mirror of
https://github.com/comfyanonymous/ComfyUI.git
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1762 lines
78 KiB
Python
1762 lines
78 KiB
Python
import math
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from functools import partial
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from scipy import integrate
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import torch
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from torch import nn
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import torchsde
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from tqdm.auto import trange, tqdm
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from . import utils
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from . import deis
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from . import sa_solver
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import comfy.model_patcher
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import comfy.model_sampling
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def append_zero(x):
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return torch.cat([x, x.new_zeros([1])])
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def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
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"""Constructs the noise schedule of Karras et al. (2022)."""
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ramp = torch.linspace(0, 1, n, device=device)
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min_inv_rho = sigma_min ** (1 / rho)
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max_inv_rho = sigma_max ** (1 / rho)
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sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
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return append_zero(sigmas).to(device)
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def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'):
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"""Constructs an exponential noise schedule."""
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sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp()
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return append_zero(sigmas)
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def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'):
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"""Constructs an polynomial in log sigma noise schedule."""
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ramp = torch.linspace(1, 0, n, device=device) ** rho
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sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min))
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return append_zero(sigmas)
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def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
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"""Constructs a continuous VP noise schedule."""
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t = torch.linspace(1, eps_s, n, device=device)
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sigmas = torch.sqrt(torch.special.expm1(beta_d * t ** 2 / 2 + beta_min * t))
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return append_zero(sigmas)
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def get_sigmas_laplace(n, sigma_min, sigma_max, mu=0., beta=0.5, device='cpu'):
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"""Constructs the noise schedule proposed by Tiankai et al. (2024). """
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epsilon = 1e-5 # avoid log(0)
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x = torch.linspace(0, 1, n, device=device)
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clamp = lambda x: torch.clamp(x, min=sigma_min, max=sigma_max)
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lmb = mu - beta * torch.sign(0.5-x) * torch.log(1 - 2 * torch.abs(0.5-x) + epsilon)
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sigmas = clamp(torch.exp(lmb))
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return sigmas
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def to_d(x, sigma, denoised):
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"""Converts a denoiser output to a Karras ODE derivative."""
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return (x - denoised) / utils.append_dims(sigma, x.ndim)
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def get_ancestral_step(sigma_from, sigma_to, eta=1.):
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"""Calculates the noise level (sigma_down) to step down to and the amount
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of noise to add (sigma_up) when doing an ancestral sampling step."""
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if not eta:
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return sigma_to, 0.
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sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5)
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sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
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return sigma_down, sigma_up
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def default_noise_sampler(x, seed=None):
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if seed is not None:
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generator = torch.Generator(device=x.device)
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generator.manual_seed(seed)
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else:
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generator = None
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return lambda sigma, sigma_next: torch.randn(x.size(), dtype=x.dtype, layout=x.layout, device=x.device, generator=generator)
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class BatchedBrownianTree:
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"""A wrapper around torchsde.BrownianTree that enables batches of entropy."""
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def __init__(self, x, t0, t1, seed=None, **kwargs):
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self.cpu_tree = True
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if "cpu" in kwargs:
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self.cpu_tree = kwargs.pop("cpu")
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t0, t1, self.sign = self.sort(t0, t1)
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w0 = kwargs.get('w0', torch.zeros_like(x))
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if seed is None:
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seed = torch.randint(0, 2 ** 63 - 1, []).item()
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self.batched = True
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try:
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assert len(seed) == x.shape[0]
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w0 = w0[0]
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except TypeError:
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seed = [seed]
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self.batched = False
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if self.cpu_tree:
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self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
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else:
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self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
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@staticmethod
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def sort(a, b):
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return (a, b, 1) if a < b else (b, a, -1)
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def __call__(self, t0, t1):
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t0, t1, sign = self.sort(t0, t1)
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if self.cpu_tree:
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w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign)
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else:
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w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
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return w if self.batched else w[0]
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class BrownianTreeNoiseSampler:
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"""A noise sampler backed by a torchsde.BrownianTree.
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Args:
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x (Tensor): The tensor whose shape, device and dtype to use to generate
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random samples.
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sigma_min (float): The low end of the valid interval.
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sigma_max (float): The high end of the valid interval.
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seed (int or List[int]): The random seed. If a list of seeds is
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supplied instead of a single integer, then the noise sampler will
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use one BrownianTree per batch item, each with its own seed.
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transform (callable): A function that maps sigma to the sampler's
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internal timestep.
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"""
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def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False):
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self.transform = transform
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t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max))
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self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu)
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def __call__(self, sigma, sigma_next):
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t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))
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return self.tree(t0, t1) / (t1 - t0).abs().sqrt()
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def sigma_to_half_log_snr(sigma, model_sampling):
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"""Convert sigma to half-logSNR log(alpha_t / sigma_t)."""
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if isinstance(model_sampling, comfy.model_sampling.CONST):
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# log((1 - t) / t) = log((1 - sigma) / sigma)
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return sigma.logit().neg()
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return sigma.log().neg()
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def half_log_snr_to_sigma(half_log_snr, model_sampling):
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"""Convert half-logSNR log(alpha_t / sigma_t) to sigma."""
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if isinstance(model_sampling, comfy.model_sampling.CONST):
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# 1 / (1 + exp(half_log_snr))
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return half_log_snr.neg().sigmoid()
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return half_log_snr.neg().exp()
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def offset_first_sigma_for_snr(sigmas, model_sampling, percent_offset=1e-4):
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"""Adjust the first sigma to avoid invalid logSNR."""
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if len(sigmas) <= 1:
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return sigmas
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if isinstance(model_sampling, comfy.model_sampling.CONST):
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if sigmas[0] >= 1:
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sigmas = sigmas.clone()
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sigmas[0] = model_sampling.percent_to_sigma(percent_offset)
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return sigmas
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@torch.no_grad()
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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.):
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"""Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
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extra_args = {} if extra_args is None else extra_args
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s_in = x.new_ones([x.shape[0]])
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for i in trange(len(sigmas) - 1, disable=disable):
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if s_churn > 0:
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gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
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sigma_hat = sigmas[i] * (gamma + 1)
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else:
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gamma = 0
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sigma_hat = sigmas[i]
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if gamma > 0:
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eps = torch.randn_like(x) * s_noise
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x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
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denoised = model(x, sigma_hat * s_in, **extra_args)
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d = to_d(x, sigma_hat, denoised)
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if callback is not None:
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callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
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dt = sigmas[i + 1] - sigma_hat
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# Euler method
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x = x + d * dt
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return x
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@torch.no_grad()
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def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
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if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):
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return sample_euler_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler)
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"""Ancestral sampling with Euler method steps."""
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extra_args = {} if extra_args is None else extra_args
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seed = extra_args.get("seed", None)
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noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
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s_in = x.new_ones([x.shape[0]])
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for i in trange(len(sigmas) - 1, disable=disable):
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denoised = model(x, sigmas[i] * s_in, **extra_args)
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sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
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if callback is not None:
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callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
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if sigma_down == 0:
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x = denoised
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else:
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d = to_d(x, sigmas[i], denoised)
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# Euler method
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dt = sigma_down - sigmas[i]
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x = x + d * dt + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
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return x
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@torch.no_grad()
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def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1., noise_sampler=None):
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"""Ancestral sampling with Euler method steps."""
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extra_args = {} if extra_args is None else extra_args
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seed = extra_args.get("seed", None)
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noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
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s_in = x.new_ones([x.shape[0]])
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for i in trange(len(sigmas) - 1, disable=disable):
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denoised = model(x, sigmas[i] * s_in, **extra_args)
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# sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
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if callback is not None:
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callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
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if sigmas[i + 1] == 0:
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x = denoised
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else:
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downstep_ratio = 1 + (sigmas[i + 1] / sigmas[i] - 1) * eta
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sigma_down = sigmas[i + 1] * downstep_ratio
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alpha_ip1 = 1 - sigmas[i + 1]
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alpha_down = 1 - sigma_down
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renoise_coeff = (sigmas[i + 1]**2 - sigma_down**2 * alpha_ip1**2 / alpha_down**2)**0.5
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# Euler method
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sigma_down_i_ratio = sigma_down / sigmas[i]
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x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * denoised
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if eta > 0:
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x = (alpha_ip1 / alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff
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return x
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@torch.no_grad()
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def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
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"""Implements Algorithm 2 (Heun steps) from Karras et al. (2022)."""
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extra_args = {} if extra_args is None else extra_args
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s_in = x.new_ones([x.shape[0]])
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for i in trange(len(sigmas) - 1, disable=disable):
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if s_churn > 0:
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gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
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sigma_hat = sigmas[i] * (gamma + 1)
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else:
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gamma = 0
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sigma_hat = sigmas[i]
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sigma_hat = sigmas[i] * (gamma + 1)
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if gamma > 0:
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eps = torch.randn_like(x) * s_noise
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x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
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denoised = model(x, sigma_hat * s_in, **extra_args)
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d = to_d(x, sigma_hat, denoised)
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if callback is not None:
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callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
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dt = sigmas[i + 1] - sigma_hat
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if sigmas[i + 1] == 0:
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# Euler method
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x = x + d * dt
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else:
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# Heun's method
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x_2 = x + d * dt
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denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
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d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
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d_prime = (d + d_2) / 2
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x = x + d_prime * dt
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return x
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@torch.no_grad()
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def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
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"""A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022)."""
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extra_args = {} if extra_args is None else extra_args
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s_in = x.new_ones([x.shape[0]])
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for i in trange(len(sigmas) - 1, disable=disable):
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if s_churn > 0:
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gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
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sigma_hat = sigmas[i] * (gamma + 1)
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else:
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gamma = 0
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sigma_hat = sigmas[i]
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if gamma > 0:
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eps = torch.randn_like(x) * s_noise
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x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
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denoised = model(x, sigma_hat * s_in, **extra_args)
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d = to_d(x, sigma_hat, denoised)
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if callback is not None:
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callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
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if sigmas[i + 1] == 0:
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# Euler method
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dt = sigmas[i + 1] - sigma_hat
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x = x + d * dt
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else:
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# DPM-Solver-2
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sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp()
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dt_1 = sigma_mid - sigma_hat
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dt_2 = sigmas[i + 1] - sigma_hat
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x_2 = x + d * dt_1
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denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
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d_2 = to_d(x_2, sigma_mid, denoised_2)
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x = x + d_2 * dt_2
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return x
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@torch.no_grad()
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def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
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if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):
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return sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler)
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"""Ancestral sampling with DPM-Solver second-order steps."""
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extra_args = {} if extra_args is None else extra_args
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seed = extra_args.get("seed", None)
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noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
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s_in = x.new_ones([x.shape[0]])
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for i in trange(len(sigmas) - 1, disable=disable):
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denoised = model(x, sigmas[i] * s_in, **extra_args)
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sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
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if callback is not None:
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callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
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d = to_d(x, sigmas[i], denoised)
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if sigma_down == 0:
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# Euler method
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dt = sigma_down - sigmas[i]
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x = x + d * dt
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else:
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# DPM-Solver-2
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sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp()
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dt_1 = sigma_mid - sigmas[i]
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dt_2 = sigma_down - sigmas[i]
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x_2 = x + d * dt_1
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denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
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d_2 = to_d(x_2, sigma_mid, denoised_2)
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x = x + d_2 * dt_2
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x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
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return x
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@torch.no_grad()
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def sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
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"""Ancestral sampling with DPM-Solver second-order steps."""
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extra_args = {} if extra_args is None else extra_args
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seed = extra_args.get("seed", None)
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noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
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s_in = x.new_ones([x.shape[0]])
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for i in trange(len(sigmas) - 1, disable=disable):
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denoised = model(x, sigmas[i] * s_in, **extra_args)
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downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta
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sigma_down = sigmas[i+1] * downstep_ratio
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alpha_ip1 = 1 - sigmas[i+1]
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alpha_down = 1 - sigma_down
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renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5
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if callback is not None:
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callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
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d = to_d(x, sigmas[i], denoised)
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if sigma_down == 0:
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# Euler method
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dt = sigma_down - sigmas[i]
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x = x + d * dt
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else:
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# DPM-Solver-2
|
||
sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp()
|
||
dt_1 = sigma_mid - sigmas[i]
|
||
dt_2 = sigma_down - sigmas[i]
|
||
x_2 = x + d * dt_1
|
||
denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
|
||
d_2 = to_d(x_2, sigma_mid, denoised_2)
|
||
x = x + d_2 * dt_2
|
||
x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff
|
||
return x
|
||
|
||
def linear_multistep_coeff(order, t, i, j):
|
||
if order - 1 > i:
|
||
raise ValueError(f'Order {order} too high for step {i}')
|
||
def fn(tau):
|
||
prod = 1.
|
||
for k in range(order):
|
||
if j == k:
|
||
continue
|
||
prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
|
||
return prod
|
||
return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0]
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4):
|
||
extra_args = {} if extra_args is None else extra_args
|
||
s_in = x.new_ones([x.shape[0]])
|
||
sigmas_cpu = sigmas.detach().cpu().numpy()
|
||
ds = []
|
||
for i in trange(len(sigmas) - 1, disable=disable):
|
||
denoised = model(x, sigmas[i] * s_in, **extra_args)
|
||
d = to_d(x, sigmas[i], denoised)
|
||
ds.append(d)
|
||
if len(ds) > order:
|
||
ds.pop(0)
|
||
if callback is not None:
|
||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
|
||
if sigmas[i + 1] == 0:
|
||
# Denoising step
|
||
x = denoised
|
||
else:
|
||
cur_order = min(i + 1, order)
|
||
coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)]
|
||
x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
|
||
return x
|
||
|
||
|
||
class PIDStepSizeController:
|
||
"""A PID controller for ODE adaptive step size control."""
|
||
def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8):
|
||
self.h = h
|
||
self.b1 = (pcoeff + icoeff + dcoeff) / order
|
||
self.b2 = -(pcoeff + 2 * dcoeff) / order
|
||
self.b3 = dcoeff / order
|
||
self.accept_safety = accept_safety
|
||
self.eps = eps
|
||
self.errs = []
|
||
|
||
def limiter(self, x):
|
||
return 1 + math.atan(x - 1)
|
||
|
||
def propose_step(self, error):
|
||
inv_error = 1 / (float(error) + self.eps)
|
||
if not self.errs:
|
||
self.errs = [inv_error, inv_error, inv_error]
|
||
self.errs[0] = inv_error
|
||
factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3
|
||
factor = self.limiter(factor)
|
||
accept = factor >= self.accept_safety
|
||
if accept:
|
||
self.errs[2] = self.errs[1]
|
||
self.errs[1] = self.errs[0]
|
||
self.h *= factor
|
||
return accept
|
||
|
||
|
||
class DPMSolver(nn.Module):
|
||
"""DPM-Solver. See https://arxiv.org/abs/2206.00927."""
|
||
|
||
def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None):
|
||
super().__init__()
|
||
self.model = model
|
||
self.extra_args = {} if extra_args is None else extra_args
|
||
self.eps_callback = eps_callback
|
||
self.info_callback = info_callback
|
||
|
||
def t(self, sigma):
|
||
return -sigma.log()
|
||
|
||
def sigma(self, t):
|
||
return t.neg().exp()
|
||
|
||
def eps(self, eps_cache, key, x, t, *args, **kwargs):
|
||
if key in eps_cache:
|
||
return eps_cache[key], eps_cache
|
||
sigma = self.sigma(t) * x.new_ones([x.shape[0]])
|
||
eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t)
|
||
if self.eps_callback is not None:
|
||
self.eps_callback()
|
||
return eps, {key: eps, **eps_cache}
|
||
|
||
def dpm_solver_1_step(self, x, t, t_next, eps_cache=None):
|
||
eps_cache = {} if eps_cache is None else eps_cache
|
||
h = t_next - t
|
||
eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
|
||
x_1 = x - self.sigma(t_next) * h.expm1() * eps
|
||
return x_1, eps_cache
|
||
|
||
def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None):
|
||
eps_cache = {} if eps_cache is None else eps_cache
|
||
h = t_next - t
|
||
eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
|
||
s1 = t + r1 * h
|
||
u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps
|
||
eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1)
|
||
x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps)
|
||
return x_2, eps_cache
|
||
|
||
def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None):
|
||
eps_cache = {} if eps_cache is None else eps_cache
|
||
h = t_next - t
|
||
eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
|
||
s1 = t + r1 * h
|
||
s2 = t + r2 * h
|
||
u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps
|
||
eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1)
|
||
u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps)
|
||
eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2)
|
||
x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps)
|
||
return x_3, eps_cache
|
||
|
||
def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None):
|
||
noise_sampler = default_noise_sampler(x, seed=self.extra_args.get("seed", None)) if noise_sampler is None else noise_sampler
|
||
if not t_end > t_start and eta:
|
||
raise ValueError('eta must be 0 for reverse sampling')
|
||
|
||
m = math.floor(nfe / 3) + 1
|
||
ts = torch.linspace(t_start, t_end, m + 1, device=x.device)
|
||
|
||
if nfe % 3 == 0:
|
||
orders = [3] * (m - 2) + [2, 1]
|
||
else:
|
||
orders = [3] * (m - 1) + [nfe % 3]
|
||
|
||
for i in range(len(orders)):
|
||
eps_cache = {}
|
||
t, t_next = ts[i], ts[i + 1]
|
||
if eta:
|
||
sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta)
|
||
t_next_ = torch.minimum(t_end, self.t(sd))
|
||
su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5
|
||
else:
|
||
t_next_, su = t_next, 0.
|
||
|
||
eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
|
||
denoised = x - self.sigma(t) * eps
|
||
if self.info_callback is not None:
|
||
self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised})
|
||
|
||
if orders[i] == 1:
|
||
x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache)
|
||
elif orders[i] == 2:
|
||
x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache)
|
||
else:
|
||
x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache)
|
||
|
||
x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next))
|
||
|
||
return x
|
||
|
||
def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None):
|
||
noise_sampler = default_noise_sampler(x, seed=self.extra_args.get("seed", None)) if noise_sampler is None else noise_sampler
|
||
if order not in {2, 3}:
|
||
raise ValueError('order should be 2 or 3')
|
||
forward = t_end > t_start
|
||
if not forward and eta:
|
||
raise ValueError('eta must be 0 for reverse sampling')
|
||
h_init = abs(h_init) * (1 if forward else -1)
|
||
atol = torch.tensor(atol)
|
||
rtol = torch.tensor(rtol)
|
||
s = t_start
|
||
x_prev = x
|
||
accept = True
|
||
pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety)
|
||
info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0}
|
||
|
||
while s < t_end - 1e-5 if forward else s > t_end + 1e-5:
|
||
eps_cache = {}
|
||
t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h)
|
||
if eta:
|
||
sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta)
|
||
t_ = torch.minimum(t_end, self.t(sd))
|
||
su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5
|
||
else:
|
||
t_, su = t, 0.
|
||
|
||
eps, eps_cache = self.eps(eps_cache, 'eps', x, s)
|
||
denoised = x - self.sigma(s) * eps
|
||
|
||
if order == 2:
|
||
x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache)
|
||
x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache)
|
||
else:
|
||
x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache)
|
||
x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache)
|
||
delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs()))
|
||
error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5
|
||
accept = pid.propose_step(error)
|
||
if accept:
|
||
x_prev = x_low
|
||
x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t))
|
||
s = t
|
||
info['n_accept'] += 1
|
||
else:
|
||
info['n_reject'] += 1
|
||
info['nfe'] += order
|
||
info['steps'] += 1
|
||
|
||
if self.info_callback is not None:
|
||
self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info})
|
||
|
||
return x, info
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None):
|
||
"""DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927."""
|
||
if sigma_min <= 0 or sigma_max <= 0:
|
||
raise ValueError('sigma_min and sigma_max must not be 0')
|
||
with tqdm(total=n, disable=disable) as pbar:
|
||
dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update)
|
||
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})
|
||
return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler)
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False):
|
||
"""DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927."""
|
||
if sigma_min <= 0 or sigma_max <= 0:
|
||
raise ValueError('sigma_min and sigma_max must not be 0')
|
||
with tqdm(disable=disable) as pbar:
|
||
dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update)
|
||
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, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler)
|
||
if return_info:
|
||
return x, info
|
||
return x
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
|
||
if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):
|
||
return sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler)
|
||
|
||
"""Ancestral sampling with DPM-Solver++(2S) second-order steps."""
|
||
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]])
|
||
sigma_fn = lambda t: t.neg().exp()
|
||
t_fn = lambda sigma: sigma.log().neg()
|
||
|
||
for i in trange(len(sigmas) - 1, disable=disable):
|
||
denoised = model(x, sigmas[i] * s_in, **extra_args)
|
||
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
|
||
if callback is not None:
|
||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
|
||
if sigma_down == 0:
|
||
# Euler method
|
||
d = to_d(x, sigmas[i], denoised)
|
||
dt = sigma_down - sigmas[i]
|
||
x = x + d * dt
|
||
else:
|
||
# DPM-Solver++(2S)
|
||
t, t_next = t_fn(sigmas[i]), t_fn(sigma_down)
|
||
r = 1 / 2
|
||
h = t_next - t
|
||
s = t + r * h
|
||
x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised
|
||
denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
|
||
x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2
|
||
# Noise addition
|
||
if sigmas[i + 1] > 0:
|
||
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
|
||
return x
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
|
||
"""Ancestral sampling with DPM-Solver++(2S) second-order steps."""
|
||
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]])
|
||
sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1
|
||
lambda_fn = lambda sigma: ((1-sigma)/sigma).log()
|
||
|
||
# logged_x = x.unsqueeze(0)
|
||
|
||
for i in trange(len(sigmas) - 1, disable=disable):
|
||
denoised = model(x, sigmas[i] * s_in, **extra_args)
|
||
downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta
|
||
sigma_down = sigmas[i+1] * downstep_ratio
|
||
alpha_ip1 = 1 - sigmas[i+1]
|
||
alpha_down = 1 - sigma_down
|
||
renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5
|
||
# sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
|
||
if callback is not None:
|
||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
|
||
if sigmas[i + 1] == 0:
|
||
# Euler method
|
||
d = to_d(x, sigmas[i], denoised)
|
||
dt = sigma_down - sigmas[i]
|
||
x = x + d * dt
|
||
else:
|
||
# DPM-Solver++(2S)
|
||
if sigmas[i] == 1.0:
|
||
sigma_s = 0.9999
|
||
else:
|
||
t_i, t_down = lambda_fn(sigmas[i]), lambda_fn(sigma_down)
|
||
r = 1 / 2
|
||
h = t_down - t_i
|
||
s = t_i + r * h
|
||
sigma_s = sigma_fn(s)
|
||
# sigma_s = sigmas[i+1]
|
||
sigma_s_i_ratio = sigma_s / sigmas[i]
|
||
u = sigma_s_i_ratio * x + (1 - sigma_s_i_ratio) * denoised
|
||
D_i = model(u, sigma_s * s_in, **extra_args)
|
||
sigma_down_i_ratio = sigma_down / sigmas[i]
|
||
x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * D_i
|
||
# print("sigma_i", sigmas[i], "sigma_ip1", sigmas[i+1],"sigma_down", sigma_down, "sigma_down_i_ratio", sigma_down_i_ratio, "sigma_s_i_ratio", sigma_s_i_ratio, "renoise_coeff", renoise_coeff)
|
||
# Noise addition
|
||
if sigmas[i + 1] > 0 and eta > 0:
|
||
x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff
|
||
# logged_x = torch.cat((logged_x, x.unsqueeze(0)), dim=0)
|
||
return x
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
|
||
"""DPM-Solver++ (stochastic)."""
|
||
if len(sigmas) <= 1:
|
||
return x
|
||
|
||
extra_args = {} if extra_args is None else extra_args
|
||
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
|
||
seed = extra_args.get("seed", None)
|
||
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
|
||
s_in = x.new_ones([x.shape[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:
|
||
# Denoising step
|
||
x = denoised
|
||
else:
|
||
# DPM-Solver++
|
||
lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
|
||
h = lambda_t - lambda_s
|
||
lambda_s_1 = lambda_s + r * h
|
||
fac = 1 / (2 * r)
|
||
|
||
sigma_s_1 = sigma_fn(lambda_s_1)
|
||
|
||
alpha_s = sigmas[i] * lambda_s.exp()
|
||
alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
|
||
alpha_t = sigmas[i + 1] * lambda_t.exp()
|
||
|
||
# Step 1
|
||
sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_s_1.neg().exp(), eta)
|
||
lambda_s_1_ = sd.log().neg()
|
||
h_ = lambda_s_1_ - lambda_s
|
||
x_2 = (alpha_s_1 / alpha_s) * (-h_).exp() * x - alpha_s_1 * (-h_).expm1() * denoised
|
||
if eta > 0 and s_noise > 0:
|
||
x_2 = x_2 + alpha_s_1 * noise_sampler(sigmas[i], sigma_s_1) * s_noise * su
|
||
denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
|
||
|
||
# Step 2
|
||
sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_t.neg().exp(), eta)
|
||
lambda_t_ = sd.log().neg()
|
||
h_ = lambda_t_ - lambda_s
|
||
denoised_d = (1 - fac) * denoised + fac * denoised_2
|
||
x = (alpha_t / alpha_s) * (-h_).exp() * x - alpha_t * (-h_).expm1() * denoised_d
|
||
if eta > 0 and s_noise > 0:
|
||
x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * su
|
||
return x
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):
|
||
"""DPM-Solver++(2M)."""
|
||
extra_args = {} if extra_args is None else extra_args
|
||
s_in = x.new_ones([x.shape[0]])
|
||
sigma_fn = lambda t: t.neg().exp()
|
||
t_fn = lambda sigma: sigma.log().neg()
|
||
old_denoised = None
|
||
|
||
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})
|
||
t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
|
||
h = t_next - t
|
||
if old_denoised is None or sigmas[i + 1] == 0:
|
||
x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised
|
||
else:
|
||
h_last = t - t_fn(sigmas[i - 1])
|
||
r = h_last / h
|
||
denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
|
||
x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
|
||
old_denoised = denoised
|
||
return x
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
|
||
"""DPM-Solver++(2M) SDE."""
|
||
if len(sigmas) <= 1:
|
||
return x
|
||
|
||
if solver_type not in {'heun', 'midpoint'}:
|
||
raise ValueError('solver_type must be \'heun\' or \'midpoint\'')
|
||
|
||
extra_args = {} if extra_args is None else extra_args
|
||
seed = extra_args.get("seed", None)
|
||
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
|
||
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
|
||
s_in = x.new_ones([x.shape[0]])
|
||
|
||
model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
|
||
lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
|
||
sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
|
||
|
||
old_denoised = None
|
||
h, h_last = None, None
|
||
|
||
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:
|
||
# Denoising step
|
||
x = denoised
|
||
else:
|
||
# DPM-Solver++(2M) SDE
|
||
lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
|
||
h = lambda_t - lambda_s
|
||
h_eta = h * (eta + 1)
|
||
|
||
alpha_t = sigmas[i + 1] * lambda_t.exp()
|
||
|
||
x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised
|
||
|
||
if old_denoised is not None:
|
||
r = h_last / h
|
||
if solver_type == 'heun':
|
||
x = x + alpha_t * ((-h_eta).expm1().neg() / (-h_eta) + 1) * (1 / r) * (denoised - old_denoised)
|
||
elif solver_type == 'midpoint':
|
||
x = x + 0.5 * alpha_t * (-h_eta).expm1().neg() * (1 / r) * (denoised - old_denoised)
|
||
|
||
if eta > 0 and s_noise > 0:
|
||
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
|
||
|
||
old_denoised = denoised
|
||
h_last = h
|
||
return x
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
|
||
"""DPM-Solver++(3M) SDE."""
|
||
|
||
if len(sigmas) <= 1:
|
||
return x
|
||
|
||
extra_args = {} if extra_args is None else extra_args
|
||
seed = extra_args.get("seed", None)
|
||
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
|
||
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
|
||
s_in = x.new_ones([x.shape[0]])
|
||
|
||
model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
|
||
lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
|
||
sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
|
||
|
||
denoised_1, denoised_2 = None, None
|
||
h, h_1, h_2 = None, None, None
|
||
|
||
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:
|
||
# Denoising step
|
||
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)
|
||
|
||
alpha_t = sigmas[i + 1] * lambda_t.exp()
|
||
|
||
x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised
|
||
|
||
if h_2 is not None:
|
||
# DPM-Solver++(3M) SDE
|
||
r0 = h_1 / h
|
||
r1 = h_2 / h
|
||
d1_0 = (denoised - denoised_1) / r0
|
||
d1_1 = (denoised_1 - denoised_2) / r1
|
||
d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1)
|
||
d2 = (d1_0 - d1_1) / (r0 + r1)
|
||
phi_2 = h_eta.neg().expm1() / h_eta + 1
|
||
phi_3 = phi_2 / h_eta - 0.5
|
||
x = x + (alpha_t * phi_2) * d1 - (alpha_t * phi_3) * d2
|
||
elif h_1 is not None:
|
||
# DPM-Solver++(2M) SDE
|
||
r = h_1 / h
|
||
d = (denoised - denoised_1) / r
|
||
phi_2 = h_eta.neg().expm1() / h_eta + 1
|
||
x = x + (alpha_t * phi_2) * d
|
||
|
||
if eta > 0 and s_noise > 0:
|
||
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
|
||
|
||
denoised_1, denoised_2 = denoised, denoised_1
|
||
h_1, h_2 = h, h_1
|
||
return x
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
|
||
if len(sigmas) <= 1:
|
||
return x
|
||
extra_args = {} if extra_args is None else extra_args
|
||
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
|
||
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
|
||
return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
|
||
if len(sigmas) <= 1:
|
||
return x
|
||
extra_args = {} if extra_args is None else extra_args
|
||
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
|
||
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
|
||
return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
|
||
if len(sigmas) <= 1:
|
||
return x
|
||
extra_args = {} if extra_args is None else extra_args
|
||
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
|
||
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
|
||
return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r)
|
||
|
||
|
||
def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler):
|
||
alpha_cumprod = 1 / ((sigma * sigma) + 1)
|
||
alpha_cumprod_prev = 1 / ((sigma_prev * sigma_prev) + 1)
|
||
alpha = (alpha_cumprod / alpha_cumprod_prev)
|
||
|
||
mu = (1.0 / alpha).sqrt() * (x - (1 - alpha) * noise / (1 - alpha_cumprod).sqrt())
|
||
if sigma_prev > 0:
|
||
mu += ((1 - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt() * noise_sampler(sigma, sigma_prev)
|
||
return mu
|
||
|
||
def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None):
|
||
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]])
|
||
|
||
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})
|
||
x = step_function(x / torch.sqrt(1.0 + sigmas[i] ** 2.0), sigmas[i], sigmas[i + 1], (x - denoised) / sigmas[i], noise_sampler)
|
||
if sigmas[i + 1] != 0:
|
||
x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2.0)
|
||
return x
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
|
||
return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step)
|
||
|
||
@torch.no_grad()
|
||
def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
|
||
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]])
|
||
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})
|
||
|
||
x = denoised
|
||
if sigmas[i + 1] > 0:
|
||
x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x)
|
||
return x
|
||
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
|
||
# From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/
|
||
extra_args = {} if extra_args is None else extra_args
|
||
s_in = x.new_ones([x.shape[0]])
|
||
s_end = sigmas[-1]
|
||
for i in trange(len(sigmas) - 1, disable=disable):
|
||
gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
|
||
eps = torch.randn_like(x) * s_noise
|
||
sigma_hat = sigmas[i] * (gamma + 1)
|
||
if gamma > 0:
|
||
x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
|
||
denoised = model(x, sigma_hat * s_in, **extra_args)
|
||
d = to_d(x, sigma_hat, denoised)
|
||
if callback is not None:
|
||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
|
||
dt = sigmas[i + 1] - sigma_hat
|
||
if sigmas[i + 1] == s_end:
|
||
# Euler method
|
||
x = x + d * dt
|
||
elif sigmas[i + 2] == s_end:
|
||
|
||
# Heun's method
|
||
x_2 = x + d * dt
|
||
denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
|
||
d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
|
||
|
||
w = 2 * sigmas[0]
|
||
w2 = sigmas[i+1]/w
|
||
w1 = 1 - w2
|
||
|
||
d_prime = d * w1 + d_2 * w2
|
||
|
||
|
||
x = x + d_prime * dt
|
||
|
||
else:
|
||
# Heun++
|
||
x_2 = x + d * dt
|
||
denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
|
||
d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
|
||
dt_2 = sigmas[i + 2] - sigmas[i + 1]
|
||
|
||
x_3 = x_2 + d_2 * dt_2
|
||
denoised_3 = model(x_3, sigmas[i + 2] * s_in, **extra_args)
|
||
d_3 = to_d(x_3, sigmas[i + 2], denoised_3)
|
||
|
||
w = 3 * sigmas[0]
|
||
w2 = sigmas[i + 1] / w
|
||
w3 = sigmas[i + 2] / w
|
||
w1 = 1 - w2 - w3
|
||
|
||
d_prime = w1 * d + w2 * d_2 + w3 * d_3
|
||
x = x + d_prime * dt
|
||
return x
|
||
|
||
|
||
#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
|
||
#under Apache 2 license
|
||
def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
|
||
extra_args = {} if extra_args is None else extra_args
|
||
s_in = x.new_ones([x.shape[0]])
|
||
|
||
x_next = x
|
||
|
||
buffer_model = []
|
||
for i in trange(len(sigmas) - 1, disable=disable):
|
||
t_cur = sigmas[i]
|
||
t_next = sigmas[i + 1]
|
||
|
||
x_cur = x_next
|
||
|
||
denoised = model(x_cur, t_cur * s_in, **extra_args)
|
||
if callback is not None:
|
||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
|
||
|
||
d_cur = (x_cur - denoised) / t_cur
|
||
|
||
order = min(max_order, i+1)
|
||
if t_next == 0: # Denoising step
|
||
x_next = denoised
|
||
elif order == 1: # First Euler step.
|
||
x_next = x_cur + (t_next - t_cur) * d_cur
|
||
elif order == 2: # Use one history point.
|
||
x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2
|
||
elif order == 3: # Use two history points.
|
||
x_next = x_cur + (t_next - t_cur) * (23 * d_cur - 16 * buffer_model[-1] + 5 * buffer_model[-2]) / 12
|
||
elif order == 4: # Use three history points.
|
||
x_next = x_cur + (t_next - t_cur) * (55 * d_cur - 59 * buffer_model[-1] + 37 * buffer_model[-2] - 9 * buffer_model[-3]) / 24
|
||
|
||
if len(buffer_model) == max_order - 1:
|
||
for k in range(max_order - 2):
|
||
buffer_model[k] = buffer_model[k+1]
|
||
buffer_model[-1] = d_cur
|
||
else:
|
||
buffer_model.append(d_cur)
|
||
|
||
return x_next
|
||
|
||
|
||
#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
|
||
#under Apache 2 license
|
||
def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
|
||
extra_args = {} if extra_args is None else extra_args
|
||
s_in = x.new_ones([x.shape[0]])
|
||
|
||
x_next = x
|
||
t_steps = sigmas
|
||
|
||
buffer_model = []
|
||
for i in trange(len(sigmas) - 1, disable=disable):
|
||
t_cur = sigmas[i]
|
||
t_next = sigmas[i + 1]
|
||
|
||
x_cur = x_next
|
||
|
||
denoised = model(x_cur, t_cur * s_in, **extra_args)
|
||
if callback is not None:
|
||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
|
||
|
||
d_cur = (x_cur - denoised) / t_cur
|
||
|
||
order = min(max_order, i+1)
|
||
if t_next == 0: # Denoising step
|
||
x_next = denoised
|
||
elif order == 1: # First Euler step.
|
||
x_next = x_cur + (t_next - t_cur) * d_cur
|
||
elif order == 2: # Use one history point.
|
||
h_n = (t_next - t_cur)
|
||
h_n_1 = (t_cur - t_steps[i-1])
|
||
coeff1 = (2 + (h_n / h_n_1)) / 2
|
||
coeff2 = -(h_n / h_n_1) / 2
|
||
x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1])
|
||
elif order == 3: # Use two history points.
|
||
h_n = (t_next - t_cur)
|
||
h_n_1 = (t_cur - t_steps[i-1])
|
||
h_n_2 = (t_steps[i-1] - t_steps[i-2])
|
||
temp = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2
|
||
coeff1 = (2 + (h_n / h_n_1)) / 2 + temp
|
||
coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp
|
||
coeff3 = temp * h_n_1 / h_n_2
|
||
x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2])
|
||
elif order == 4: # Use three history points.
|
||
h_n = (t_next - t_cur)
|
||
h_n_1 = (t_cur - t_steps[i-1])
|
||
h_n_2 = (t_steps[i-1] - t_steps[i-2])
|
||
h_n_3 = (t_steps[i-2] - t_steps[i-3])
|
||
temp1 = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2
|
||
temp2 = ((1 - h_n / (3 * (h_n + h_n_1))) / 2 + (1 - h_n / (2 * (h_n + h_n_1))) * h_n / (6 * (h_n + h_n_1 + h_n_2))) \
|
||
* (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3))
|
||
coeff1 = (2 + (h_n / h_n_1)) / 2 + temp1 + temp2
|
||
coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp1 - (1 + (h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3)))) * temp2
|
||
coeff3 = temp1 * h_n_1 / h_n_2 + ((h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * (1 + h_n_2 / h_n_3)) * temp2
|
||
coeff4 = -temp2 * (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * h_n_1 / h_n_2
|
||
x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2] + coeff4 * buffer_model[-3])
|
||
|
||
if len(buffer_model) == max_order - 1:
|
||
for k in range(max_order - 2):
|
||
buffer_model[k] = buffer_model[k+1]
|
||
buffer_model[-1] = d_cur.detach()
|
||
else:
|
||
buffer_model.append(d_cur.detach())
|
||
|
||
return x_next
|
||
|
||
|
||
#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
|
||
#under Apache 2 license
|
||
@torch.no_grad()
|
||
def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=3, deis_mode='tab'):
|
||
extra_args = {} if extra_args is None else extra_args
|
||
s_in = x.new_ones([x.shape[0]])
|
||
|
||
x_next = x
|
||
t_steps = sigmas
|
||
|
||
coeff_list = deis.get_deis_coeff_list(t_steps, max_order, deis_mode=deis_mode)
|
||
|
||
buffer_model = []
|
||
for i in trange(len(sigmas) - 1, disable=disable):
|
||
t_cur = sigmas[i]
|
||
t_next = sigmas[i + 1]
|
||
|
||
x_cur = x_next
|
||
|
||
denoised = model(x_cur, t_cur * s_in, **extra_args)
|
||
if callback is not None:
|
||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
|
||
|
||
d_cur = (x_cur - denoised) / t_cur
|
||
|
||
order = min(max_order, i+1)
|
||
if t_next <= 0:
|
||
order = 1
|
||
|
||
if order == 1: # First Euler step.
|
||
x_next = x_cur + (t_next - t_cur) * d_cur
|
||
elif order == 2: # Use one history point.
|
||
coeff_cur, coeff_prev1 = coeff_list[i]
|
||
x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1]
|
||
elif order == 3: # Use two history points.
|
||
coeff_cur, coeff_prev1, coeff_prev2 = coeff_list[i]
|
||
x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2]
|
||
elif order == 4: # Use three history points.
|
||
coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3 = coeff_list[i]
|
||
x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] + coeff_prev3 * buffer_model[-3]
|
||
|
||
if len(buffer_model) == max_order - 1:
|
||
for k in range(max_order - 2):
|
||
buffer_model[k] = buffer_model[k+1]
|
||
buffer_model[-1] = d_cur.detach()
|
||
else:
|
||
buffer_model.append(d_cur.detach())
|
||
|
||
return x_next
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
|
||
extra_args = {} if extra_args is None else extra_args
|
||
|
||
temp = [0]
|
||
def post_cfg_function(args):
|
||
temp[0] = args["uncond_denoised"]
|
||
return args["denoised"]
|
||
|
||
model_options = extra_args.get("model_options", {}).copy()
|
||
extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
|
||
|
||
s_in = x.new_ones([x.shape[0]])
|
||
for i in trange(len(sigmas) - 1, disable=disable):
|
||
sigma_hat = sigmas[i]
|
||
denoised = model(x, sigma_hat * s_in, **extra_args)
|
||
d = to_d(x, sigma_hat, temp[0])
|
||
if callback is not None:
|
||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
|
||
# Euler method
|
||
x = denoised + d * sigmas[i + 1]
|
||
return x
|
||
|
||
@torch.no_grad()
|
||
def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
|
||
"""Ancestral sampling with Euler method steps."""
|
||
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
|
||
|
||
temp = [0]
|
||
def post_cfg_function(args):
|
||
temp[0] = args["uncond_denoised"]
|
||
return args["denoised"]
|
||
|
||
model_options = extra_args.get("model_options", {}).copy()
|
||
extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
|
||
|
||
s_in = x.new_ones([x.shape[0]])
|
||
for i in trange(len(sigmas) - 1, disable=disable):
|
||
denoised = model(x, sigmas[i] * s_in, **extra_args)
|
||
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
|
||
if callback is not None:
|
||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
|
||
d = to_d(x, sigmas[i], temp[0])
|
||
# Euler method
|
||
x = denoised + d * sigma_down
|
||
if sigmas[i + 1] > 0:
|
||
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
|
||
return x
|
||
@torch.no_grad()
|
||
def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
|
||
"""Ancestral sampling with DPM-Solver++(2S) second-order steps."""
|
||
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
|
||
|
||
temp = [0]
|
||
def post_cfg_function(args):
|
||
temp[0] = args["uncond_denoised"]
|
||
return args["denoised"]
|
||
|
||
model_options = extra_args.get("model_options", {}).copy()
|
||
extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
|
||
|
||
s_in = x.new_ones([x.shape[0]])
|
||
sigma_fn = lambda t: t.neg().exp()
|
||
t_fn = lambda sigma: sigma.log().neg()
|
||
|
||
for i in trange(len(sigmas) - 1, disable=disable):
|
||
denoised = model(x, sigmas[i] * s_in, **extra_args)
|
||
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
|
||
if callback is not None:
|
||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
|
||
if sigma_down == 0:
|
||
# Euler method
|
||
d = to_d(x, sigmas[i], temp[0])
|
||
x = denoised + d * sigma_down
|
||
else:
|
||
# DPM-Solver++(2S)
|
||
t, t_next = t_fn(sigmas[i]), t_fn(sigma_down)
|
||
# r = torch.sinh(1 + (2 - eta) * (t_next - t) / (t - t_fn(sigma_up))) works only on non-cfgpp, weird
|
||
r = 1 / 2
|
||
h = t_next - t
|
||
s = t + r * h
|
||
x_2 = (sigma_fn(s) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h * r).expm1() * denoised
|
||
denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
|
||
x = (sigma_fn(t_next) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h).expm1() * denoised_2
|
||
# Noise addition
|
||
if sigmas[i + 1] > 0:
|
||
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
|
||
return x
|
||
|
||
@torch.no_grad()
|
||
def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
|
||
"""DPM-Solver++(2M)."""
|
||
extra_args = {} if extra_args is None else extra_args
|
||
s_in = x.new_ones([x.shape[0]])
|
||
t_fn = lambda sigma: sigma.log().neg()
|
||
|
||
old_uncond_denoised = None
|
||
uncond_denoised = None
|
||
def post_cfg_function(args):
|
||
nonlocal uncond_denoised
|
||
uncond_denoised = args["uncond_denoised"]
|
||
return args["denoised"]
|
||
|
||
model_options = extra_args.get("model_options", {}).copy()
|
||
extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
|
||
|
||
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})
|
||
t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
|
||
h = t_next - t
|
||
if old_uncond_denoised is None or sigmas[i + 1] == 0:
|
||
denoised_mix = -torch.exp(-h) * uncond_denoised
|
||
else:
|
||
h_last = t - t_fn(sigmas[i - 1])
|
||
r = h_last / h
|
||
denoised_mix = -torch.exp(-h) * uncond_denoised - torch.expm1(-h) * (1 / (2 * r)) * (denoised - old_uncond_denoised)
|
||
x = denoised + denoised_mix + torch.exp(-h) * x
|
||
old_uncond_denoised = uncond_denoised
|
||
return x
|
||
|
||
@torch.no_grad()
|
||
def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, eta=1., cfg_pp=False):
|
||
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]])
|
||
sigma_fn = lambda t: t.neg().exp()
|
||
t_fn = lambda sigma: sigma.log().neg()
|
||
phi1_fn = lambda t: torch.expm1(t) / t
|
||
phi2_fn = lambda t: (phi1_fn(t) - 1.0) / t
|
||
|
||
old_sigma_down = None
|
||
old_denoised = None
|
||
uncond_denoised = None
|
||
def post_cfg_function(args):
|
||
nonlocal uncond_denoised
|
||
uncond_denoised = args["uncond_denoised"]
|
||
return args["denoised"]
|
||
|
||
if cfg_pp:
|
||
model_options = extra_args.get("model_options", {}).copy()
|
||
extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
|
||
|
||
for i in trange(len(sigmas) - 1, disable=disable):
|
||
denoised = model(x, sigmas[i] * s_in, **extra_args)
|
||
sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
|
||
if callback is not None:
|
||
callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
|
||
if sigma_down == 0 or old_denoised is None:
|
||
# Euler method
|
||
if cfg_pp:
|
||
d = to_d(x, sigmas[i], uncond_denoised)
|
||
x = denoised + d * sigma_down
|
||
else:
|
||
d = to_d(x, sigmas[i], denoised)
|
||
dt = sigma_down - sigmas[i]
|
||
x = x + d * dt
|
||
else:
|
||
# Second order multistep method in https://arxiv.org/pdf/2308.02157
|
||
t, t_old, t_next, t_prev = t_fn(sigmas[i]), t_fn(old_sigma_down), t_fn(sigma_down), t_fn(sigmas[i - 1])
|
||
h = t_next - t
|
||
c2 = (t_prev - t_old) / h
|
||
|
||
phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h)
|
||
b1 = torch.nan_to_num(phi1_val - phi2_val / c2, nan=0.0)
|
||
b2 = torch.nan_to_num(phi2_val / c2, nan=0.0)
|
||
|
||
if cfg_pp:
|
||
x = x + (denoised - uncond_denoised)
|
||
x = sigma_fn(h) * x + h * (b1 * uncond_denoised + b2 * old_denoised)
|
||
else:
|
||
x = sigma_fn(h) * x + h * (b1 * denoised + b2 * old_denoised)
|
||
|
||
# Noise addition
|
||
if sigmas[i + 1] > 0:
|
||
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
|
||
|
||
if cfg_pp:
|
||
old_denoised = uncond_denoised
|
||
else:
|
||
old_denoised = denoised
|
||
old_sigma_down = sigma_down
|
||
return x
|
||
|
||
@torch.no_grad()
|
||
def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None):
|
||
return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=False)
|
||
|
||
@torch.no_grad()
|
||
def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None):
|
||
return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=True)
|
||
|
||
@torch.no_grad()
|
||
def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
|
||
return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=False)
|
||
|
||
@torch.no_grad()
|
||
def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
|
||
return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=True)
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2., cfg_pp=False):
|
||
"""Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK"""
|
||
extra_args = {} if extra_args is None else extra_args
|
||
s_in = x.new_ones([x.shape[0]])
|
||
old_d = None
|
||
|
||
uncond_denoised = None
|
||
def post_cfg_function(args):
|
||
nonlocal uncond_denoised
|
||
uncond_denoised = args["uncond_denoised"]
|
||
return args["denoised"]
|
||
|
||
if cfg_pp:
|
||
model_options = extra_args.get("model_options", {}).copy()
|
||
extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
|
||
|
||
for i in trange(len(sigmas) - 1, disable=disable):
|
||
denoised = model(x, sigmas[i] * s_in, **extra_args)
|
||
if cfg_pp:
|
||
d = to_d(x, sigmas[i], uncond_denoised)
|
||
else:
|
||
d = to_d(x, sigmas[i], denoised)
|
||
if callback is not None:
|
||
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
|
||
dt = sigmas[i + 1] - sigmas[i]
|
||
if sigmas[i + 1] == 0:
|
||
# Denoising step
|
||
x = denoised
|
||
else:
|
||
# Euler method
|
||
if cfg_pp:
|
||
x = denoised + d * sigmas[i + 1]
|
||
else:
|
||
x = x + d * dt
|
||
|
||
if i >= 1:
|
||
# Gradient estimation
|
||
d_bar = (ge_gamma - 1) * (d - old_d)
|
||
x = x + d_bar * dt
|
||
old_d = d
|
||
return x
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_gradient_estimation_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.):
|
||
return sample_gradient_estimation(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, ge_gamma=ge_gamma, cfg_pp=True)
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.0, noise_sampler=None, noise_scaler=None, max_stage=3):
|
||
"""Extended Reverse-Time SDE solver (VP ER-SDE-Solver-3). arXiv: https://arxiv.org/abs/2309.06169.
|
||
Code reference: https://github.com/QinpengCui/ER-SDE-Solver/blob/main/er_sde_solver.py.
|
||
"""
|
||
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]])
|
||
|
||
def default_er_sde_noise_scaler(x):
|
||
return x * ((x ** 0.3).exp() + 10.0)
|
||
|
||
noise_scaler = default_er_sde_noise_scaler if noise_scaler is None else noise_scaler
|
||
num_integration_points = 200.0
|
||
point_indice = torch.arange(0, num_integration_points, dtype=torch.float32, device=x.device)
|
||
|
||
model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
|
||
sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
|
||
half_log_snrs = sigma_to_half_log_snr(sigmas, model_sampling)
|
||
er_lambdas = half_log_snrs.neg().exp() # er_lambda_t = sigma_t / alpha_t
|
||
|
||
old_denoised = None
|
||
old_denoised_d = None
|
||
|
||
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})
|
||
stage_used = min(max_stage, i + 1)
|
||
if sigmas[i + 1] == 0:
|
||
x = denoised
|
||
else:
|
||
er_lambda_s, er_lambda_t = er_lambdas[i], er_lambdas[i + 1]
|
||
alpha_s = sigmas[i] / er_lambda_s
|
||
alpha_t = sigmas[i + 1] / er_lambda_t
|
||
r_alpha = alpha_t / alpha_s
|
||
r = noise_scaler(er_lambda_t) / noise_scaler(er_lambda_s)
|
||
|
||
# Stage 1 Euler
|
||
x = r_alpha * r * x + alpha_t * (1 - r) * denoised
|
||
|
||
if stage_used >= 2:
|
||
dt = er_lambda_t - er_lambda_s
|
||
lambda_step_size = -dt / num_integration_points
|
||
lambda_pos = er_lambda_t + point_indice * lambda_step_size
|
||
scaled_pos = noise_scaler(lambda_pos)
|
||
|
||
# Stage 2
|
||
s = torch.sum(1 / scaled_pos) * lambda_step_size
|
||
denoised_d = (denoised - old_denoised) / (er_lambda_s - er_lambdas[i - 1])
|
||
x = x + alpha_t * (dt + s * noise_scaler(er_lambda_t)) * denoised_d
|
||
|
||
if stage_used >= 3:
|
||
# Stage 3
|
||
s_u = torch.sum((lambda_pos - er_lambda_s) / scaled_pos) * lambda_step_size
|
||
denoised_u = (denoised_d - old_denoised_d) / ((er_lambda_s - er_lambdas[i - 2]) / 2)
|
||
x = x + alpha_t * ((dt ** 2) / 2 + s_u * noise_scaler(er_lambda_t)) * denoised_u
|
||
old_denoised_d = denoised_d
|
||
|
||
if s_noise > 0:
|
||
x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (er_lambda_t ** 2 - er_lambda_s ** 2 * r ** 2).sqrt().nan_to_num(nan=0.0)
|
||
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 (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
|
||
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 * h
|
||
fac = 1 / (2 * r)
|
||
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 = (-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 = 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
|
||
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 = 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
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, use_pece=False, simple_order_2=False):
|
||
"""Stochastic Adams Solver with predictor-corrector method (NeurIPS 2023)."""
|
||
if len(sigmas) <= 1:
|
||
return x
|
||
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]])
|
||
|
||
model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
|
||
sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
|
||
lambdas = sigma_to_half_log_snr(sigmas, model_sampling=model_sampling)
|
||
|
||
if tau_func is None:
|
||
# Use default interval for stochastic sampling
|
||
start_sigma = model_sampling.percent_to_sigma(0.2)
|
||
end_sigma = model_sampling.percent_to_sigma(0.8)
|
||
tau_func = sa_solver.get_tau_interval_func(start_sigma, end_sigma, eta=1.0)
|
||
|
||
max_used_order = max(predictor_order, corrector_order)
|
||
x_pred = x # x: current state, x_pred: predicted next state
|
||
|
||
h = 0.0
|
||
tau_t = 0.0
|
||
noise = 0.0
|
||
pred_list = []
|
||
|
||
# Lower order near the end to improve stability
|
||
lower_order_to_end = sigmas[-1].item() == 0
|
||
|
||
for i in trange(len(sigmas) - 1, disable=disable):
|
||
# Evaluation
|
||
denoised = model(x_pred, sigmas[i] * s_in, **extra_args)
|
||
if callback is not None:
|
||
callback({"x": x_pred, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
|
||
pred_list.append(denoised)
|
||
pred_list = pred_list[-max_used_order:]
|
||
|
||
predictor_order_used = min(predictor_order, len(pred_list))
|
||
if i == 0 or (sigmas[i + 1] == 0 and not use_pece):
|
||
corrector_order_used = 0
|
||
else:
|
||
corrector_order_used = min(corrector_order, len(pred_list))
|
||
|
||
if lower_order_to_end:
|
||
predictor_order_used = min(predictor_order_used, len(sigmas) - 2 - i)
|
||
corrector_order_used = min(corrector_order_used, len(sigmas) - 1 - i)
|
||
|
||
# Corrector
|
||
if corrector_order_used == 0:
|
||
# Update by the predicted state
|
||
x = x_pred
|
||
else:
|
||
curr_lambdas = lambdas[i - corrector_order_used + 1:i + 1]
|
||
b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs(
|
||
sigmas[i],
|
||
curr_lambdas,
|
||
lambdas[i - 1],
|
||
lambdas[i],
|
||
tau_t,
|
||
simple_order_2,
|
||
is_corrector_step=True,
|
||
)
|
||
pred_mat = torch.stack(pred_list[-corrector_order_used:], dim=1) # (B, K, ...)
|
||
corr_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0])) # (B, ...)
|
||
x = sigmas[i] / sigmas[i - 1] * (-(tau_t ** 2) * h).exp() * x + corr_res
|
||
|
||
if tau_t > 0 and s_noise > 0:
|
||
# The noise from the previous predictor step
|
||
x = x + noise
|
||
|
||
if use_pece:
|
||
# Evaluate the corrected state
|
||
denoised = model(x, sigmas[i] * s_in, **extra_args)
|
||
pred_list[-1] = denoised
|
||
|
||
# Predictor
|
||
if sigmas[i + 1] == 0:
|
||
# Denoising step
|
||
x = denoised
|
||
else:
|
||
tau_t = tau_func(sigmas[i + 1])
|
||
curr_lambdas = lambdas[i - predictor_order_used + 1:i + 1]
|
||
b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs(
|
||
sigmas[i + 1],
|
||
curr_lambdas,
|
||
lambdas[i],
|
||
lambdas[i + 1],
|
||
tau_t,
|
||
simple_order_2,
|
||
is_corrector_step=False,
|
||
)
|
||
pred_mat = torch.stack(pred_list[-predictor_order_used:], dim=1) # (B, K, ...)
|
||
pred_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0])) # (B, ...)
|
||
h = lambdas[i + 1] - lambdas[i]
|
||
x_pred = sigmas[i + 1] / sigmas[i] * (-(tau_t ** 2) * h).exp() * x + pred_res
|
||
|
||
if tau_t > 0 and s_noise > 0:
|
||
noise = noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * tau_t ** 2 * h).expm1().neg().sqrt() * s_noise
|
||
x_pred = x_pred + noise
|
||
return x
|
||
|
||
|
||
@torch.no_grad()
|
||
def sample_sa_solver_pece(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, simple_order_2=False):
|
||
"""Stochastic Adams Solver with PECE (Predict–Evaluate–Correct–Evaluate) mode (NeurIPS 2023)."""
|
||
return sample_sa_solver(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, tau_func=tau_func, s_noise=s_noise, noise_sampler=noise_sampler, predictor_order=predictor_order, corrector_order=corrector_order, use_pece=True, simple_order_2=simple_order_2)
|