Mikey Sampler Base Only¶
Documentation¶
- Class name:
Mikey Sampler Base Only - Category:
Mikey/Sampling - Output node:
False
The Mikey Sampler Base Only node is designed for basic sampling operations within a specific framework, focusing on generating samples from a given input without the advanced features found in other variants. It serves as a foundational element for more complex sampling processes, providing a streamlined and efficient approach to sample generation.
Input types¶
Required¶
base_model- The 'base_model' parameter specifies the model used for the sampling process, serving as the foundation for generating samples. It is crucial for defining the behavior and capabilities of the sampling operation.
- Comfy dtype:
MODEL - Python dtype:
str
samples- The 'samples' parameter represents the initial set of samples or data points that the sampling process will use or modify. It is essential for starting the sampling operation with a predefined set of conditions or states.
- Comfy dtype:
LATENT - Python dtype:
str
positive_cond_base- Specifies the positive conditioning to guide the sampling towards desired attributes or features.
- Comfy dtype:
CONDITIONING - Python dtype:
str
negative_cond_base- Specifies the negative conditioning to steer the sampling away from undesired attributes or features.
- Comfy dtype:
CONDITIONING - Python dtype:
str
vae- The VAE (Variational Autoencoder) used in the sampling process for generating or refining samples.
- Comfy dtype:
VAE - Python dtype:
str
model_name- The name of the model used for upscaling the generated samples, enhancing their resolution or quality.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
seed- The 'seed' parameter ensures the reproducibility of the sampling process by initializing the random number generator with a specific value. This allows for consistent results across multiple runs, making it essential for experiments and comparisons.
- Comfy dtype:
INT - Python dtype:
int
upscale_by- The factor by which the samples are upscaled, improving their resolution or detail.
- Comfy dtype:
FLOAT - Python dtype:
float
hires_strength- Determines the strength of high-resolution features in the upscaled samples, affecting their clarity and detail.
- Comfy dtype:
FLOAT - Python dtype:
float
smooth_step- Controls the smoothness of the transition between steps in the sampling process, affecting the gradual evolution of samples.
- Comfy dtype:
INT - Python dtype:
int
Output types¶
latent- Comfy dtype:
LATENT - The output is a latent representation generated based on the input parameters, including the base model, samples, and seed. This latent representation captures the essence of the input in a form suitable for further processing or generation.
- Python dtype:
str
- Comfy dtype:
Usage tips¶
- Infra type:
CPU - Common nodes: unknown
Source code¶
class MikeySamplerBaseOnly:
@classmethod
def INPUT_TYPES(s):
return {"required": {"base_model": ("MODEL",), "samples": ("LATENT",),
"positive_cond_base": ("CONDITIONING",), "negative_cond_base": ("CONDITIONING",),
"vae": ("VAE",),
"model_name": (folder_paths.get_filename_list("upscale_models"), ),
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
"upscale_by": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1}),
"hires_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 2.0, "step": 0.1}),
'smooth_step': ("INT", {"default": 0, "min": -1, "max": 100})}}
RETURN_TYPES = ('LATENT',)
FUNCTION = 'run'
CATEGORY = 'Mikey/Sampling'
def adjust_start_step(self, image_complexity, hires_strength=1.0):
image_complexity /= 24
if image_complexity > 1:
image_complexity = 1
image_complexity = min([0.55, image_complexity]) * hires_strength
return min([31, 31 - int(round(image_complexity * 31,0))])
def run(self, seed, base_model, vae, samples, positive_cond_base, negative_cond_base,
model_name, upscale_by=1.0, hires_strength=1.0, upscale_method='normal', smooth_step=0):
image_scaler = ImageScale()
vaeencoder = VAEEncode()
vaedecoder = VAEDecode()
uml = UpscaleModelLoader()
upscale_model = uml.load_model(model_name)[0]
iuwm = ImageUpscaleWithModel()
# common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0,
# disable_noise=False, start_step=None, last_step=None, force_full_denoise=False)
# step 1 run base model low cfg
sample1 = common_ksampler(base_model, seed, 30, 5, 'dpmpp_3m_sde_gpu', 'exponential', positive_cond_base, negative_cond_base, samples,
start_step=0, last_step=14, force_full_denoise=False)[0]
# step 2 run base model high cfg
sample2 = common_ksampler(base_model, seed+1, 31 + smooth_step, 9.5, 'dpmpp_3m_sde_gpu', 'exponential', positive_cond_base, negative_cond_base, sample1,
disable_noise=True, start_step=15, 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]
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_strength == 0:
return (vaeencoder.encode(vae, img)[0],)
# Adjust start_step based on complexity
image_complexity = calculate_image_complexity(img)
#print('Image Complexity:', image_complexity)
start_step = self.adjust_start_step(image_complexity, hires_strength)
# encode image
latent = vaeencoder.encode(vae, img)[0]
# step 3 run base model
out = common_ksampler(base_model, seed, 31, 9.5, 'dpmpp_3m_sde_gpu', 'exponential', positive_cond_base, negative_cond_base, latent,
start_step=start_step, force_full_denoise=True)
return out