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Generalize SEEDS samplers (#8529)

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Restore VP algorithm for RF and refactor noise_coeffs and half-logSNR calculations
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chaObserv 2025-06-15 04:58:16 +08:00 committed by GitHub
parent 29596bd53f
commit 53e8d8193c
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1 changed files with 109 additions and 60 deletions
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169
comfy/k_diffusion/sampling.py
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@ -1,4 +1,5 @@
import math
from functools import partial
from scipy import integrate
import torch
@ -142,6 +143,33 @@ class BrownianTreeNoiseSampler:
return self.tree(t0, t1) / (t1 - t0).abs().sqrt()
def sigma_to_half_log_snr(sigma, model_sampling):
"""Convert sigma to half-logSNR log(alpha_t / sigma_t)."""
if isinstance(model_sampling, comfy.model_sampling.CONST):
# log((1 - t) / t) = log((1 - sigma) / sigma)
return sigma.logit().neg()
return sigma.log().neg()
def half_log_snr_to_sigma(half_log_snr, model_sampling):
"""Convert half-logSNR log(alpha_t / sigma_t) to sigma."""
if isinstance(model_sampling, comfy.model_sampling.CONST):
# 1 / (1 + exp(half_log_snr))
return half_log_snr.neg().sigmoid()
return half_log_snr.neg().exp()
def offset_first_sigma_for_snr(sigmas, model_sampling, percent_offset=1e-4):
"""Adjust the first sigma to avoid invalid logSNR."""
if len(sigmas) <= 1:
return sigmas
if isinstance(model_sampling, comfy.model_sampling.CONST):
if sigmas[0] >= 1:
sigmas = sigmas.clone()
sigmas[0] = model_sampling.percent_to_sigma(percent_offset)
return sigmas
@torch.no_grad()
def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
"""Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
@ -1449,12 +1477,12 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
old_denoised = denoised
return x
@torch.no_grad()
def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5):
'''
SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VE Data Prediction) stage 2
Arxiv: https://arxiv.org/abs/2305.14267
'''
"""SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2.
arXiv: https://arxiv.org/abs/2305.14267
"""
extra_args = {} if extra_args is None else extra_args
seed = extra_args.get("seed", None)
noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
@ -1462,6 +1490,11 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non
inject_noise = eta > 0 and s_noise > 0
model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
if callback is not None:
@ -1469,80 +1502,96 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non
if sigmas[i + 1] == 0:
x = denoised
else:
t, t_next = -sigmas[i].log(), -sigmas[i + 1].log()
h = t_next - t
lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
h = lambda_t - lambda_s
h_eta = h * (eta + 1)
s = t + r * h
lambda_s_1 = lambda_s + r * h
fac = 1 / (2 * r)
sigma_s = s.neg().exp()
sigma_s_1 = sigma_fn(lambda_s_1)
# alpha_t = sigma_t * exp(log(alpha_t / sigma_t)) = sigma_t * exp(lambda_t)
alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
alpha_t = sigmas[i + 1] * lambda_t.exp()
coeff_1, coeff_2 = (-r * h_eta).expm1(), (-h_eta).expm1()
if inject_noise:
# 0 < r < 1
noise_coeff_1 = (-2 * r * h * eta).expm1().neg().sqrt()
noise_coeff_2 = ((-2 * r * h * eta).expm1() - (-2 * h * eta).expm1()).sqrt()
noise_1, noise_2 = noise_sampler(sigmas[i], sigma_s), noise_sampler(sigma_s, sigmas[i + 1])
noise_coeff_2 = (-r * h * eta).exp() * (-2 * (1 - r) * h * eta).expm1().neg().sqrt()
noise_1, noise_2 = noise_sampler(sigmas[i], sigma_s_1), noise_sampler(sigma_s_1, sigmas[i + 1])
# Step 1
x_2 = (coeff_1 + 1) * x - coeff_1 * denoised
if inject_noise:
x_2 = x_2 + sigma_s * (noise_coeff_1 * noise_1) * s_noise
denoised_2 = model(x_2, sigma_s * s_in, **extra_args)
# Step 2
denoised_d = (1 - fac) * denoised + fac * denoised_2
x = (coeff_2 + 1) * x - coeff_2 * denoised_d
if inject_noise:
x = x + sigmas[i + 1] * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise
return x
@torch.no_grad()
def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r_1=1./3, r_2=2./3):
'''
SEEDS-3 - Stochastic Explicit Exponential Derivative-free Solvers (VE Data Prediction) stage 3
Arxiv: https://arxiv.org/abs/2305.14267
'''
extra_args = {} if extra_args is None else extra_args
seed = extra_args.get("seed", None)
noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
s_in = x.new_ones([x.shape[0]])
inject_noise = eta > 0 and s_noise > 0
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
if sigmas[i + 1] == 0:
x = denoised
else:
t, t_next = -sigmas[i].log(), -sigmas[i + 1].log()
h = t_next - t
h_eta = h * (eta + 1)
s_1 = t + r_1 * h
s_2 = t + r_2 * h
sigma_s_1, sigma_s_2 = s_1.neg().exp(), s_2.neg().exp()
coeff_1, coeff_2, coeff_3 = (-r_1 * h_eta).expm1(), (-r_2 * h_eta).expm1(), (-h_eta).expm1()
if inject_noise:
noise_coeff_1 = (-2 * r_1 * h * eta).expm1().neg().sqrt()
noise_coeff_2 = ((-2 * r_1 * h * eta).expm1() - (-2 * r_2 * h * eta).expm1()).sqrt()
noise_coeff_3 = ((-2 * r_2 * h * eta).expm1() - (-2 * h * eta).expm1()).sqrt()
noise_1, noise_2, noise_3 = noise_sampler(sigmas[i], sigma_s_1), noise_sampler(sigma_s_1, sigma_s_2), noise_sampler(sigma_s_2, sigmas[i + 1])
# Step 1
x_2 = (coeff_1 + 1) * x - coeff_1 * denoised
x_2 = sigma_s_1 / sigmas[i] * (-r * h * eta).exp() * x - alpha_s_1 * coeff_1 * denoised
if inject_noise:
x_2 = x_2 + sigma_s_1 * (noise_coeff_1 * noise_1) * s_noise
denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
# Step 2
x_3 = (coeff_2 + 1) * x - coeff_2 * denoised + (r_2 / r_1) * (coeff_2 / (r_2 * h_eta) + 1) * (denoised_2 - denoised)
denoised_d = (1 - fac) * denoised + fac * denoised_2
x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * coeff_2 * denoised_d
if inject_noise:
x = x + sigmas[i + 1] * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise
return x
@torch.no_grad()
def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r_1=1./3, r_2=2./3):
"""SEEDS-3 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 3.
arXiv: https://arxiv.org/abs/2305.14267
"""
extra_args = {} if extra_args is None else extra_args
seed = extra_args.get("seed", None)
noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
s_in = x.new_ones([x.shape[0]])
inject_noise = eta > 0 and s_noise > 0
model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
for i in trange(len(sigmas) - 1, disable=disable):
denoised = model(x, sigmas[i] * s_in, **extra_args)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
if sigmas[i + 1] == 0:
x = denoised
else:
lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
h = lambda_t - lambda_s
h_eta = h * (eta + 1)
lambda_s_1 = lambda_s + r_1 * h
lambda_s_2 = lambda_s + r_2 * h
sigma_s_1, sigma_s_2 = sigma_fn(lambda_s_1), sigma_fn(lambda_s_2)
# alpha_t = sigma_t * exp(log(alpha_t / sigma_t)) = sigma_t * exp(lambda_t)
alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
alpha_s_2 = sigma_s_2 * lambda_s_2.exp()
alpha_t = sigmas[i + 1] * lambda_t.exp()
coeff_1, coeff_2, coeff_3 = (-r_1 * h_eta).expm1(), (-r_2 * h_eta).expm1(), (-h_eta).expm1()
if inject_noise:
# 0 < r_1 < r_2 < 1
noise_coeff_1 = (-2 * r_1 * h * eta).expm1().neg().sqrt()
noise_coeff_2 = (-r_1 * h * eta).exp() * (-2 * (r_2 - r_1) * h * eta).expm1().neg().sqrt()
noise_coeff_3 = (-r_2 * h * eta).exp() * (-2 * (1 - r_2) * h * eta).expm1().neg().sqrt()
noise_1, noise_2, noise_3 = noise_sampler(sigmas[i], sigma_s_1), noise_sampler(sigma_s_1, sigma_s_2), noise_sampler(sigma_s_2, sigmas[i + 1])
# Step 1
x_2 = sigma_s_1 / sigmas[i] * (-r_1 * h * eta).exp() * x - alpha_s_1 * coeff_1 * denoised
if inject_noise:
x_2 = x_2 + sigma_s_1 * (noise_coeff_1 * noise_1) * s_noise
denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
# Step 2
x_3 = sigma_s_2 / sigmas[i] * (-r_2 * h * eta).exp() * x - alpha_s_2 * coeff_2 * denoised + (r_2 / r_1) * alpha_s_2 * (coeff_2 / (r_2 * h_eta) + 1) * (denoised_2 - denoised)
if inject_noise:
x_3 = x_3 + sigma_s_2 * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise
denoised_3 = model(x_3, sigma_s_2 * s_in, **extra_args)
# Step 3
x = (coeff_3 + 1) * x - coeff_3 * denoised + (1. / r_2) * (coeff_3 / h_eta + 1) * (denoised_3 - denoised)
x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * coeff_3 * denoised + (1. / r_2) * alpha_t * (coeff_3 / h_eta + 1) * (denoised_3 - denoised)
if inject_noise:
x = x + sigmas[i + 1] * (noise_coeff_3 * noise_1 + noise_coeff_2 * noise_2 + noise_coeff_1 * noise_3) * s_noise
return x