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"""
Partially ported from https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py
"""
from typing import Dict, Union
import torch
from omegaconf import ListConfig, OmegaConf
from tqdm import tqdm
from ...modules.diffusionmodules.sampling_utils import (
get_ancestral_step,
linear_multistep_coeff,
to_d,
to_neg_log_sigma,
to_sigma,
)
from ...util import append_dims, default, instantiate_from_config
from k_diffusion.sampling import get_sigmas_karras, BrownianTreeNoiseSampler
DEFAULT_GUIDER = {"target": "sgm.modules.diffusionmodules.guiders.IdentityGuider"}
class BaseDiffusionSampler:
def __init__(
self,
discretization_config: Union[Dict, ListConfig, OmegaConf],
num_steps: Union[int, None] = None,
guider_config: Union[Dict, ListConfig, OmegaConf, None] = None,
verbose: bool = False,
device: str = "cuda",
):
self.num_steps = num_steps
self.discretization = instantiate_from_config(discretization_config)
self.guider = instantiate_from_config(
default(
guider_config,
DEFAULT_GUIDER,
)
)
self.verbose = verbose
self.device = device
def prepare_sampling_loop(self, x, cond, uc=None, num_steps=None):
sigmas = self.discretization(
self.num_steps if num_steps is None else num_steps, device=self.device
)
uc = default(uc, cond)
x *= torch.sqrt(1.0 + sigmas[0] ** 2.0)
num_sigmas = len(sigmas)
s_in = x.new_ones([x.shape[0]])
return x, s_in, sigmas, num_sigmas, cond, uc
def denoise(self, x, denoiser, sigma, cond, uc):
denoised = denoiser(*self.guider.prepare_inputs(x, sigma, cond, uc))
denoised = self.guider(denoised, sigma)
return denoised
def get_sigma_gen(self, num_sigmas):
sigma_generator = range(num_sigmas - 1)
if self.verbose:
print("#" * 30, " Sampling setting ", "#" * 30)
print(f"Sampler: {self.__class__.__name__}")
print(f"Discretization: {self.discretization.__class__.__name__}")
print(f"Guider: {self.guider.__class__.__name__}")
sigma_generator = tqdm(
sigma_generator,
total=num_sigmas,
desc=f"Sampling with {self.__class__.__name__} for {num_sigmas} steps",
)
return sigma_generator
class SingleStepDiffusionSampler(BaseDiffusionSampler):
def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc, *args, **kwargs):
raise NotImplementedError
def euler_step(self, x, d, dt):
return x + dt * d
class EDMSampler(SingleStepDiffusionSampler):
def __init__(
self, s_churn=0.0, s_tmin=0.0, s_tmax=float("inf"), s_noise=1.0, *args, **kwargs
):
super().__init__(*args, **kwargs)
self.s_churn = s_churn
self.s_tmin = s_tmin
self.s_tmax = s_tmax
self.s_noise = s_noise
def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc=None, gamma=0.0):
sigma_hat = sigma * (gamma + 1.0)
if gamma > 0:
eps = torch.randn_like(x) * self.s_noise
x = x + eps * append_dims(sigma_hat**2 - sigma**2, x.ndim) ** 0.5
denoised = self.denoise(x, denoiser, sigma_hat, cond, uc)
# print('denoised', denoised.mean(axis=[0, 2, 3]))
d = to_d(x, sigma_hat, denoised)
dt = append_dims(next_sigma - sigma_hat, x.ndim)
euler_step = self.euler_step(x, d, dt)
x = self.possible_correction_step(
euler_step, x, d, dt, next_sigma, denoiser, cond, uc
)
return x
def __call__(self, denoiser, x, cond, uc=None, num_steps=None):
x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
x, cond, uc, num_steps
)
for i in self.get_sigma_gen(num_sigmas):
gamma = (
min(self.s_churn / (num_sigmas - 1), 2**0.5 - 1)
if self.s_tmin <= sigmas[i] <= self.s_tmax
else 0.0
)
x = self.sampler_step(
s_in * sigmas[i],
s_in * sigmas[i + 1],
denoiser,
x,
cond,
uc,
gamma,
)
return x
class AncestralSampler(SingleStepDiffusionSampler):
def __init__(self, eta=1.0, s_noise=1.0, *args, **kwargs):
super().__init__(*args, **kwargs)
self.eta = eta
self.s_noise = s_noise
self.noise_sampler = lambda x: torch.randn_like(x)
def ancestral_euler_step(self, x, denoised, sigma, sigma_down):
d = to_d(x, sigma, denoised)
dt = append_dims(sigma_down - sigma, x.ndim)
return self.euler_step(x, d, dt)
def ancestral_step(self, x, sigma, next_sigma, sigma_up):
x = torch.where(
append_dims(next_sigma, x.ndim) > 0.0,
x + self.noise_sampler(x) * self.s_noise * append_dims(sigma_up, x.ndim),
x,
)
return x
def __call__(self, denoiser, x, cond, uc=None, num_steps=None):
x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
x, cond, uc, num_steps
)
for i in self.get_sigma_gen(num_sigmas):
x = self.sampler_step(
s_in * sigmas[i],
s_in * sigmas[i + 1],
denoiser,
x,
cond,
uc,
)
return x
class LinearMultistepSampler(BaseDiffusionSampler):
def __init__(
self,
order=4,
*args,
**kwargs,
):
super().__init__(*args, **kwargs)
self.order = order
def __call__(self, denoiser, x, cond, uc=None, num_steps=None, **kwargs):
x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
x, cond, uc, num_steps
)
ds = []
sigmas_cpu = sigmas.detach().cpu().numpy()
for i in self.get_sigma_gen(num_sigmas):
sigma = s_in * sigmas[i]
denoised = denoiser(
*self.guider.prepare_inputs(x, sigma, cond, uc), **kwargs
)
denoised = self.guider(denoised, sigma)
d = to_d(x, sigma, denoised)
ds.append(d)
if len(ds) > self.order:
ds.pop(0)
cur_order = min(i + 1, self.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 EulerEDMSampler(EDMSampler):
def possible_correction_step(
self, euler_step, x, d, dt, next_sigma, denoiser, cond, uc
):
# print("euler_step: ", euler_step.mean(axis=[0, 2, 3]))
return euler_step
class HeunEDMSampler(EDMSampler):
def possible_correction_step(
self, euler_step, x, d, dt, next_sigma, denoiser, cond, uc
):
if torch.sum(next_sigma) < 1e-14:
# Save a network evaluation if all noise levels are 0
return euler_step
else:
denoised = self.denoise(euler_step, denoiser, next_sigma, cond, uc)
d_new = to_d(euler_step, next_sigma, denoised)
d_prime = (d + d_new) / 2.0
# apply correction if noise level is not 0
x = torch.where(
append_dims(next_sigma, x.ndim) > 0.0, x + d_prime * dt, euler_step
)
return x
class EulerAncestralSampler(AncestralSampler):
def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc):
sigma_down, sigma_up = get_ancestral_step(sigma, next_sigma, eta=self.eta)
denoised = self.denoise(x, denoiser, sigma, cond, uc)
x = self.ancestral_euler_step(x, denoised, sigma, sigma_down)
x = self.ancestral_step(x, sigma, next_sigma, sigma_up)
return x
class DPMPP2SAncestralSampler(AncestralSampler):
def get_variables(self, sigma, sigma_down):
t, t_next = [to_neg_log_sigma(s) for s in (sigma, sigma_down)]
h = t_next - t
s = t + 0.5 * h
return h, s, t, t_next
def get_mult(self, h, s, t, t_next):
mult1 = to_sigma(s) / to_sigma(t)
mult2 = (-0.5 * h).expm1()
mult3 = to_sigma(t_next) / to_sigma(t)
mult4 = (-h).expm1()
return mult1, mult2, mult3, mult4
def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc=None, **kwargs):
sigma_down, sigma_up = get_ancestral_step(sigma, next_sigma, eta=self.eta)
denoised = self.denoise(x, denoiser, sigma, cond, uc)
x_euler = self.ancestral_euler_step(x, denoised, sigma, sigma_down)
if torch.sum(sigma_down) < 1e-14:
# Save a network evaluation if all noise levels are 0
x = x_euler
else:
h, s, t, t_next = self.get_variables(sigma, sigma_down)
mult = [
append_dims(mult, x.ndim) for mult in self.get_mult(h, s, t, t_next)
]
x2 = mult[0] * x - mult[1] * denoised
denoised2 = self.denoise(x2, denoiser, to_sigma(s), cond, uc)
x_dpmpp2s = mult[2] * x - mult[3] * denoised2
# apply correction if noise level is not 0
x = torch.where(append_dims(sigma_down, x.ndim) > 0.0, x_dpmpp2s, x_euler)
x = self.ancestral_step(x, sigma, next_sigma, sigma_up)
return x
class DPMPP2MSampler(BaseDiffusionSampler):
def get_variables(self, sigma, next_sigma, previous_sigma=None):
t, t_next = [to_neg_log_sigma(s) for s in (sigma, next_sigma)]
h = t_next - t
if previous_sigma is not None:
h_last = t - to_neg_log_sigma(previous_sigma)
r = h_last / h
return h, r, t, t_next
else:
return h, None, t, t_next
def get_mult(self, h, r, t, t_next, previous_sigma):
mult1 = to_sigma(t_next) / to_sigma(t)
mult2 = (-h).expm1()
if previous_sigma is not None:
mult3 = 1 + 1 / (2 * r)
mult4 = 1 / (2 * r)
return mult1, mult2, mult3, mult4
else:
return mult1, mult2
def sampler_step(
self,
old_denoised,
previous_sigma,
sigma,
next_sigma,
denoiser,
x,
cond,
uc=None,
):
denoised = self.denoise(x, denoiser, sigma, cond, uc)
h, r, t, t_next = self.get_variables(sigma, next_sigma, previous_sigma)
mult = [
append_dims(mult, x.ndim)
for mult in self.get_mult(h, r, t, t_next, previous_sigma)
]
x_standard = mult[0] * x - mult[1] * denoised
if old_denoised is None or torch.sum(next_sigma) < 1e-14:
# Save a network evaluation if all noise levels are 0 or on the first step
return x_standard, denoised
else:
denoised_d = mult[2] * denoised - mult[3] * old_denoised
x_advanced = mult[0] * x - mult[1] * denoised_d
# apply correction if noise level is not 0 and not first step
x = torch.where(
append_dims(next_sigma, x.ndim) > 0.0, x_advanced, x_standard
)
return x, denoised
def __call__(self, denoiser, x, cond, uc=None, num_steps=None, **kwargs):
x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
x, cond, uc, num_steps
)
old_denoised = None
for i in self.get_sigma_gen(num_sigmas):
x, old_denoised = self.sampler_step(
old_denoised,
None if i == 0 else s_in * sigmas[i - 1],
s_in * sigmas[i],
s_in * sigmas[i + 1],
denoiser,
x,
cond,
uc=uc,
)
return x
class SubstepSampler(EulerAncestralSampler):
def __init__(self, s_churn=0.0, s_tmin=0.0, s_tmax=float("inf"), s_noise=1.0, restore_cfg=4.0,
restore_cfg_s_tmin=0.05, eta=1., n_sample_steps=4, *args, **kwargs):
super().__init__(*args, **kwargs)
self.n_sample_steps = n_sample_steps
self.steps_subset = [0, 100, 200, 300, 1000]
def prepare_sampling_loop(self, x, cond, uc=None, num_steps=None):
sigmas = self.discretization(1000, device=self.device)
sigmas = sigmas[
self.steps_subset[: self.num_steps] + self.steps_subset[-1:]
]
print(sigmas)
# uc = cond
x *= torch.sqrt(1.0 + sigmas[0] ** 2.0)
num_sigmas = len(sigmas)
s_in = x.new_ones([x.shape[0]])
return x, s_in, sigmas, num_sigmas, cond, uc
def denoise(self, x, denoiser, sigma, cond, uc, control_scale=1.0):
denoised = denoiser(*self.guider.prepare_inputs(x, sigma, cond, uc), control_scale)
denoised = self.guider(denoised, sigma)
return denoised
def __call__(self, denoiser, x, cond, uc=None, num_steps=None, control_scale=1.0, *args, **kwargs):
x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
x, cond, uc, num_steps
)
for i in self.get_sigma_gen(num_sigmas):
x = self.sampler_step(
s_in * sigmas[i],
s_in * sigmas[i + 1],
denoiser,
x,
cond,
uc,
control_scale=control_scale,
)
return x
def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc, control_scale=1.0):
sigma_down, sigma_up = get_ancestral_step(sigma, next_sigma, eta=self.eta)
denoised = self.denoise(x, denoiser, sigma, cond, uc, control_scale=control_scale)
x = self.ancestral_euler_step(x, denoised, sigma, sigma_down)
x = self.ancestral_step(x, sigma, next_sigma, sigma_up)
return x
class RestoreDPMPP2MSampler(DPMPP2MSampler):
def __init__(self, s_churn=0.0, s_tmin=0.0, s_tmax=float("inf"), s_noise=1.0, restore_cfg=4.0,
restore_cfg_s_tmin=0.05, eta=1., *args, **kwargs):
self.s_noise = s_noise
self.eta = eta
super().__init__(*args, **kwargs)
def denoise(self, x, denoiser, sigma, cond, uc, control_scale=1.0):
denoised = denoiser(*self.guider.prepare_inputs(x, sigma, cond, uc), control_scale)
denoised = self.guider(denoised, sigma)
return denoised
def get_mult(self, h, r, t, t_next, previous_sigma):
eta_h = self.eta * h
mult1 = to_sigma(t_next) / to_sigma(t) * (-eta_h).exp()
mult2 = (-h -eta_h).expm1()
if previous_sigma is not None:
mult3 = 1 + 1 / (2 * r)
mult4 = 1 / (2 * r)
return mult1, mult2, mult3, mult4
else:
return mult1, mult2
def sampler_step(
self,
old_denoised,
previous_sigma,
sigma,
next_sigma,
denoiser,
x,
cond,
uc=None,
eps_noise=None,
control_scale=1.0,
):
denoised = self.denoise(x, denoiser, sigma, cond, uc, control_scale=control_scale)
h, r, t, t_next = self.get_variables(sigma, next_sigma, previous_sigma)
eta_h = self.eta * h
mult = [
append_dims(mult, x.ndim)
for mult in self.get_mult(h, r, t, t_next, previous_sigma)
]
x_standard = mult[0] * x - mult[1] * denoised
if old_denoised is None or torch.sum(next_sigma) < 1e-14:
# Save a network evaluation if all noise levels are 0 or on the first step
return x_standard, denoised
else:
denoised_d = mult[2] * denoised - mult[3] * old_denoised
x_advanced = mult[0] * x - mult[1] * denoised_d
# apply correction if noise level is not 0 and not first step
x = torch.where(
append_dims(next_sigma, x.ndim) > 0.0, x_advanced, x_standard
)
if self.eta:
x = x + eps_noise * next_sigma * (-2 * eta_h).expm1().neg().sqrt() * self.s_noise
return x, denoised
def __call__(self, denoiser, x, cond, uc=None, num_steps=None, control_scale=1.0, **kwargs):
x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
x, cond, uc, num_steps
)
sigmas_min, sigmas_max = sigmas[-2].cpu(), sigmas[0].cpu()
sigmas_new = get_sigmas_karras(self.num_steps, sigmas_min, sigmas_max, device=x.device)
sigmas = sigmas_new
noise_sampler = BrownianTreeNoiseSampler(x, sigmas_min, sigmas_max)
old_denoised = None
for i in self.get_sigma_gen(num_sigmas):
if i > 0 and torch.sum(s_in * sigmas[i + 1]) > 1e-14:
eps_noise = noise_sampler(s_in * sigmas[i], s_in * sigmas[i + 1])
else:
eps_noise = None
x, old_denoised = self.sampler_step(
old_denoised,
None if i == 0 else s_in * sigmas[i - 1],
s_in * sigmas[i],
s_in * sigmas[i + 1],
denoiser,
x,
cond,
uc=uc,
eps_noise=eps_noise,
control_scale=control_scale,
)
return x
def to_d_center(denoised, x_center, x):
b = denoised.shape[0]
v_center = (denoised - x_center).view(b, -1)
v_denoise = (x - denoised).view(b, -1)
d_center = v_center - v_denoise * (v_center * v_denoise).sum(dim=1).view(b, 1) / \
(v_denoise * v_denoise).sum(dim=1).view(b, 1)
d_center = d_center / d_center.view(x.shape[0], -1).norm(dim=1).view(-1, 1)
return d_center.view(denoised.shape)
class RestoreEDMSampler(SingleStepDiffusionSampler):
def __init__(
self, s_churn=0.0, s_tmin=0.0, s_tmax=float("inf"), s_noise=1.0, restore_cfg=4.0,
restore_cfg_s_tmin=0.05, *args, **kwargs
):
super().__init__(*args, **kwargs)
self.s_churn = s_churn
self.s_tmin = s_tmin
self.s_tmax = s_tmax
self.s_noise = s_noise
self.restore_cfg = restore_cfg
self.restore_cfg_s_tmin = restore_cfg_s_tmin
self.sigma_max = 14.6146
def denoise(self, x, denoiser, sigma, cond, uc, control_scale=1.0):
denoised = denoiser(*self.guider.prepare_inputs(x, sigma, cond, uc), control_scale)
denoised = self.guider(denoised, sigma)
return denoised
def sampler_step(self, sigma, next_sigma, denoiser, x, cond, uc=None, gamma=0.0, x_center=None, eps_noise=None,
control_scale=1.0, use_linear_control_scale=False, control_scale_start=0.0):
sigma_hat = sigma * (gamma + 1.0)
if gamma > 0:
if eps_noise is not None:
eps = eps_noise * self.s_noise
else:
eps = torch.randn_like(x) * self.s_noise
x = x + eps * append_dims(sigma_hat**2 - sigma**2, x.ndim) ** 0.5
if use_linear_control_scale:
control_scale = (sigma[0].item() / self.sigma_max) * (control_scale_start - control_scale) + control_scale
denoised = self.denoise(x, denoiser, sigma_hat, cond, uc, control_scale=control_scale)
if (next_sigma[0] > self.restore_cfg_s_tmin) and (self.restore_cfg > 0):
d_center = (denoised - x_center)
denoised = denoised - d_center * ((sigma.view(-1, 1, 1, 1) / self.sigma_max) ** self.restore_cfg)
d = to_d(x, sigma_hat, denoised)
dt = append_dims(next_sigma - sigma_hat, x.ndim)
x = self.euler_step(x, d, dt)
return x
def __call__(self, denoiser, x, cond, uc=None, num_steps=None, x_center=None, control_scale=1.0,
use_linear_control_scale=False, control_scale_start=0.0):
x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
x, cond, uc, num_steps
)
for _idx, i in enumerate(self.get_sigma_gen(num_sigmas)):
gamma = (
min(self.s_churn / (num_sigmas - 1), 2**0.5 - 1)
if self.s_tmin <= sigmas[i] <= self.s_tmax
else 0.0
)
x = self.sampler_step(
s_in * sigmas[i],
s_in * sigmas[i + 1],
denoiser,
x,
cond,
uc,
gamma,
x_center,
control_scale=control_scale,
use_linear_control_scale=use_linear_control_scale,
control_scale_start=control_scale_start,
)
return x
class TiledRestoreEDMSampler(RestoreEDMSampler):
def __init__(self, tile_size=128, tile_stride=64, *args, **kwargs):
super().__init__(*args, **kwargs)
self.tile_size = tile_size
self.tile_stride = tile_stride
self.tile_weights = gaussian_weights(self.tile_size, self.tile_size, 1)
def __call__(self, denoiser, x, cond, uc=None, num_steps=None, x_center=None, control_scale=1.0,
use_linear_control_scale=False, control_scale_start=0.0):
use_local_prompt = isinstance(cond, list)
b, _, h, w = x.shape
latent_tiles_iterator = _sliding_windows(h, w, self.tile_size, self.tile_stride)
tile_weights = self.tile_weights.repeat(b, 1, 1, 1)
if not use_local_prompt:
LQ_latent = cond['control']
else:
assert len(cond) == len(latent_tiles_iterator), "Number of local prompts should be equal to number of tiles"
LQ_latent = cond[0]['control']
clean_LQ_latent = x_center
x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
x, cond, uc, num_steps
)
for _idx, i in enumerate(self.get_sigma_gen(num_sigmas)):
gamma = (
min(self.s_churn / (num_sigmas - 1), 2**0.5 - 1)
if self.s_tmin <= sigmas[i] <= self.s_tmax
else 0.0
)
x_next = torch.zeros_like(x)
count = torch.zeros_like(x)
eps_noise = torch.randn_like(x)
for j, (hi, hi_end, wi, wi_end) in enumerate(latent_tiles_iterator):
x_tile = x[:, :, hi:hi_end, wi:wi_end]
_eps_noise = eps_noise[:, :, hi:hi_end, wi:wi_end]
x_center_tile = clean_LQ_latent[:, :, hi:hi_end, wi:wi_end]
if use_local_prompt:
_cond = cond[j]
else:
_cond = cond
_cond['control'] = LQ_latent[:, :, hi:hi_end, wi:wi_end]
uc['control'] = LQ_latent[:, :, hi:hi_end, wi:wi_end]
_x = self.sampler_step(
s_in * sigmas[i],
s_in * sigmas[i + 1],
denoiser,
x_tile,
_cond,
uc,
gamma,
x_center_tile,
eps_noise=_eps_noise,
control_scale=control_scale,
use_linear_control_scale=use_linear_control_scale,
control_scale_start=control_scale_start,
)
x_next[:, :, hi:hi_end, wi:wi_end] += _x * tile_weights
count[:, :, hi:hi_end, wi:wi_end] += tile_weights
x_next /= count
x = x_next
return x
class TiledRestoreDPMPP2MSampler(RestoreDPMPP2MSampler):
def __init__(self, tile_size=128, tile_stride=64, *args, **kwargs):
super().__init__(*args, **kwargs)
self.tile_size = tile_size
self.tile_stride = tile_stride
self.tile_weights = gaussian_weights(self.tile_size, self.tile_size, 1)
def __call__(self, denoiser, x, cond, uc=None, num_steps=None, control_scale=1.0, **kwargs):
use_local_prompt = isinstance(cond, list)
b, _, h, w = x.shape
latent_tiles_iterator = _sliding_windows(h, w, self.tile_size, self.tile_stride)
tile_weights = self.tile_weights.repeat(b, 1, 1, 1)
if not use_local_prompt:
LQ_latent = cond['control']
else:
assert len(cond) == len(latent_tiles_iterator), "Number of local prompts should be equal to number of tiles"
LQ_latent = cond[0]['control']
x, s_in, sigmas, num_sigmas, cond, uc = self.prepare_sampling_loop(
x, cond, uc, num_steps
)
sigmas_min, sigmas_max = sigmas[-2].cpu(), sigmas[0].cpu()
sigmas_new = get_sigmas_karras(self.num_steps, sigmas_min, sigmas_max, device=x.device)
sigmas = sigmas_new
noise_sampler = BrownianTreeNoiseSampler(x, sigmas_min, sigmas_max)
old_denoised = None
for _idx, i in enumerate(self.get_sigma_gen(num_sigmas)):
if i > 0 and torch.sum(s_in * sigmas[i + 1]) > 1e-14:
eps_noise = noise_sampler(s_in * sigmas[i], s_in * sigmas[i + 1])
else:
eps_noise = torch.zeros_like(x)
x_next = torch.zeros_like(x)
old_denoised_next = torch.zeros_like(x)
count = torch.zeros_like(x)
for j, (hi, hi_end, wi, wi_end) in enumerate(latent_tiles_iterator):
x_tile = x[:, :, hi:hi_end, wi:wi_end]
_eps_noise = eps_noise[:, :, hi:hi_end, wi:wi_end]
if old_denoised is not None:
old_denoised_tile = old_denoised[:, :, hi:hi_end, wi:wi_end]
else:
old_denoised_tile = None
if use_local_prompt:
_cond = cond[j]
else:
_cond = cond
_cond['control'] = LQ_latent[:, :, hi:hi_end, wi:wi_end]
uc['control'] = LQ_latent[:, :, hi:hi_end, wi:wi_end]
_x, _old_denoised = self.sampler_step(
old_denoised_tile,
None if i == 0 else s_in * sigmas[i - 1],
s_in * sigmas[i],
s_in * sigmas[i + 1],
denoiser,
x_tile,
_cond,
uc=uc,
eps_noise=_eps_noise,
control_scale=control_scale,
)
x_next[:, :, hi:hi_end, wi:wi_end] += _x * tile_weights
old_denoised_next[:, :, hi:hi_end, wi:wi_end] += _old_denoised * tile_weights
count[:, :, hi:hi_end, wi:wi_end] += tile_weights
old_denoised_next /= count
x_next /= count
x = x_next
old_denoised = old_denoised_next
return x
def gaussian_weights(tile_width, tile_height, nbatches):
"""Generates a gaussian mask of weights for tile contributions"""
from numpy import pi, exp, sqrt
import numpy as np
latent_width = tile_width
latent_height = tile_height
var = 0.01
midpoint = (latent_width - 1) / 2 # -1 because index goes from 0 to latent_width - 1
x_probs = [exp(-(x - midpoint) * (x - midpoint) / (latent_width * latent_width) / (2 * var)) / sqrt(2 * pi * var)
for x in range(latent_width)]
midpoint = latent_height / 2
y_probs = [exp(-(y - midpoint) * (y - midpoint) / (latent_height * latent_height) / (2 * var)) / sqrt(2 * pi * var)
for y in range(latent_height)]
weights = np.outer(y_probs, x_probs)
return torch.tile(torch.tensor(weights, device='cuda'), (nbatches, 4, 1, 1))
def _sliding_windows(h: int, w: int, tile_size: int, tile_stride: int):
hi_list = list(range(0, h - tile_size + 1, tile_stride))
if (h - tile_size) % tile_stride != 0:
hi_list.append(h - tile_size)
wi_list = list(range(0, w - tile_size + 1, tile_stride))
if (w - tile_size) % tile_stride != 0:
wi_list.append(w - tile_size)
coords = []
for hi in hi_list:
for wi in wi_list:
coords.append((hi, hi + tile_size, wi, wi + tile_size))
return coords
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