nodes/Clybius-ComfyUI-Extra-Samplers
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clean up you sick f**k.

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Clybius
2024-02-02 09:26:22 -06:00
parent 9f08cd98a4
commit 6e56dfe04d
1 changed files with 1 additions and 50 deletions
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extra_samplers.py
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@@ -314,46 +314,6 @@ 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 mixed_noise_sampler(x): # Meant for RES
gaussian = torch.randn_like(x)
uniform = ((torch.rand_like(x) - 0.5) * 2 * 1.73)
# Calculate variances
#gaussian_variance = torch.var(gaussian, dim=(-2, -1), keepdim=True)
#uniform_variance = torch.var(uniform, dim=(-2, -1), keepdim=True)
# Determine weights based on variances
#total_variance = gaussian_variance + uniform_variance
#gaussian_weight = gaussian_variance / total_variance
#uniform_weight = uniform_variance / total_variance
#mixed_noise = gaussian * gaussian_weight + uniform * uniform_weight
#print(gaussian_weight, uniform_weight)
mixed_noise = (gaussian * uniform) / 4
# Return the final mixed noise sample
return lambda sigma, sigma_next: mixed_noise
import random
def pyramid_noise_like(x, discount=0.75):
b, c, w, h = x.shape # EDIT: w and h get over-written, rename for a different variant!
#noise_vector_magnitude = (torch.linalg.vector_norm(torch.randn_like(x), dim=(1)) + 0.0000000001)[:,None]
gauss_noise = torch.randn_like(x)
gn_mean = gauss_noise.mean() * 0
gn_std = gauss_noise.std() / 2
noise = torch.nn.functional.interpolate((torch.normal(mean=0, std=0.25, size=(b, c, w // 2, h // 2)).to(x)), size=(w, h), mode='bicubic')
noise_2 = torch.nn.functional.interpolate((torch.normal(mean=0, std=0.5, size=(b, c, w * 8, h * 8)).to(x)), size=(w, h), mode='bicubic')
noise = noise + noise_2
#gauss_mag = (torch.linalg.vector_norm(gauss_noise, dim=(1)) + 0.0000000001)[:,None]
#noise_mag = (torch.linalg.vector_norm(noise, dim=(1)) + 0.0000000001)[:,None]
#noise /= noise_mag
#noise *= gauss_mag
#noise_scaled = torch.copysign(torch.pow(torch.abs(noise / noise.max()), 0.95), noise) * 1.4
#noise = torch.copysign(torch.pow(torch.abs(noise_scaled), 0.9), noise_scaled) / 1.15
return lambda sigma, sigma_next: noise# / 2# / 1.5#torch.copysign(torch.pow(torch.abs(noise), 1.25), noise) * 2 * 1.73#.sub_(noise.mean()).div_(noise.std()) # Scaled back to roughly unit variance
# 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=0.5):
@@ -484,7 +444,7 @@ def sample_ttm_jvp(model, x, sigmas, extra_args=None, callback=None, disable=Non
return x
# 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=0.5):
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
@@ -517,15 +477,6 @@ def sample_dpmpp_dualsde_momentum(model, x, sigmas, extra_args=None, callback=No
denoisedsde_1, denoisedsde_2, denoisedsde_3 = None, None, None # new line
h_1, h_2, h_3 = None, None, None # new line
#def momentum_func(diff, velocity, timescale=1.0, offset=momentum_offset): # Diff is current diff, vel is previous diff
# if velocity is None:
# momentum_vel = diff
# #print("Setting up momentum")
# else:
# momentum_vel = momentum * (timescale - momentum_offset) * velocity + (1 - momentum * (timescale - momentum_offset)) * diff
# #print("Calculating momentum at", momentum)
# return momentum_vel
def momentum_func(diff, velocity, timescale=1.0, offset=-momentum / 2.0): # Diff is current diff, vel is previous diff
if velocity is None:
momentum_vel = diff