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Upgrade Supreme sampler with more options, fix some latent exploding

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Update README.md
Move samplers over into their correct category
Add Kornia as a requirement
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Clybius
2024-03-25 16:12:05 -05:00
parent 6f66795962
commit 448ecdfedd
4 changed files with 117 additions and 92 deletions
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README.md
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@@ -6,8 +6,20 @@
* Clyb 4M SDE (A modified DPMPP 3M SDE, with an added SDE, egotisticalized by yours truly)
* TTM (Thanks to Kat and Birch-San for the source implementation!)
* LCM Custom Noise (Supports different types of noise other than generic gaussian)
* DPMPP 3M SDE with Dynamic ETA (Anneals down towards a minimum eta via a cosine curve)
* Supreme (Many extra functionalities and step methods available)
### Currently included extra K-Sampling nodes:
* SamplerCustomNoise (Supports custom noises other than gaussian noise for init noise)
* SamplerCustomNoiseDuo (Same as above, but with an added High-res fix for simplicity.
* SamplerCustomNoiseDuo (Same as above, but with an added High-res fix for simplicity.)
* SamplerCustomModelMixtureDuo (Samples with custom noises, and switches between model1 and model2 every step. If you encounter vram errors, try adding/removing `--disable-smart-memory` when launching ComfyUI)
#### Supreme Sampler features:
* centralization: Subtracts mean from the denoised latent. This can lead to perceptually sharper results, though may change the perceivable brightness of the image. Conservatively defaults to **(0.02)**.
* normalization: Divides the denoised latent by the standard deviation. Can increase contrast in the image, though may hurt fidelity and coherency at high strengths. Conservatively defaults to **(0.01)**.
* edge_enhancement: Sharpens the latent, and then applies a bilateral blur, leaving the edges sharpened. Conservatively defaults to **(0.05)**.
* perphist: Adds previous denoised variable to the current denoised using perpendicular vector projection. Default of **(0)**, where higher values add the old denoised variable, and negative values subtract the old denoised variable.
* substeps: Amount of times to iterate over each step and average the results. Can be useful for obtaining higher quality at a given step count. Inspired by [ReNoise](https://arxiv.org/pdf/2403.14602v1.pdf). Default of **(2)**.
Utilizing custom sampling within ComfyUI is encouraged for these samplers!
extra_samplers.py
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@@ -3,6 +3,7 @@ import math
import torch
from torch import nn, FloatTensor
import torchsde
import kornia
from tqdm.auto import trange, tqdm
import numpy as np
@@ -794,7 +795,7 @@ def sample_dpmpp_3m_sde_dynamic_eta(model, x, sigmas, extra_args=None, callback=
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))
@torch.no_grad()
def sampler_supreme(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, eta=1.0, step_method="euler", centralization=0.02, normalization=0.01, edge_enhancement=0.5, perphist=-0.15):
def sampler_supreme(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, eta=1.0, step_method="euler", centralization=0.02, normalization=0.01, edge_enhancement=0.05, perphist=0, substeps=2):
"""
Supreme Sampler, Euler steps. Based on no paper, purely interesting thoughts.
@@ -808,10 +809,11 @@ def sampler_supreme(model, x, sigmas, extra_args=None, callback=None, disable=No
s_noise: The noise scale factor.
noise_sampler: A custom noise sampler function.
eta: Ancestral-ness.
centralization: Subtracts mean from the denoised latent, reduces edge enhancement when between 0-1.
centralization: Subtracts mean from the denoised latent.
normalization: Divides the denoised latent by the standard deviation.
edge_enhancement: Multiplies the edges by the mean using a laplacian kernel
perphist: Adds previous denoised variable to the current denoised using perpendicular vector projection
substeps: Amount of times to iterate over each step and average the results
"""
extra_args = {} if extra_args is None else extra_args
@@ -850,19 +852,11 @@ def sampler_supreme(model, x, sigmas, extra_args=None, callback=None, disable=No
eta = eta_min + 0.5 * (eta_max - eta_min) * (1 + math.cos(math.pi * progress))
return eta
def f(x, sigma):
"""Function representing the Karras ODE derivative."""
denoised = model(x, sigma * s_in, **extra_args)
return to_d(x, sigma, denoised)
old_denoised = None
for i in trange(len(sigmas) - 1, disable=disable):
# DynETA
eta = eta_schedule_cosine_annealing(i, len(sigmas))
eps_cache = {}
dpm_solver = DPMSolver(model, extra_args)
denoised = model(x, sigmas[i] * s_in, **extra_args)
def apply_enhancements(x, i, denoised, old_denoised):
if edge_enhancement != 0:
blur = (kornia.filters.joint_bilateral_blur(x, denoised, (3, 3), 0.1, (1.5, 1.5)) - x) # Blurs non-edges
denoised += (kornia.filters.unsharp_mask(denoised, (3, 3), (1.5, 1.5)) - denoised) * (sigmas[i] - sigmas[i + 1]) * edge_enhancement # Sharpens everything
denoised += blur * (sigmas[i] - sigmas[i + 1]) * edge_enhancement # Apply blur to non-edges, thus leaving edges sharpened
if centralization != 0:
denoised = centralize(denoised, centralization)
@@ -872,86 +866,103 @@ def sampler_supreme(model, x, sigmas, extra_args=None, callback=None, disable=No
if old_denoised != None and perphist != 0:
denoised = perpadd(denoised, old_denoised, x, perphist)
if old_denoised != None and edge_enhancement != 0:
lap_kern = torch.tensor([[0, -1, 0], [-1, 4, -1], [0, -1, 0]], device=denoised.device, dtype=denoised.dtype).repeat(denoised.shape[1], 1, 1, 1)
denoised = denoised + torch.conv2d(denoised, lap_kern, groups=denoised.shape[1], padding=1) * denoised.mean(dim=(1, 2, 3), keepdim=True) * sigmas[i] * edge_enhancement
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
eps = (x - denoised) / sigmas[i]
eps_cache = {'eps': eps}
return denoised
match step_method:
case "euler":
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
d = to_d(x, sigmas[i], denoised)
dt = sigma_down - sigmas[i]
renoise_weights = torch.ones(substeps, device=x.device) / substeps
x = x + d * dt
if sigmas[i + 1] > 0:
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
case "dpm_1s": # DPM Family
if callback is not None:
dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})
x, eps_cache = dpm_solver.dpm_solver_1_step(x, dpm_solver.t(sigmas[i]), dpm_solver.t(sigmas[i + 1]), eps_cache=eps_cache)
case "dpm_2s":
if callback is not None:
dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})
x, eps_cache = dpm_solver.dpm_solver_2_step(x, dpm_solver.t(sigmas[i]), dpm_solver.t(sigmas[i + 1]), eps_cache=eps_cache)
case "dpm_3s":
if callback is not None:
dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})
x, eps_cache = dpm_solver.dpm_solver_3_step(x, dpm_solver.t(sigmas[i]), dpm_solver.t(sigmas[i + 1]), eps_cache=eps_cache)
case "rk4": # Fourth-order Runge-Kutta method
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
# Calculate the derivative using the model
d = to_d(x, sigmas[i], denoised)
dt = sigma_down - sigmas[i]
orig_model = model
old_denoised = None
for i in trange(len(sigmas) - 1, disable=disable):
def model(x, sigma_s_in, **extra_args):
return apply_enhancements(x, i, orig_model(x, sigma_s_in, **extra_args), old_denoised)
# DynETA
eta = eta_schedule_cosine_annealing(i, len(sigmas))
# Runge-Kutta steps
k1 = d * dt
k2 = to_d(x + k1 / 2, sigmas[i] + dt / 2, model(x + k1 / 2, (sigmas[i] + dt / 2) * s_in, **extra_args)) * dt
k3 = to_d(x + k2 / 2, sigmas[i] + dt / 2, model(x + k2 / 2, (sigmas[i] + dt / 2) * s_in, **extra_args)) * dt
k4 = to_d(x + k3, sigmas[i] + dt, model(x + k3, (sigmas[i] + dt) * s_in, **extra_args)) * dt
dpm_solver = DPMSolver(model, extra_args)
# Update the sample
x = x + (k1 + 2 * k2 + 2 * k3 + k4) / 6
if sigmas[i + 1] > 0:
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
case "trapezoidal":
if sigmas[i + 1] > 0:
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
dt = sigmas[i + 1] - sigmas[i]
# Renoising iterations
z_avg = torch.zeros_like(x)
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
for k in range(substeps):
z_k = x
eps_cache = {}
denoised = model(z_k, sigmas[i] * s_in, **extra_args)
if callback is not None:
callback({'x': z_k, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
eps = (z_k - denoised) / sigmas[i]
eps_cache = {'eps': eps}
match step_method if sigmas[i + 1] != 0 else "euler":
case "euler":
d = to_d(z_k, sigmas[i], denoised)
dt = sigma_down - sigmas[i]
z_k = z_k + d * dt
case "dpm_1s": # DPM Family
if callback is not None:
dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})
z_k, eps_cache = dpm_solver.dpm_solver_1_step(z_k, dpm_solver.t(sigmas[i]), dpm_solver.t(sigma_down), eps_cache=eps_cache)
case "dpm_2s":
if callback is not None:
dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})
z_k, eps_cache = dpm_solver.dpm_solver_2_step(z_k, dpm_solver.t(sigmas[i]), dpm_solver.t(sigma_down), eps_cache=eps_cache)
case "dpm_3s":
if callback is not None:
dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})
z_k, eps_cache = dpm_solver.dpm_solver_3_step(z_k, dpm_solver.t(sigmas[i]), dpm_solver.t(sigma_down), eps_cache=eps_cache)
case "rk4": # Fourth-order Runge-Kutta method
# Calculate the derivative using the model
d_i = to_d(x, sigmas[i], denoised)
d = to_d(z_k, sigmas[i], denoised)
dt = sigma_down - sigmas[i]
# Predict the sample at the next sigma using Euler step
x_pred = x + d_i * dt
# Runge-Kutta steps
k1 = d * dt
k2 = to_d(z_k + k1 / 2, sigmas[i] + dt / 2, model(z_k + k1 / 2, (sigmas[i] + dt / 2) * s_in, **extra_args)) * dt
k3 = to_d(z_k + k2 / 2, sigmas[i] + dt / 2, model(z_k + k2 / 2, (sigmas[i] + dt / 2) * s_in, **extra_args)) * dt
k4 = to_d(z_k + k3, sigmas[i] + dt, model(z_k + k3, (sigmas[i] + dt) * s_in, **extra_args)) * dt
# Denoised sample at the next sigma
denoised_i_plus_1 = model(x_pred, sigmas[i + 1] * s_in, **extra_args)
# Update the sample
z_k = z_k + (k1 + 2 * k2 + 2 * k3 + k4) / 6
case "trapezoidal":
if sigmas[i + 1] > 0:
dt = sigmas[i + 1] - sigmas[i]
# Calculate the derivative at the next sigma
d_i_plus_1 = to_d(x_pred, sigmas[i + 1], denoised_i_plus_1)
# Calculate the derivative using the model
d_i = to_d(z_k, sigmas[i], denoised)
#if callback is not None:
# callback({'x': x, 'i': i, 'sigma': sigmas[i + 1], 'denoised': denoised_i_plus_1})
dt_2 = sigma_down - sigmas[i]
# Update the sample using the Trapezoidal rule
x = x + dt_2 * (d_i + d_i_plus_1) / 2
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
else:
x = denoised
# Predict the sample at the next sigma using Euler step
x_pred = z_k + d_i * dt
# Denoised sample at the next sigma
denoised_i_plus_1 = model(x_pred, sigmas[i + 1] * s_in, **extra_args)
# Calculate the derivative at the next sigma
d_i_plus_1 = to_d(x_pred, sigmas[i + 1], denoised_i_plus_1)
dt_2 = sigma_down - sigmas[i]
# Update the sample using the Trapezoidal rule
z_k = z_k + dt_2 * (d_i + d_i_plus_1) / 2
else:
z_k = denoised
z_avg += renoise_weights[k] * z_k
if sigmas[i + 1] > 0: # Random noise for variance on ancestral samplers
z_k = z_k + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
x = z_avg
if sigmas[i + 1] > 0:
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
old_denoised = denoised
return x
def sample_supreme(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler_type="gaussian", noise_sampler=None, eta=1.0, step_method="euler", centralization=0.02, normalization=0.01, edge_enhancement=0.5, perphist=-0.15):
return sampler_supreme(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler or get_noise_sampler(x, sigmas, noise_sampler_type, noise_sampler, extra_args), eta=eta, step_method=step_method, centralization=centralization, normalization=normalization, edge_enhancement=edge_enhancement, perphist=perphist)
def sample_supreme(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler_type="gaussian", noise_sampler=None, eta=1.0, step_method="euler", centralization=0.02, normalization=0.01, edge_enhancement=0.05, perphist=0, substeps=2):
return sampler_supreme(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler or get_noise_sampler(x, sigmas, noise_sampler_type, noise_sampler, extra_args), eta=eta, step_method=step_method, centralization=centralization, normalization=normalization, edge_enhancement=edge_enhancement, perphist=perphist, substeps=substeps)
# Add your personal samplers below here, just for formatting purposes ;3
nodes.py
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@@ -20,7 +20,7 @@ class SamplerRES_MOMENTUMIZED:
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling"
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
@@ -40,7 +40,7 @@ class SamplerDPMPP_DUALSDE_MOMENTUMIZED:
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling"
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
@@ -58,7 +58,7 @@ class SamplerTTM:
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling"
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
@@ -75,7 +75,7 @@ class SamplerLCMCustom:
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling"
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
@@ -94,7 +94,7 @@ class SamplerCLYB_4M_SDE_MOMENTUMIZED:
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling"
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
@@ -114,7 +114,7 @@ class SamplerEULER_ANCESTRAL_DANCING:
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling"
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
@@ -133,7 +133,7 @@ class SamplerDPMPP_3M_SDE_DYN_ETA:
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling"
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
@@ -150,18 +150,19 @@ class SamplerSUPREME:
"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01}),
"centralization": ("FLOAT", {"default": 0.02, "min": -1.0, "max": 1.0, "step":0.01}),
"normalization": ("FLOAT", {"default": 0.01, "min": -1.0, "max": 1.0, "step":0.01}),
"edge_enhancement": ("FLOAT", {"default": 0.5, "min": -100.0, "max": 100.0, "step":0.01}),
"perphist": ("FLOAT", {"default": -0.15, "min": -5.0, "max": 5.0, "step":0.01}),
"edge_enhancement": ("FLOAT", {"default": 0.05, "min": -100.0, "max": 100.0, "step":0.01}),
"perphist": ("FLOAT", {"default": 0, "min": -5.0, "max": 5.0, "step":0.01}),
"substeps": ("INT", {"default": 2, "min": 1, "max": 100, "step":1}),
"s_noise": ("FLOAT", {"default": 1, "min": 0.0, "max": 100.0, "step":0.01}),
}
}
RETURN_TYPES = ("SAMPLER",)
CATEGORY = "sampling/custom_sampling"
CATEGORY = "sampling/custom_sampling/samplers"
FUNCTION = "get_sampler"
def get_sampler(self, noise_sampler_type, step_method, eta, centralization, normalization, edge_enhancement, perphist, s_noise):
sampler = comfy.samplers.ksampler("supreme", {"noise_sampler_type": noise_sampler_type, "step_method": step_method, "eta": eta, "centralization": centralization, "normalization": normalization, "edge_enhancement": edge_enhancement, "perphist": perphist, "s_noise": s_noise})
def get_sampler(self, noise_sampler_type, step_method, eta, centralization, normalization, edge_enhancement, perphist, substeps, s_noise):
sampler = comfy.samplers.ksampler("supreme", {"noise_sampler_type": noise_sampler_type, "step_method": step_method, "eta": eta, "centralization": centralization, "normalization": normalization, "edge_enhancement": edge_enhancement, "perphist": perphist, "substeps": substeps, "s_noise": s_noise})
return (sampler, )
### Schedulers
requirements.txt
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@@ -0,0 +1 @@
kornia