exact', upscaled_width, upscaled_height, 'center')[0]
if hires_denoise == 0:
return (vaeencoder.encode(vae, img)[0],)
# encode image
latent = vaeencoder.encode(vae, img)[0]
# step 3 run base model
start_step = int(hires_steps - (hires_steps * hires_denoise))
out = common_ksampler(base_model, seed, hires_steps, cfg_2, sampler_name, scheduler,
positive_cond_base, negative_cond_base, latent,
start_step=start_step, force_full_denoise=True)
return out