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Allow overriding noise_sampler + reduce code duplication

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blepping
2024-03-10 05:50:36 -06:00
parent b609db0df7
commit b6610d3c01
3 changed files with 133 additions and 201 deletions
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.gitignore
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@@ -0,0 +1,3 @@
*.bak
*~
__pycache__
extra_samplers.py
+115 -120
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@@ -4,6 +4,7 @@ import torch
from torch import nn, FloatTensor
import torchsde
from tqdm.auto import trange, tqdm
import numpy as np
import comfy.sample
@@ -45,12 +46,33 @@ def add_schedulers():
import importlib
importlib.reload(k_diffusion_sampling)
# Noise samplers
NOISE_SAMPLER_NAMES=("gaussian", "uniform", "brownian", "highres-pyramid", "pyramid", "perlin", "laplacian")
def get_noise_sampler_names(default=None):
if not default:
return NOISE_SAMPLER_NAMES
return (default,) + tuple(n for n in NOISE_SAMPLER_NAMES if n != default)
def mk_noise_sampler(x, fun):
return lambda _sigma, _sigma_next: fun(x)
def get_noise_sampler(x, sigmas, noise_sampler_type="brownian", extra_args=None, cpu=False):
if noise_sampler_type == "brownian":
seed = extra_args.get("seed", None) if extra_args else None
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=cpu)
return mk_noise_sampler(x, NOISE_SAMPLER_HANDLERS.get(noise_sampler_type, uniform_noise_like))
from torch import Generator, Tensor, lerp
from torch.nn.functional import unfold
from typing import Callable, Tuple
from math import pi
def uniform_noise_like(x):
return (torch.rand_like(x) - 0.5) * 2 * 1.73
def get_positions(block_shape: Tuple[int, int]) -> Tensor:
"""
Generate position tensor.
@@ -307,7 +329,8 @@ def green_noise_sampler(x): # This doesn't work properly right now
print(noise)
return lambda sigma, sigma_next: noise
def power_noise_sampler(tensor, alpha=2, k=1): # This doesn't work properly right now
# I'm not sure how this differs from the other implementation but it doesn't seem to be used at present.
def power_noise_sampler_2(tensor, alpha=2, k=1): # This doesn't work properly right now
"""Generate 1/f noise for a given tensor.
Args:
@@ -330,6 +353,83 @@ def power_noise_sampler(tensor, alpha=2, k=1): # This doesn't work properly righ
print(variance)
return lambda sigma, sigma_next: noise / 3
def pyramid_noise_like(size, dtype, layout, generator, device="cpu", discount=0.8):
b, c, h, w = size
orig_h = h
orig_w = w
noise = torch.zeros(size=size, dtype=dtype, layout=layout, device=device)
r = 1
for i in range(5):
r *= 2 # Rather than always going 2x,
#w, h = max(1, int(w/(r**i))), max(1, int(h/(r**i)))
noise += torch.nn.functional.interpolate((torch.normal(mean=0, std=0.5 ** i, size=(b, c, h * r, w * r), dtype=dtype, layout=layout, generator=generator, device=device)), size=(orig_h, orig_w), mode='nearest-exact') * discount**i
#if w>=orig_w*16 or h>=orig_h*16: break
return noise
def power_noise_sampler(size, dtype, layout, generator, device="cpu", alpha=2, k=1): # This doesn't work properly right now
"""Generate 1/f noise for a given tensor.
Args:
tensor: The tensor to add noise to.
alpha: The parameter that determines the slope of the spectrum.
k: A constant.
Returns:
A tensor with the same shape as `tensor` containing 1/f noise.
"""
tensor = torch.randn(size=size, dtype=dtype, layout=layout, generator=generator, device=device)
fft = torch.fft.fft2(tensor)
freq = torch.arange(1, len(fft) + 1, dtype=torch.float)
spectral_density = k / freq**alpha
noise = torch.rand(size=size, dtype=dtype, layout=layout, generator=generator, device=device) * spectral_density
mean = torch.mean(noise, dim=(-2, -1), keepdim=True).to(tensor.device)
std = torch.std(noise, dim=(-2, -1), keepdim=True).to(tensor.device)
noise = noise.to(tensor.device).sub_(mean).div_(std)
return noise
def prepare_noise(latent_image, seed, noise_type, noise_inds=None): # From `sample.py`
"""
creates random noise given a latent image and a seed.
optional arg skip can be used to skip and discard x number of noise generations for a given seed
"""
generator = torch.manual_seed(seed)
match noise_type:
case "gaussian":
noise_func = torch.randn
case "uniform":
def uniform_rand(*size, **kwargs):
return (torch.rand(*size, **kwargs) - 0.5) * 2 * 1.73
noise_func = uniform_rand
case "pyramid":
noise_func = pyramid_noise_like
case "power":
noise_func = power_noise_sampler
case _:
noise_func = torch.randn
if noise_inds is None:
return noise_func(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
noises = []
for i in range(unique_inds[-1]+1):
noise = noise_func([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
if i in unique_inds:
noises.append(noise)
noises = [noises[i] for i in inverse]
noises = torch.cat(noises, axis=0)
return noises
NOISE_SAMPLER_HANDLERS={
# Brownian is special-cased.
"gaussian": torch.randn_like,
"highres-pyramid": highres_pyramid_noise_like,
"pyramid": lambda x: pyramid_noise_like(x.size(), x.dtype, x.layout, None, device=x.device),
"perlin": rand_perlin_like,
"laplacian": rand_laplacian_like,
"uniform": uniform_noise_like,
}
# Below this point are extra samplers
@torch.no_grad()
def sample_clyb_4m_sde_momentumized(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1., noise_sampler=None, momentum=0.0):
@@ -462,25 +562,8 @@ def sample_ttm_jvp(model, x, sigmas, extra_args=None, callback=None, disable=Non
# Many thanks to Kat + Birch-San for this wonderful sampler implementation! https://github.com/Birch-san/sdxl-play/commits/res/
from .other_samplers.refined_exp_solver import sample_refined_exp_s
def sample_res_solver(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, denoise_to_zero=True, simple_phi_calc=False, c2=0.5, ita=torch.Tensor((0.25,)), momentum=0.0):
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
seed = extra_args.get("seed", None)
match noise_sampler:
case "brownian":
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=False)
case "gaussian":
noise_sampler = lambda sigma, sigma_next: torch.randn_like(x)
case "uniform":
noise_sampler = lambda sigma, sigma_next: (torch.rand_like(x) - 0.5) * 2 * 1.73
case "highres-pyramid":
noise_sampler = lambda sigma, sigma_next: highres_pyramid_noise_like(x)
case "perlin":
noise_sampler = lambda sigma, sigma_next: rand_perlin_like(x)
case "laplacian":
noise_sampler = lambda sigma, sigma_next: rand_laplacian_like(x)
case _:
noise_sampler = lambda sigma, sigma_next: torch.randn_like(x)
return sample_refined_exp_s(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, noise_sampler=noise_sampler, denoise_to_zero=denoise_to_zero, simple_phi_calc=simple_phi_calc, c2=c2, ita=ita, momentum=momentum)
def sample_res_solver(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler_type="gaussian", noise_sampler=None, denoise_to_zero=True, simple_phi_calc=False, c2=0.5, ita=torch.Tensor((0.25,)), momentum=0.0):
return sample_refined_exp_s(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, noise_sampler=noise_sampler or get_noise_sampler(x, sigmas, noise_sampler_type, noise_sampler, extra_args), denoise_to_zero=denoise_to_zero, simple_phi_calc=simple_phi_calc, c2=c2, ita=ita, momentum=momentum)
@torch.no_grad()
def sample_dpmpp_dualsde_momentum(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1/2, momentum=0.0):
@@ -582,73 +665,19 @@ def sample_dpmpp_dualsde_momentum(model, x, sigmas, extra_args=None, callback=No
h_1, h_2, h_3 = h, h_1, h_2
return x
def sample_dpmpp_dualsdemomentum(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1/2, momentum=0.0):
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
seed = extra_args.get("seed", None)
match noise_sampler:
case "brownian":
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=False)
case "gaussian":
noise_sampler = lambda sigma, sigma_next: torch.randn_like(x)
case "uniform":
noise_sampler = lambda sigma, sigma_next: (torch.rand_like(x) - 0.5) * 2 * 1.73
case "perlin":
noise_sampler = lambda sigma, sigma_next: rand_perlin_like(x)
case "laplacian":
noise_sampler = lambda sigma, sigma_next: rand_laplacian_like(x)
case _:
noise_sampler = lambda sigma, sigma_next: torch.randn_like(x)
return sample_dpmpp_dualsde_momentum(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r, momentum=momentum)
def sample_dpmpp_dualsdemomentum(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler_type="gaussian", noise_sampler=None, r=1/2, momentum=0.0):
return sample_dpmpp_dualsde_momentum(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler or get_noise_sampler(x, sigmas, noise_sampler_type, noise_sampler, extra_args), r=r, momentum=momentum)
from .other_samplers.sample_ttm import sample_ttm_jvp
def sample_ttmcustom(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
seed = extra_args.get("seed", None)
match noise_sampler:
case "gaussian":
noise_sampler = lambda sigma, sigma_next: torch.randn_like(x)
case "uniform":
noise_sampler = lambda sigma, sigma_next: (torch.rand_like(x) - 0.5) * 2 * 1.73
case "brownian":
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=False)
case _:
noise_sampler = lambda sigma, sigma_next: torch.randn_like(x)
return sample_ttm_jvp(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)
def sample_ttmcustom(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler_type="gaussian",noise_sampler=None):
return sample_ttm_jvp(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler or get_noise_sampler(x, sigmas, noise_sampler_type, noise_sampler, extra_args))
from comfy.k_diffusion.sampling import sample_lcm
def sample_lcmcustom(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
seed = extra_args.get("seed", None)
match noise_sampler:
case "gaussian":
noise_sampler = lambda sigma, sigma_next: torch.randn_like(x)
case "uniform":
noise_sampler = lambda sigma, sigma_next: (torch.rand_like(x) - 0.5) * 2 * 1.73
case "brownian":
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=False)
case _:
noise_sampler = lambda sigma, sigma_next: torch.randn_like(x)
return sample_lcm(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, noise_sampler=noise_sampler)
def sample_lcmcustom(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler_type="gaussian", noise_sampler=None):
return sample_lcm(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, noise_sampler=noise_sampler or get_noise_sampler(x, sigmas, noise_sampler_type, noise_sampler, extra_args))
def sample_clyb_4m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler="brownian", momentum=0.0):
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
seed = extra_args.get("seed", None)
match noise_sampler:
case "brownian":
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=False)
case "gaussian":
noise_sampler = lambda sigma, sigma_next: torch.randn_like(x)
case "uniform":
noise_sampler = lambda sigma, sigma_next: (torch.rand_like(x) - 0.5) * 2 * 1.73
case "highres-pyramid":
noise_sampler = lambda sigma, sigma_next: highres_pyramid_noise_like(x)
case "perlin":
noise_sampler = lambda sigma, sigma_next: rand_perlin_like(x)
case "laplacian":
noise_sampler = lambda sigma, sigma_next: rand_laplacian_like(x)
case _:
noise_sampler = lambda sigma, sigma_next: (torch.rand_like(x) - 0.5) * 2 * 1.73
return sample_clyb_4m_sde_momentumized(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, momentum=momentum)
def sample_clyb_4m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler_type="brownian", noise_sampler=None, momentum=0.0):
return sample_clyb_4m_sde_momentumized(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler or get_noise_sampler(x, sigmas, noise_sampler_type, noise_sampler, extra_args), momentum=momentum)
# This code works, but I'm currently experimenting with different methods
@@ -698,25 +727,8 @@ def sampler_euler_ancestral_dancing(model, x, sigmas, extra_args=None, callback=
return x
def sample_euler_ancestral_dancing(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler="gaussian", leap=2, eta_dance=1.0):
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
seed = extra_args.get("seed", None)
match noise_sampler:
case "brownian":
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=False)
case "gaussian":
noise_sampler = lambda sigma, sigma_next: torch.randn_like(x)
case "uniform":
noise_sampler = lambda sigma, sigma_next: (torch.rand_like(x) - 0.5) * 2 * 1.73
case "highres-pyramid":
noise_sampler = lambda sigma, sigma_next: highres_pyramid_noise_like(x)
case "perlin":
noise_sampler = lambda sigma, sigma_next: rand_perlin_like(x)
case "laplacian":
noise_sampler = lambda sigma, sigma_next: rand_laplacian_like(x)
case _:
noise_sampler = lambda sigma, sigma_next: (torch.rand_like(x) - 0.5) * 2 * 1.73
return sampler_euler_ancestral_dancing(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, leap=leap, eta_dance=eta_dance)
def sample_euler_ancestral_dancing(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler_type="gaussian", noise_sampler=None, leap=2, eta_dance=1.0):
return sampler_euler_ancestral_dancing(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler or get_noise_sampler(x, sigmas, noise_sampler_type, noise_sampler, extra_args), leap=leap, eta_dance=eta_dance)
@torch.no_grad()
@@ -778,25 +790,8 @@ def sampler_dpmpp_3m_sde_dynamic_eta(model, x, sigmas, extra_args=None, callback
h_1, h_2 = h, h_1
return x
def sample_dpmpp_3m_sde_dynamic_eta(model, x, sigmas, extra_args=None, callback=None, disable=None, eta_max=1.0, eta_min=0.0, s_noise=1., noise_sampler="brownian"):
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
seed = extra_args.get("seed", None)
match noise_sampler:
case "brownian":
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=False)
case "gaussian":
noise_sampler = lambda sigma, sigma_next: torch.randn_like(x)
case "uniform":
noise_sampler = lambda sigma, sigma_next: (torch.rand_like(x) - 0.5) * 2 * 1.73
case "highres-pyramid":
noise_sampler = lambda sigma, sigma_next: highres_pyramid_noise_like(x)
case "perlin":
noise_sampler = lambda sigma, sigma_next: rand_perlin_like(x)
case "laplacian":
noise_sampler = lambda sigma, sigma_next: rand_laplacian_like(x)
case _:
noise_sampler = lambda sigma, sigma_next: (torch.rand_like(x) - 0.5) * 2 * 1.73
return sampler_dpmpp_3m_sde_dynamic_eta(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta_max=eta_max, eta_min=eta_min, s_noise=s_noise, noise_sampler=noise_sampler)
def sample_dpmpp_3m_sde_dynamic_eta(model, x, sigmas, extra_args=None, callback=None, disable=None, eta_max=1.0, eta_min=0.0, s_noise=1., noise_sampler_type="brownian", noise_sampler=None):
return sampler_dpmpp_3m_sde_dynamic_eta(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta_max=eta_max, eta_min=eta_min, s_noise=s_noise, noise_sampler=noise_sampler or get_noise_sampler(x, sigmas, noise_sampler_type, noise_sampler, extra_args))
# Add your personal samplers below here, just for formatting purposes ;3
nodes.py
+15 -81
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@@ -1,83 +1,17 @@
from .other_samplers.refined_exp_solver import sample_refined_exp_s
from .extra_samplers import get_noise_sampler_names, prepare_noise
import comfy.samplers
import comfy.sample
from comfy.k_diffusion import sampling as k_diffusion_sampling
import latent_preview
import torch
import numpy as np
from tqdm.auto import trange
def pyramid_noise_like(size, dtype, layout, generator, device="cpu", discount=0.8):
b, c, h, w = size
orig_h = h
orig_w = w
noise = torch.zeros(size=size, dtype=dtype, layout=layout, device=device)
r = 1
for i in range(5):
r *= 2 # Rather than always going 2x,
#w, h = max(1, int(w/(r**i))), max(1, int(h/(r**i)))
noise += torch.nn.functional.interpolate((torch.normal(mean=0, std=0.5 ** i, size=(b, c, h * r, w * r), dtype=dtype, layout=layout, generator=generator, device=device)), size=(orig_h, orig_w), mode='nearest-exact') * discount**i
#if w>=orig_w*16 or h>=orig_h*16: break
return noise
def power_noise_sampler(size, dtype, layout, generator, device="cpu", alpha=2, k=1): # This doesn't work properly right now
"""Generate 1/f noise for a given tensor.
Args:
tensor: The tensor to add noise to.
alpha: The parameter that determines the slope of the spectrum.
k: A constant.
Returns:
A tensor with the same shape as `tensor` containing 1/f noise.
"""
tensor = torch.randn(size=size, dtype=dtype, layout=layout, generator=generator, device=device)
fft = torch.fft.fft2(tensor)
freq = torch.arange(1, len(fft) + 1, dtype=torch.float)
spectral_density = k / freq**alpha
noise = torch.rand(size=size, dtype=dtype, layout=layout, generator=generator, device=device) * spectral_density
mean = torch.mean(noise, dim=(-2, -1), keepdim=True).to(tensor.device)
std = torch.std(noise, dim=(-2, -1), keepdim=True).to(tensor.device)
noise = noise.to(tensor.device).sub_(mean).div_(std)
return noise
def prepare_noise(latent_image, seed, noise_type, noise_inds=None): # From `sample.py`
"""
creates random noise given a latent image and a seed.
optional arg skip can be used to skip and discard x number of noise generations for a given seed
"""
generator = torch.manual_seed(seed)
match noise_type:
case "gaussian":
noise_func = torch.randn
case "uniform":
def uniform_rand(*size, **kwargs):
return (torch.rand(*size, **kwargs) - 0.5) * 2 * 1.73
noise_func = uniform_rand
case "pyramid":
noise_func = pyramid_noise_like
case "power":
noise_func = power_noise_sampler
case _:
noise_func = torch.randn
if noise_inds is None:
return noise_func(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
noises = []
for i in range(unique_inds[-1]+1):
noise = noise_func([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
if i in unique_inds:
noises.append(noise)
noises = [noises[i] for i in inverse]
noises = torch.cat(noises, axis=0)
return noises
class SamplerRES_MOMENTUMIZED:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"noise_sampler_type": (["gaussian", "uniform", "brownian", "highres-pyramid", "perlin", "laplacian"], ),
{"noise_sampler_type": (get_noise_sampler_names(), ),
"momentum": ("FLOAT", {"default": 0.5, "min": -1.0, "max": 1.0, "step":0.01}),
"denoise_to_zero": ("BOOLEAN", {"default": True}),
"simple_phi_calc": ("BOOLEAN", {"default": False}),
@@ -91,14 +25,14 @@ class SamplerRES_MOMENTUMIZED:
FUNCTION = "get_sampler"
def get_sampler(self, noise_sampler_type, momentum, denoise_to_zero, simple_phi_calc, ita, c2):
sampler = comfy.samplers.ksampler("res_momentumized", {"noise_sampler": noise_sampler_type, "denoise_to_zero": denoise_to_zero, "simple_phi_calc": simple_phi_calc, "c2": c2, "ita": torch.Tensor((ita,)), "momentum": momentum})
sampler = comfy.samplers.ksampler("res_momentumized", {"noise_sampler_type": noise_sampler_type, "denoise_to_zero": denoise_to_zero, "simple_phi_calc": simple_phi_calc, "c2": c2, "ita": torch.Tensor((ita,)), "momentum": momentum})
return (sampler, )
class SamplerDPMPP_DUALSDE_MOMENTUMIZED:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"noise_sampler_type": (["gaussian", "uniform", "brownian", "perlin", "laplacian"], ),
{"noise_sampler_type": (get_noise_sampler_names(), ),
"momentum": ("FLOAT", {"default": 0.5, "min": -1.0, "max": 1.0, "step":0.01}),
"eta": ("FLOAT", {"default": 1, "min": 0.0, "max": 100.0, "step":0.01}),
"s_noise": ("FLOAT", {"default": 1, "min": 0.0, "max": 100.0, "step":0.01}),
@@ -111,14 +45,14 @@ class SamplerDPMPP_DUALSDE_MOMENTUMIZED:
FUNCTION = "get_sampler"
def get_sampler(self, noise_sampler_type, momentum, eta, s_noise, r,):
sampler = comfy.samplers.ksampler("dpmpp_dualsde_momentumized", {"noise_sampler": noise_sampler_type, "eta": eta, "s_noise": s_noise, "r": r, "momentum": momentum})
sampler = comfy.samplers.ksampler("dpmpp_dualsde_momentumized", {"noise_sampler_type": noise_sampler_type, "eta": eta, "s_noise": s_noise, "r": r, "momentum": momentum})
return (sampler, )
class SamplerTTM:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"noise_sampler_type": (["gaussian", "uniform", "brownian"], ),
{"noise_sampler_type": (get_noise_sampler_names(), ),
"eta": ("FLOAT", {"default": 1, "min": 0.0, "max": 100.0, "step":0.01}),
"s_noise": ("FLOAT", {"default": 1, "min": 0.0, "max": 100.0, "step":0.01}),
}
@@ -129,7 +63,7 @@ class SamplerTTM:
FUNCTION = "get_sampler"
def get_sampler(self, noise_sampler_type, eta, s_noise):
sampler = comfy.samplers.ksampler("ttm", {"noise_sampler": noise_sampler_type, "eta": eta, "s_noise": s_noise})
sampler = comfy.samplers.ksampler("ttm", {"noise_sampler_type": noise_sampler_type, "eta": eta, "s_noise": s_noise})
return (sampler, )
@@ -137,7 +71,7 @@ class SamplerLCMCustom:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"noise_sampler_type": (["gaussian", "uniform", "brownian"], ),
{"noise_sampler_type": (get_noise_sampler_names(), ),
}
}
RETURN_TYPES = ("SAMPLER",)
@@ -146,14 +80,14 @@ class SamplerLCMCustom:
FUNCTION = "get_sampler"
def get_sampler(self, noise_sampler_type):
sampler = comfy.samplers.ksampler("lcm_custom_noise", {"noise_sampler": noise_sampler_type})
sampler = comfy.samplers.ksampler("lcm_custom_noise", {"noise_sampler_type": noise_sampler_type})
return (sampler, )
class SamplerCLYB_4M_SDE_MOMENTUMIZED:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"noise_sampler_type": (["gaussian", "uniform", "brownian", "highres-pyramid", "perlin", "laplacian"], ),
{"noise_sampler_type": (get_noise_sampler_names(default="brownian"), ),
"momentum": ("FLOAT", {"default": 0.5, "min": -1.0, "max": 1.0, "step":0.01}),
"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01}),
"s_noise": ("FLOAT", {"default": 1, "min": 0.0, "max": 100.0, "step":0.01}),
@@ -165,14 +99,14 @@ class SamplerCLYB_4M_SDE_MOMENTUMIZED:
FUNCTION = "get_sampler"
def get_sampler(self, noise_sampler_type, eta, s_noise, momentum):
sampler = comfy.samplers.ksampler("clyb_4m_sde_momentumized", {"noise_sampler": noise_sampler_type, "eta": eta, "s_noise": s_noise, "momentum": momentum})
sampler = comfy.samplers.ksampler("clyb_4m_sde_momentumized", {"noise_sampler_type": noise_sampler_type, "eta": eta, "s_noise": s_noise, "momentum": momentum})
return (sampler, )
class SamplerEULER_ANCESTRAL_DANCING:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"noise_sampler_type": (["gaussian", "uniform", "brownian", "highres-pyramid", "perlin", "laplacian"], ),
{"noise_sampler_type": (get_noise_sampler_names(), ),
"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01}),
"eta_dance": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01}),
"s_noise": ("FLOAT", {"default": 1, "min": 0.0, "max": 100.0, "step":0.01}),
@@ -185,14 +119,14 @@ class SamplerEULER_ANCESTRAL_DANCING:
FUNCTION = "get_sampler"
def get_sampler(self, noise_sampler_type, eta, s_noise, leap, eta_dance):
sampler = comfy.samplers.ksampler("euler_ancestral_dancing", {"noise_sampler": noise_sampler_type, "eta": eta, "s_noise": s_noise, "leap": leap, "eta_dance": eta_dance})
sampler = comfy.samplers.ksampler("euler_ancestral_dancing", {"noise_sampler_type": noise_sampler_type, "eta": eta, "s_noise": s_noise, "leap": leap, "eta_dance": eta_dance})
return (sampler, )
class SamplerDPMPP_3M_SDE_DYN_ETA:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"noise_sampler_type": (["gaussian", "uniform", "brownian", "highres-pyramid", "perlin", "laplacian"], ),
{"noise_sampler_type": (get_noise_sampler_names(default="brownian"), ),
"eta_max": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01}),
"eta_min": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01}),
"s_noise": ("FLOAT", {"default": 1, "min": 0.0, "max": 100.0, "step":0.01}),
@@ -204,7 +138,7 @@ class SamplerDPMPP_3M_SDE_DYN_ETA:
FUNCTION = "get_sampler"
def get_sampler(self, noise_sampler_type, eta_max, eta_min, s_noise):
sampler = comfy.samplers.ksampler("dpmpp_3m_sde_dynamic_eta", {"noise_sampler": noise_sampler_type, "eta_max": eta_max, "eta_min": eta_min, "s_noise": s_noise})
sampler = comfy.samplers.ksampler("dpmpp_3m_sde_dynamic_eta", {"noise_sampler_type": noise_sampler_type, "eta_max": eta_max, "eta_min": eta_min, "s_noise": s_noise})
return (sampler, )
### Schedulers