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rk_guide_func.py
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import torch
import torch.nn.functional as F
import gc
from einops import rearrange
from .sigmas import get_sigmas
from .latents import hard_light_blend, normalize_latent, initialize_or_scale
from .rk_method import RK_Method
from .helper import get_extra_options_kv, extra_options_flag, get_cosine_similarity, get_extra_options_list
import itertools
from typing import Tuple
# test_mode bit flags
TEST_MODE_ENABLE = 0b0001
TEST_CLONE_LGW_MASK = 0b0010
def normalize_inputs(x, y0, y0_inv, guide_mode, extra_options) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
if guide_mode == "epsilon_guide_mean_std_from_bkg":
y0 = normalize_latent(y0, y0_inv)
input_norm = get_extra_options_kv("input_norm", "", extra_options)
input_std = float(get_extra_options_kv("input_std", "1.0", extra_options))
if input_norm == "input_ch_mean_set_std_to":
x = normalize_latent(x, set_std=input_std)
elif input_norm == "input_ch_set_std_to":
x = normalize_latent(x, set_std=input_std, mean=False)
elif input_norm == "input_mean_set_std_to":
x = normalize_latent(x, set_std=input_std, channelwise=False)
elif input_norm == "input_std_set_std_to":
x = normalize_latent(x, set_std=input_std, mean=False, channelwise=False)
return x, y0, y0_inv
class LatentGuide:
def __init__(self, guides, x, model, sigmas, UNSAMPLE, LGW_MASK_RESCALE_MIN, extra_options, device='cuda', offload_device='cpu', dtype=torch.float64, max_steps=10000):
self.model = model
self.device = device
self.offload_device = offload_device
self.sigma_min = model.inner_model.inner_model.model_sampling.sigma_min.to(dtype)
self.sigma_max = model.inner_model.inner_model.model_sampling.sigma_max.to(dtype)
self.sigmas = sigmas
self.UNSAMPLE = UNSAMPLE
self.SAMPLE = (sigmas[0] > sigmas[1])
self.extra_options = extra_options
self.y0 = torch.zeros_like(x)
self.y0_inv = torch.zeros_like(x)
self.guide_mode = ""
self.mask = None
self.mask_inv = None
self.latent_guide = None
self.latent_guide_inv = None
self.lgw_masks = []
self.lgw_masks_inv = []
self.lgw, self.lgw_inv = [torch.full_like(sigmas, 0.) for _ in range(2)]
self.guide_cossim_cutoff_, self.guide_bkg_cossim_cutoff_ = 1.0, 1.0
test_mode_names = get_extra_options_kv("test_mode", "", extra_options)
test_mode = 0b0000
if "TEST_MODE_ENABLE" in test_mode_names:
test_mode |= TEST_MODE_ENABLE
if "TEST_CLONE_LGW_MASK" in test_mode_names:
test_mode |= TEST_CLONE_LGW_MASK
self.test_mode = test_mode
latent_guide_weight, latent_guide_weight_inv = 0., 0.
latent_guide_weights = torch.zeros_like(sigmas)
latent_guide_weights_inv = torch.zeros_like(sigmas)
if guides is not None:
(self.guide_mode, latent_guide_weight, latent_guide_weight_inv,
latent_guide_weights, latent_guide_weights_inv,
self.latent_guide, self.latent_guide_inv,
latent_guide_mask, latent_guide_mask_inv,
scheduler_, scheduler_inv_, steps_, steps_inv_,
denoise_, denoise_inv_) = guides
self.mask, self.mask_inv = latent_guide_mask, latent_guide_mask_inv
self.guide_cossim_cutoff_, self.guide_bkg_cossim_cutoff_ = denoise_, denoise_inv_
if latent_guide_weights is None:
latent_guide_weights = get_sigmas(model, scheduler_, steps_, 1.0).to(x.dtype)
if latent_guide_weights_inv is None:
latent_guide_weights_inv = get_sigmas(model, scheduler_inv_, steps_inv_, 1.0).to(x.dtype)
latent_guide_weights = initialize_or_scale(latent_guide_weights, latent_guide_weight, max_steps).to(dtype)
latent_guide_weights_inv = initialize_or_scale(latent_guide_weights_inv, latent_guide_weight_inv, max_steps).to(dtype)
latent_guide_weights = F.pad(latent_guide_weights, (0, max_steps), value=0.0)
latent_guide_weights_inv = F.pad(latent_guide_weights_inv, (0, max_steps), value=0.0)
if latent_guide_weights is not None:
self.lgw = latent_guide_weights.to(x.device)
if latent_guide_weights_inv is not None:
self.lgw_inv = latent_guide_weights_inv.to(x.device)
self.mask, self.lgw_mask_rescale_min = prepare_mask(x, self.mask, LGW_MASK_RESCALE_MIN)
if self.mask_inv is not None:
self.mask_inv, self.lgw_mask_rescale_min = prepare_mask(x, self.mask_inv, LGW_MASK_RESCALE_MIN)
elif not self.SAMPLE:
self.mask_inv = (1-self.mask)
gc.collect()
def get_guide_masks(self, step):
if len(self.lgw_masks) <= step:
lgw_mask, lgw_mask_inv = self.prepare_weighted_masks(step)
self.lgw_masks.append(lgw_mask.to(self.device))
self.lgw_masks_inv.append(lgw_mask_inv.to(self.device) if lgw_mask_inv is not None else None)
if self.test_mode & (TEST_CLONE_LGW_MASK & TEST_MODE_ENABLE):
try:
lgw_mask = self.lgw_masks[step].clone()
lgw_mask_inv = self.lgw_masks_inv[step].clone() if self.lgw_masks_inv[step] is not None else None
return lgw_mask, lgw_mask_inv
except Exception as e:
print(f"Error cloning masks for test mode: {e}")
raise
gc.collect()
return self.lgw_masks[step], self.lgw_masks_inv[step]
def prepare_weighted_masks(self, step):
lgw_ = self.lgw[step]
lgw_inv_ = self.lgw_inv[step]
mask = torch.ones_like(self.y0) if self.mask is None else self.mask
mask_inv = torch.zeros_like(mask) if self.mask_inv is None else self.mask_inv
if self.lgw_mask_rescale_min:
lgw_mask = mask * (1-lgw_) + lgw_
lgw_mask_inv = (1-mask) * (1-lgw_inv_) + lgw_inv_
else:
if self.latent_guide is not None:
lgw_mask = mask * lgw_
else:
lgw_mask = torch.zeros_like(mask)
if self.latent_guide_inv is not None:
if mask_inv is not None:
inv_mask = 1-mask_inv
scaled_mask = (1-mask) * lgw_inv_
lgw_mask_inv = torch.minimum(inv_mask, scaled_mask)
del inv_mask, scaled_mask
else:
lgw_mask_inv = (1-mask) * lgw_inv_
else:
lgw_mask_inv = torch.zeros_like(mask)
return lgw_mask, lgw_mask_inv
def init_guides(self, x, noise_sampler, latent_guide=None, latent_guide_inv=None):
x_ = x.clone().to(self.device)
self.y0, self.y0_inv = torch.zeros_like(x_), torch.zeros_like(x_)
latent_guide = self.latent_guide if latent_guide is None else latent_guide
latent_guide_inv = self.latent_guide_inv if latent_guide_inv is None else latent_guide_inv
if latent_guide is not None:
if type(latent_guide) == dict:
latent_guide_samples = self.model.inner_model.inner_model.process_latent_in(latent_guide['samples']).clone().to(x_.device)
else:
latent_guide_samples = latent_guide.clone().to(x_.device)
if self.SAMPLE:
self.y0 = latent_guide_samples.clone().to(x_.device)
elif self.UNSAMPLE: # and self.mask is not None:
x_ = ((1-self.mask) * x_ + self.mask * latent_guide_samples).to(x_.device)
else:
x_ = latent_guide_samples.to(x_.device)
del latent_guide_samples
if latent_guide_inv is not None:
if isinstance(latent_guide_inv, dict):
latent_guide_inv_samples = self.model.inner_model.inner_model.process_latent_in(latent_guide_inv['samples']).clone().to(x_.device)
else:
latent_guide_inv_samples = latent_guide_inv.clone().to(x_.device)
if self.SAMPLE:
self.y0_inv = latent_guide_inv_samples.clone().to(x_.device)
elif self.UNSAMPLE: # and self.mask is not None:
x_ = ((1-self.mask_inv) * x + self.mask_inv * latent_guide_inv_samples).to(x_.device) #fixed old approach, which was mask, (1-mask)
else:
x_ = latent_guide_inv_samples.to(x_.device) #THIS COULD LEAD TO WEIRD BEHAVIOR! OVERWRITING X WITH LG_INV AFTER SETTING TO LG above!
del latent_guide_inv_samples
if self.UNSAMPLE and not self.SAMPLE: #sigma_next > sigma:
self.y0 = noise_sampler(sigma=self.sigma_max, sigma_next=self.sigma_min).to(x_.device)
self.y0 = (self.y0 - self.y0.mean()) / self.y0.std()
self.y0_inv = noise_sampler(sigma=self.sigma_max, sigma_next=self.sigma_min).to(x_.device)
self.y0_inv = (self.y0_inv - self.y0_inv.mean()) / self.y0_inv.std()
x_, self.y0, self.y0_inv = normalize_inputs(x_, self.y0, self.y0_inv, self.guide_mode, self.extra_options)
return x_
def process_guides_substep(self, x_0, x_, eps_, data_, row, step, sigma, sigma_next, sigma_down, s_, unsample_resample_scale, rk, rk_type, extra_options, frame_weights_grp=None) -> Tuple[torch.Tensor, torch.Tensor]:
dynamic_guides_mean_std_flag = extra_options_flag("dynamic_guides_mean_std", extra_options)
dynamic_guides_inv_flag = extra_options_flag("dynamic_guides_inv", extra_options)
dynamic_guides_mean_flag = extra_options_flag("dynamic_guides_mean", extra_options)
y0 = self.y0
if self.y0.shape[0] > 1:
y0 = self.y0[min(step, self.y0.shape[0]-1)].unsqueeze(0)
y0_inv = self.y0_inv
lgw_mask, lgw_mask_inv = self.get_guide_masks(step)
lgw = self.lgw[step].clone()
lgw_inv = self.lgw_inv[step].clone()
if x_0.dim() == 5 and frame_weights_grp is not None:
apply_frame_weights(lgw_mask, frame_weights_grp[0])
apply_frame_weights(lgw_mask_inv, frame_weights_grp[1])
if self.guide_mode:
with torch.no_grad():
data_norm = data_[row] - data_[row].mean(dim=(-2,-1), keepdim=True)
y0_norm = y0 - y0.mean(dim=(-2,-1), keepdim=True)
y0_inv_norm = y0_inv - y0_inv.mean(dim=(-2,-1), keepdim=True)
y0_cossim = get_cosine_similarity(data_norm*lgw_mask, y0_norm *lgw_mask)
y0_cossim_inv = get_cosine_similarity(data_norm*lgw_mask_inv, y0_inv_norm*lgw_mask_inv)
if y0_cossim < self.guide_cossim_cutoff_ or y0_cossim_inv < self.guide_bkg_cossim_cutoff_:
lgw_mask_cossim, lgw_mask_cossim_inv = lgw_mask, lgw_mask_inv
if y0_cossim >= self.guide_cossim_cutoff_:
lgw_mask_cossim = torch.zeros_like(lgw_mask)
if y0_cossim_inv >= self.guide_bkg_cossim_cutoff_:
lgw_mask_cossim_inv = torch.zeros_like(lgw_mask_inv)
lgw_mask = lgw_mask_cossim
lgw_mask_inv = lgw_mask_cossim_inv
else:
return eps_, x_
else:
return eps_, x_
if self.UNSAMPLE and RK_Method.is_exponential(rk_type):
if not (extra_options_flag("disable_power_unsample", extra_options) or extra_options_flag("disable_power_resample", extra_options)):
extra_options += "\npower_unsample\npower_resample\n"
if not extra_options_flag("disable_lgw_scaling_substep_ch_mean_std", extra_options):
extra_options += "\nsubstep_eps_ch_mean_std\n"
s_in = x_0.new_ones([x_0.shape[0]])
eps_orig = eps_.clone()
if dynamic_guides_mean_std_flag:
y_shift, y_inv_shift = normalize_latent([y0, y0_inv], [data_, data_])
y0 = y_shift
if dynamic_guides_inv_flag:
y0_inv = y_inv_shift
if dynamic_guides_mean_flag:
y_shift, y_inv_shift = normalize_latent([y0, y0_inv], [data_, data_], std=False)
y0 = y_shift
if dynamic_guides_inv_flag:
y0_inv = y_inv_shift
if 'y_shift' in locals():
del y_shift
if 'y_inv_shift' in locals():
del y_inv_shift
if "data" == self.guide_mode:
if self.latent_guide_inv is not None:
y0_tmp = data_[row].clone()
y0_tmp.mul_(1-lgw_mask).add_(lgw_mask * y0)
y0_tmp.mul_(1-lgw_mask_inv).add_(lgw_mask_inv * y0_inv)
else:
y0_tmp = y0.clone()
x_[row+1].copy_(y0_tmp + eps_[row])
if 'y0_tmp' in locals():
del y0_tmp
if self.guide_mode == "data_projection":
d_lerp = data_[row].clone()
d_lerp.add_(lgw_mask * (y0-data_[row]))
d_lerp.add_(lgw_mask_inv * (y0_inv-data_[row]))
d_collinear_d_lerp = get_collinear(data_[row], d_lerp)
d_lerp_ortho_d = get_orthogonal(d_lerp, data_[row])
data_[row].copy_(d_collinear_d_lerp + d_lerp_ortho_d)
x_[row+1].copy_(data_[row] + eps_[row] * sigma)
if 'd_lerp' in locals():
del d_lerp
if 'd_collinear_d_lerp' in locals():
del d_collinear_d_lerp
if 'd_lerp_ortho_d' in locals():
del d_lerp_ortho_d
elif "epsilon" in self.guide_mode:
if sigma > sigma_next:
tol_value = float(get_extra_options_kv("tol", "-1.0", extra_options))
if tol_value >= 0 and (lgw > 0 or lgw_inv > 0):
for b, c in itertools.product(range(x_0.shape[0]), range(x_0.shape[1])):
current_diff = torch.norm(data_[row][b][c] - y0 [b][c])
current_diff_inv = torch.norm(data_[row][b][c] - y0_inv[b][c])
lgw_scaled = torch.nan_to_num(1-(tol_value/current_diff), 0)
lgw_scaled_inv = torch.nan_to_num(1-(tol_value/current_diff_inv), 0)
lgw_tmp = min(lgw , lgw_scaled)
lgw_tmp_inv = min(lgw_inv, lgw_scaled_inv)
lgw_mask_clamp = torch.clamp(lgw_mask, max=lgw_tmp)
lgw_mask_clamp_inv = torch.clamp(lgw_mask_inv, max=lgw_tmp_inv)
eps_row, eps_row_inv = get_guide_epsilon_substep(x_0, x_, y0, y0_inv, s_, row, rk_type, b, c)
eps_[row][b][c] = eps_[row][b][c] + lgw_mask_clamp[b][c] * (eps_row - eps_[row][b][c]) + lgw_mask_clamp_inv[b][c] * (eps_row_inv - eps_[row][b][c])
elif self.guide_mode == "epsilon_projection":
with torch.no_grad():
eps_row, eps_row_inv = get_guide_epsilon_substep(x_0, x_, y0, y0_inv, s_, row, rk_type)
if extra_options_flag("eps_proj_v2", extra_options):
eps_row_lerp_fg = eps_[row].clone()
eps_row_lerp_fg.add_(lgw_mask * (eps_row - eps_[row]))
eps_row_lerp_bg = eps_[row].clone()
eps_row_lerp_bg.add_(lgw_mask_inv * (eps_row_inv - eps_[row]))
eps_collinear_fg = get_collinear(eps_[row], eps_row_lerp_fg)
eps_ortho_fg = get_orthogonal(eps_row_lerp_fg, eps_[row])
eps_collinear_bg = get_collinear(eps_[row], eps_row_lerp_bg)
eps_ortho_bg = get_orthogonal(eps_row_lerp_bg, eps_[row])
eps_[row].add_(lgw_mask * (eps_collinear_fg + eps_ortho_fg - eps_[row]))
eps_[row].add_(lgw_mask_inv * (eps_collinear_bg + eps_ortho_bg - eps_[row]))
elif extra_options_flag("eps_proj_v3", extra_options):
eps_collinear_eps_lerp_fg = get_collinear(eps_[row], eps_row)
eps_lerp_ortho_eps_fg = get_orthogonal(eps_row, eps_[row])
eps_collinear_eps_lerp_bg = get_collinear(eps_[row], eps_row_inv)
eps_lerp_ortho_eps_bg = get_orthogonal(eps_row_inv, eps_[row])
eps_[row] = eps_[row] + lgw_mask * (eps_collinear_eps_lerp_fg + eps_lerp_ortho_eps_fg - eps_[row]) + lgw_mask_inv * (eps_collinear_eps_lerp_bg + eps_lerp_ortho_eps_bg - eps_[row])
elif extra_options_flag("eps_proj_v5", extra_options):
eps2g_collin = get_collinear(eps_[row], eps_row)
g2eps_ortho = get_orthogonal(eps_row, eps_[row])
g2eps_collin = get_collinear(eps_row, eps_[row])
eps2g_ortho = get_orthogonal(eps_[row], eps_row)
eps2i_collin = get_collinear(eps_[row], eps_row_inv)
i2eps_ortho = get_orthogonal(eps_row_inv, eps_[row])
i2eps_collin = get_collinear(eps_row_inv, eps_[row])
eps2i_ortho = get_orthogonal(eps_[row], eps_row_inv)
#eps_[row] = (eps2g_collin+g2eps_ortho) + (g2eps_collin+eps2g_ortho) + (eps2i_collin+i2eps_ortho) + (i2eps_collin+eps2i_ortho)
#eps_[row] = eps_[row] + lgw_mask * (eps2g_collin+g2eps_ortho) + (1-lgw_mask) * (g2eps_collin+eps2g_ortho) + lgw_mask_inv * (eps2i_collin+i2eps_ortho) + (1-lgw_mask_inv) * (i2eps_collin+eps2i_ortho)
eps_[row] = lgw_mask * (eps2g_collin+g2eps_ortho) - lgw_mask * (g2eps_collin+eps2g_ortho) + lgw_mask_inv * (eps2i_collin+i2eps_ortho) - lgw_mask_inv * (i2eps_collin+eps2i_ortho)
#eps_[row] = eps_[row] + lgw_mask * (eps_collinear_eps_lerp_fg + eps_lerp_ortho_eps_fg - eps_[row]) + lgw_mask_inv * (eps_collinear_eps_lerp_bg + eps_lerp_ortho_eps_bg - eps_[row])
elif extra_options_flag("eps_proj_v4a", extra_options):
eps_row_lerp = eps_[row] + lgw_mask * (eps_row-eps_[row]) + lgw_mask_inv * (eps_row_inv-eps_[row])
eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
eps_lerp_ortho_eps = get_orthogonal(eps_row_lerp, eps_[row])
eps_[row] = (1 - torch.clamp(lgw_mask + lgw_mask_inv, max=1.0)) * eps_[row] + torch.clamp((lgw_mask + lgw_mask_inv), max=1.0) * (eps_collinear_eps_lerp + eps_lerp_ortho_eps)
elif extra_options_flag("eps_proj_v4b", extra_options):
eps_row_lerp = eps_[row] + lgw_mask * (eps_row-eps_[row]) + lgw_mask_inv * (eps_row_inv-eps_[row])
eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
eps_lerp_ortho_eps = get_orthogonal(eps_row_lerp, eps_[row])
eps_[row] = (1 - (lgw_mask + lgw_mask_inv)/2) * eps_[row] + ((lgw_mask + lgw_mask_inv)/2) * (eps_collinear_eps_lerp + eps_lerp_ortho_eps)
elif extra_options_flag("eps_proj_v4c", extra_options):
eps_row_lerp = eps_[row] + lgw_mask * (eps_row-eps_[row]) + lgw_mask_inv * (eps_row_inv-eps_[row])
eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
eps_lerp_ortho_eps = get_orthogonal(eps_row_lerp, eps_[row])
lgw_mask_sum = (lgw_mask + lgw_mask_inv)
eps_[row] = (1 - (lgw_mask + lgw_mask_inv)/2) * eps_[row] + ((lgw_mask + lgw_mask_inv)/2) * (eps_collinear_eps_lerp + eps_lerp_ortho_eps)
elif extra_options_flag("eps_proj_v4e", extra_options):
eps_row_lerp = eps_[row] + lgw_mask * (eps_row-eps_[row]) + lgw_mask_inv * (eps_row_inv-eps_[row])
eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
eps_lerp_ortho_eps = get_orthogonal(eps_row_lerp, eps_[row])
eps_sum = eps_collinear_eps_lerp + eps_lerp_ortho_eps
eps_[row] = eps_[row] + self.mask * (eps_sum - eps_[row]) + self.mask_inv * (eps_sum - eps_[row])
elif extra_options_flag("eps_proj_self1", extra_options):
eps_row_lerp = eps_[row] + self.mask * (eps_row-eps_[row]) + self.mask_inv * (eps_row_inv-eps_[row])
eps_collinear_eps_lerp = get_collinear(eps_[row], eps_[row])
eps_lerp_ortho_eps = get_orthogonal(eps_[row], eps_[row])
eps_[row] = eps_collinear_eps_lerp + eps_lerp_ortho_eps
elif extra_options_flag("eps_proj_v4z", extra_options):
eps_row_lerp = eps_[row] + self.mask * (eps_row-eps_[row]) + self.mask_inv * (eps_row_inv-eps_[row])
eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
eps_lerp_ortho_eps = get_orthogonal(eps_row_lerp, eps_[row])
peak = max(lgw, lgw_inv)
lgw_mask_sum = (lgw_mask + lgw_mask_inv)
eps_sum = eps_collinear_eps_lerp + eps_lerp_ortho_eps
#NOT FINISHED!!!
#eps_[row] = eps_[row] + lgw_mask * (eps_sum - eps_[row]) + lgw_mask_inv * (eps_sum - eps_[row])
elif extra_options_flag("eps_proj_v5", extra_options):
eps_row_lerp = eps_[row] + lgw_mask * (eps_row-eps_[row]) + lgw_mask_inv * (eps_row_inv-eps_[row])
eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
eps_lerp_ortho_eps = get_orthogonal(eps_row_lerp, eps_[row])
eps_[row] = ((lgw_mask + lgw_mask_inv)==0) * eps_[row] + ((lgw_mask + lgw_mask_inv)>0) * (eps_collinear_eps_lerp + eps_lerp_ortho_eps)
elif extra_options_flag("eps_proj_v6", extra_options):
eps_row_lerp = eps_[row] + lgw_mask * (eps_row-eps_[row]) + lgw_mask_inv * (eps_row_inv-eps_[row])
eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
eps_lerp_ortho_eps = get_orthogonal(eps_row_lerp, eps_[row])
eps_[row] = ((lgw_mask * lgw_mask_inv)==0) * eps_[row] + ((lgw_mask * lgw_mask_inv)>0) * (eps_collinear_eps_lerp + eps_lerp_ortho_eps)
elif extra_options_flag("eps_proj_old_default", extra_options):
eps_row_lerp = eps_[row] + lgw_mask * (eps_row-eps_[row]) + lgw_mask_inv * (eps_row_inv-eps_[row])
#eps_row_lerp = eps_[row] + lgw_mask * (eps_row-eps_[row]) + (1-lgw_mask) * (eps_row_inv-eps_[row])
eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
eps_lerp_ortho_eps = get_orthogonal(eps_row_lerp, eps_[row])
eps_[row] = eps_collinear_eps_lerp + eps_lerp_ortho_eps
else: #elif extra_options_flag("eps_proj_v4d", extra_options):
#if row > 0:
#lgw_mask_factor = float(get_extra_options_kv("substep_lgw_mask_factor", "1.0", extra_options))
#lgw_mask_inv_factor = float(get_extra_options_kv("substep_lgw_mask_inv_factor", "1.0", extra_options))
lgw_mask_factor = 1
if extra_options_flag("substep_eps_proj_scaling", extra_options):
lgw_mask_factor = 1/(row+1)
if extra_options_flag("substep_eps_proj_factors", extra_options):
value_str = get_extra_options_list("substep_eps_proj_factors", "", extra_options)
float_list = [float(item.strip()) for item in value_str.split(',') if item.strip()]
lgw_mask_factor = float_list[row]
eps_row_lerp = eps_[row] + self.mask * (eps_row-eps_[row]) + (1-self.mask) * (eps_row_inv-eps_[row])
eps_collinear_eps_lerp = get_collinear(eps_[row], eps_row_lerp)
eps_lerp_ortho_eps = get_orthogonal(eps_row_lerp, eps_[row])
eps_sum = eps_collinear_eps_lerp + eps_lerp_ortho_eps
eps_[row] = eps_[row] + lgw_mask_factor*lgw_mask * (eps_sum - eps_[row]) + lgw_mask_factor*lgw_mask_inv * (eps_sum - eps_[row])
# Clean up all intermediate tensors
locals_to_delete = ['eps_row', 'eps_row_inv', 'eps_row_lerp',
'eps_row_lerp_fg', 'eps_row_lerp_bg', 'eps_collinear_fg',
'eps_ortho_fg', 'eps_collinear_bg', 'eps_ortho_bg',
'eps_collinear_eps_lerp', 'eps_lerp_ortho_eps',
'eps2g_collin', 'g2eps_ortho', 'g2eps_collin', 'eps2g_ortho',
'eps2i_collin', 'i2eps_ortho', 'i2eps_collin', 'eps2i_ortho',
'eps_sum', 'lgw_mask_sum', 'peak']
for var in locals_to_delete:
if var in locals():
del locals()[var]
elif extra_options_flag("disable_lgw_scaling", extra_options):
eps_row, eps_row_inv = get_guide_epsilon_substep(x_0, x_, y0, y0_inv, s_, row, rk_type)
eps_[row] = eps_[row] + lgw_mask * (eps_row - eps_[row]) + lgw_mask_inv * (eps_row_inv - eps_[row])
elif (lgw > 0 or lgw_inv > 0): # default old channelwise epsilon
avg, avg_inv = 0, 0
for b, c in itertools.product(range(x_0.shape[0]), range(x_0.shape[1])):
avg += torch.norm(data_[row][b][c] - y0 [b][c])
avg_inv += torch.norm(data_[row][b][c] - y0_inv[b][c])
avg /= x_0.shape[1]
avg_inv /= x_0.shape[1]
for b, c in itertools.product(range(x_0.shape[0]), range(x_0.shape[1])):
ratio = torch.nan_to_num(torch.norm(data_[row][b][c] - y0 [b][c]) / avg, 0)
ratio_inv = torch.nan_to_num(torch.norm(data_[row][b][c] - y0_inv[b][c]) / avg_inv, 0)
eps_row, eps_row_inv = get_guide_epsilon_substep(x_0, x_, y0, y0_inv, s_, row, rk_type, b, c)
eps_[row][b][c] = eps_[row][b][c] + ratio * lgw_mask[b][c] * (eps_row - eps_[row][b][c]) + ratio_inv * lgw_mask_inv[b][c] * (eps_row_inv - eps_[row][b][c])
temporal_smoothing = float(get_extra_options_kv("temporal_smoothing", "0.0", extra_options))
if temporal_smoothing > 0:
eps_[row] = apply_temporal_smoothing(eps_[row], temporal_smoothing)
elif (self.UNSAMPLE or self.guide_mode in {"resample", "unsample"}) and (lgw > 0 or lgw_inv > 0):
cvf = rk.get_epsilon(x_0, x_[row+1], y0, sigma, s_[row], sigma_down, unsample_resample_scale, extra_options)
if self.UNSAMPLE and sigma > sigma_next and self.latent_guide_inv is not None:
cvf_inv = rk.get_epsilon(x_0, x_[row+1], y0_inv, sigma, s_[row], sigma_down, unsample_resample_scale, extra_options)
else:
cvf_inv = torch.zeros_like(cvf)
tol_value = float(get_extra_options_kv("tol", "-1.0", extra_options))
if tol_value >= 0:
for b, c in itertools.product(range(x_0.shape[0]), range(x_0.shape[1])):
current_diff = torch.norm(data_[row][b][c] - y0 [b][c])
current_diff_inv = torch.norm(data_[row][b][c] - y0_inv[b][c])
lgw_scaled = torch.nan_to_num(1-(tol_value/current_diff), 0)
lgw_scaled_inv = torch.nan_to_num(1-(tol_value/current_diff_inv), 0)
lgw_tmp = min(lgw , lgw_scaled)
lgw_tmp_inv = min(lgw_inv, lgw_scaled_inv)
lgw_mask_clamp = torch.clamp(lgw_mask, max=lgw_tmp)
lgw_mask_clamp_inv = torch.clamp(lgw_mask_inv, max=lgw_tmp_inv)
eps_[row][b][c] = eps_[row][b][c] + lgw_mask_clamp[b][c] * (cvf[b][c] - eps_[row][b][c]) + lgw_mask_clamp_inv[b][c] * (cvf_inv[b][c] - eps_[row][b][c])
elif extra_options_flag("disable_lgw_scaling", extra_options):
eps_[row].add_(lgw_mask * (cvf - eps_[row]) + lgw_mask_inv * (cvf_inv - eps_[row]))
else:
avg, avg_inv = 0, 0
for b, c in itertools.product(range(x_0.shape[0]), range(x_0.shape[1])):
avg += torch.norm(lgw_mask [b][c] * data_[row][b][c] - lgw_mask [b][c] * y0 [b][c])
avg_inv += torch.norm(lgw_mask_inv[b][c] * data_[row][b][c] - lgw_mask_inv[b][c] * y0_inv[b][c])
avg /= x_0.shape[1]
avg_inv /= x_0.shape[1]
for b, c in itertools.product(range(x_0.shape[0]), range(x_0.shape[1])):
ratio = torch.nan_to_num(torch.norm(lgw_mask [b][c] * data_[row][b][c] - lgw_mask [b][c] * y0 [b][c]) / avg, 0)
ratio_inv = torch.nan_to_num(torch.norm(lgw_mask_inv[b][c] * data_[row][b][c] - lgw_mask_inv[b][c] * y0_inv[b][c]) / avg_inv, 0)
eps_[row][b][c] = eps_[row][b][c] + ratio * lgw_mask[b][c] * (cvf[b][c] - eps_[row][b][c]) + ratio_inv * lgw_mask_inv[b][c] * (cvf_inv[b][c] - eps_[row][b][c])
if extra_options_flag("substep_eps_ch_mean_std", extra_options):
eps_[row] = normalize_latent(eps_[row], eps_orig[row])
if extra_options_flag("substep_eps_ch_mean", extra_options):
eps_[row] = normalize_latent(eps_[row], eps_orig[row], std=False)
if extra_options_flag("substep_eps_ch_std", extra_options):
eps_[row] = normalize_latent(eps_[row], eps_orig[row], mean=False)
if extra_options_flag("substep_eps_mean_std", extra_options):
eps_[row] = normalize_latent(eps_[row], eps_orig[row], channelwise=False)
if extra_options_flag("substep_eps_mean", extra_options):
eps_[row] = normalize_latent(eps_[row], eps_orig[row], std=False, channelwise=False)
if extra_options_flag("substep_eps_std", extra_options):
eps_[row] = normalize_latent(eps_[row], eps_orig[row], mean=False, channelwise=False)
return eps_, x_
@torch.no_grad
def process_guides_poststep(self, x, denoised, eps, step, extra_options) -> torch.Tensor:
x_orig = x.clone()
mean_weight = float(get_extra_options_kv("mean_weight", "0.01", extra_options))
y0 = self.y0
if self.y0.shape[0] > 1:
y0 = self.y0[min(step, self.y0.shape[0]-1)].unsqueeze(0)
y0_inv = self.y0_inv
lgw_mask, lgw_mask_inv = self.get_guide_masks(step)
mask = self.mask #needed for bitwise mask below
latent_guide = self.latent_guide
latent_guide_inv = self.latent_guide_inv
guide_mode = self.guide_mode
UNSAMPLE = self.UNSAMPLE
if self.guide_mode:
data_norm = denoised - denoised.mean(dim=(-2,-1), keepdim=True)
y0_norm = y0 - y0.mean(dim=(-2,-1), keepdim=True)
y0_inv_norm = y0_inv - y0_inv.mean(dim=(-2,-1), keepdim=True)
y0_cossim = get_cosine_similarity(data_norm*lgw_mask, y0_norm *lgw_mask)
y0_cossim_inv = get_cosine_similarity(data_norm*lgw_mask_inv, y0_inv_norm*lgw_mask_inv)
# Clean up temporary tensors
del data_norm, y0_norm, y0_inv_norm
if y0_cossim < self.guide_cossim_cutoff_ or y0_cossim_inv < self.guide_bkg_cossim_cutoff_:
lgw_mask_cossim, lgw_mask_cossim_inv = lgw_mask, lgw_mask_inv
if y0_cossim >= self.guide_cossim_cutoff_:
lgw_mask_cossim = torch.zeros_like(lgw_mask)
if y0_cossim_inv >= self.guide_bkg_cossim_cutoff_:
lgw_mask_cossim_inv = torch.zeros_like(lgw_mask_inv)
lgw_mask = lgw_mask_cossim
lgw_mask_inv = lgw_mask_cossim_inv
else:
return x
if guide_mode in {"epsilon_dynamic_mean_std", "epsilon_dynamic_mean", "epsilon_dynamic_std", "epsilon_dynamic_mean_from_bkg"}:
denoised_masked = denoised * ((mask==1)*mask)
denoised_masked_inv = denoised * ((mask==0)*(1-mask))
d_shift, d_shift_inv = torch.zeros_like(x), torch.zeros_like(x)
for b, c in itertools.product(range(x.shape[0]), range(x.shape[1])):
denoised_mask = denoised[b][c][mask[b][c] == 1]
denoised_mask_inv = denoised[b][c][mask[b][c] == 0]
if guide_mode == "epsilon_dynamic_mean_std":
d_shift[b][c] = (denoised_masked[b][c] - denoised_mask.mean()) / denoised_mask.std()
d_shift[b][c] = (d_shift[b][c] * denoised_mask_inv.std()) + denoised_mask_inv.mean()
elif guide_mode == "epsilon_dynamic_mean":
d_shift[b][c] = denoised_masked[b][c] - denoised_mask.mean() + denoised_mask_inv.mean()
d_shift_inv[b][c] = denoised_masked_inv[b][c] - denoised_mask_inv.mean() + denoised_mask.mean()
elif guide_mode == "epsilon_dynamic_mean_from_bkg":
d_shift[b][c] = denoised_masked[b][c] - denoised_mask.mean() + denoised_mask_inv.mean()
if guide_mode in {"epsilon_dynamic_mean_std", "epsilon_dynamic_mean_from_bkg"}:
denoised_shifted = denoised + mean_weight * lgw_mask * (d_shift - denoised_masked)
elif guide_mode == "epsilon_dynamic_mean":
denoised_shifted = denoised + mean_weight * lgw_mask * (d_shift - denoised_masked) + mean_weight * lgw_mask_inv * (d_shift_inv - denoised_masked_inv)
x = denoised_shifted + eps
if UNSAMPLE == False and (latent_guide is not None or latent_guide_inv is not None) and guide_mode in ("hard_light", "blend", "blend_projection", "mean_std", "mean", "mean_tiled", "std"):
if guide_mode == "hard_light":
d_shift, d_shift_inv = hard_light_blend(y0, denoised), hard_light_blend(y0_inv, denoised)
elif guide_mode == "blend":
d_shift, d_shift_inv = y0, y0_inv
elif guide_mode == "blend_projection":
#d_shift = get_collinear(denoised, y0)
#d_shift_inv = get_collinear(denoised, y0_inv)
d_lerp = denoised + lgw_mask * (y0-denoised) + lgw_mask_inv * (y0_inv-denoised)
d_collinear_d_lerp = get_collinear(denoised, d_lerp)
d_lerp_ortho_d = get_orthogonal(d_lerp, denoised)
denoised_shifted = d_collinear_d_lerp + d_lerp_ortho_d
x = denoised_shifted + eps
return x
elif guide_mode == "mean_std":
d_shift, d_shift_inv = normalize_latent([denoised, denoised], [y0, y0_inv])
elif guide_mode == "mean":
d_shift, d_shift_inv = normalize_latent([denoised, denoised], [y0, y0_inv], std=False)
elif guide_mode == "std":
d_shift, d_shift_inv = normalize_latent([denoised, denoised], [y0, y0_inv], mean=False)
elif guide_mode == "mean_tiled":
mean_tile_size = int(get_extra_options_kv("mean_tile", "8", extra_options))
y0_tiled = rearrange(y0, "b c (h t1) (w t2) -> (t1 t2) b c h w", t1=mean_tile_size, t2=mean_tile_size)
y0_inv_tiled = rearrange(y0_inv, "b c (h t1) (w t2) -> (t1 t2) b c h w", t1=mean_tile_size, t2=mean_tile_size)
denoised_tiled = rearrange(denoised, "b c (h t1) (w t2) -> (t1 t2) b c h w", t1=mean_tile_size, t2=mean_tile_size)
d_shift_tiled, d_shift_inv_tiled = torch.zeros_like(y0_tiled), torch.zeros_like(y0_tiled)
for i in range(y0_tiled.shape[0]):
d_shift_tiled[i], d_shift_inv_tiled[i] = normalize_latent([denoised_tiled[i], denoised_tiled[i]], [y0_tiled[i], y0_inv_tiled[i]], std=False)
d_shift = rearrange(d_shift_tiled, "(t1 t2) b c h w -> b c (h t1) (w t2)", t1=mean_tile_size, t2=mean_tile_size)
d_shift_inv = rearrange(d_shift_inv_tiled, "(t1 t2) b c h w -> b c (h t1) (w t2)", t1=mean_tile_size, t2=mean_tile_size)
if guide_mode in ("hard_light", "blend", "mean_std", "mean", "mean_tiled", "std"):
if latent_guide_inv is None:
denoised_shifted = denoised + lgw_mask * (d_shift - denoised)
else:
denoised_shifted = denoised + lgw_mask * (d_shift - denoised) + lgw_mask_inv * (d_shift_inv - denoised)
if extra_options_flag("poststep_denoised_ch_mean_std", extra_options):
denoised_shifted = normalize_latent(denoised_shifted, denoised)
if extra_options_flag("poststep_denoised_ch_mean", extra_options):
denoised_shifted = normalize_latent(denoised_shifted, denoised, std=False)
if extra_options_flag("poststep_denoised_ch_std", extra_options):
denoised_shifted = normalize_latent(denoised_shifted, denoised, mean=False)
if extra_options_flag("poststep_denoised_mean_std", extra_options):
denoised_shifted = normalize_latent(denoised_shifted, denoised, channelwise=False)
if extra_options_flag("poststep_denoised_mean", extra_options):
denoised_shifted = normalize_latent(denoised_shifted, denoised, std=False, channelwise=False)
if extra_options_flag("poststep_denoised_std", extra_options):
denoised_shifted = normalize_latent(denoised_shifted, denoised, mean=False, channelwise=False)
x = denoised_shifted + eps
if extra_options_flag("poststep_x_ch_mean_std", extra_options):
x = normalize_latent(x, x_orig)
if extra_options_flag("poststep_x_ch_mean", extra_options):
x = normalize_latent(x, x_orig, std=False)
if extra_options_flag("poststep_x_ch_std", extra_options):
x = normalize_latent(x, x_orig, mean=False)
if extra_options_flag("poststep_x_mean_std", extra_options):
x = normalize_latent(x, x_orig, channelwise=False)
if extra_options_flag("poststep_x_mean", extra_options):
x = normalize_latent(x, x_orig, std=False, channelwise=False)
if extra_options_flag("poststep_x_std", extra_options):
x = normalize_latent(x, x_orig, mean=False, channelwise=False)
return x
def apply_frame_weights(mask, frame_weights):
if frame_weights is not None:
for f in range(mask.shape[2]):
frame_weight = frame_weights[f]
mask[..., f:f+1, :, :] *= frame_weight
def prepare_mask(x, mask, LGW_MASK_RESCALE_MIN) -> Tuple[torch.Tensor, bool]:
if mask is None:
result_mask = torch.ones_like(x)
LGW_MASK_RESCALE_MIN = False
return mask, LGW_MASK_RESCALE_MIN
spatial_mask = mask.unsqueeze(1)
target_height = x.shape[-2]
target_width = x.shape[-1]
spatial_mask = F.interpolate(spatial_mask, size=(target_height, target_width), mode='bilinear', align_corners=False)
while spatial_mask.dim() < x.dim():
spatial_mask = spatial_mask.unsqueeze(2)
repeat_shape = [1] #batch
for i in range(1, x.dim() - 2):
repeat_shape.append(x.shape[i])
repeat_shape.extend([1, 1]) #height and width
mask = spatial_mask.repeat(*repeat_shape).to(x.dtype).to(x.device)
del spatial_mask
return mask, LGW_MASK_RESCALE_MIN
def apply_temporal_smoothing(tensor, temporal_smoothing) -> torch.Tensor:
if temporal_smoothing <= 0:
return tensor
if tensor.dim() != 5:
print(f"Temporal-smoothing is enabled but expected 5D video tensor, got {tensor.dim()}D")
return tensor
data_flat = None
temporal_kernel = None
data_smooth = None
try:
kernel_size = 5
padding = kernel_size // 2
temporal_kernel = torch.tensor([0.1, 0.2, 0.4, 0.2, 0.1], device=tensor.device, dtype=tensor.dtype) * temporal_smoothing
temporal_kernel[kernel_size//2] += (1 - temporal_smoothing)
temporal_kernel.div_(temporal_kernel.sum())
b, c, f, h, w = tensor.shape
data_flat = tensor.permute(0, 1, 3, 4, 2).reshape(-1, f)
data_smooth = F.conv1d(data_flat.unsqueeze(1), temporal_kernel.view(1, 1, -1), padding=padding).squeeze(1)
result = data_smooth.view(b, c, h, w, f).permute(0, 1, 4, 2, 3)
return result
finally:
if 'temporal_kernel' in locals() and temporal_kernel is not None:
del temporal_kernel
if 'data_flat' in locals() and data_flat is not None:
del data_flat
if 'data_smooth' in locals() and data_smooth is not None:
del data_smooth
def get_guide_epsilon_substep(x_0, x_, y0, y0_inv, s_, row, rk_type, b=None, c=None) -> Tuple[torch.Tensor, torch.Tensor]:
eps_row = None
eps_row_inv = None
s_in = None
try:
s_in = x_0.new_ones([x_0.shape[0]])
if b is not None and c is not None:
index = (b, c)
elif b is not None:
index = (b,)
else:
index = ()
if RK_Method.is_exponential(rk_type):
eps_row = y0 [index] - x_0[index]
eps_row_inv = y0_inv[index] - x_0[index]
else:
eps_row = (x_[row+1][index] - y0 [index]) / (s_[row] * s_in)
eps_row_inv = (x_[row+1][index] - y0_inv[index]) / (s_[row] * s_in)
return eps_row, eps_row_inv
finally:
if 's_in' in locals() and s_in is not None and s_in is not x_0:
del s_in
def get_guide_epsilon(x_0, x_, y0, sigma, rk_type, b=None, c=None) -> torch.Tensor:
try:
s_in = None
if not RK_Method.is_exponential(rk_type):
s_in = x_0.new_ones([x_0.shape[0]])
if b is not None and c is not None:
index = (b, c)
elif b is not None:
index = (b,)
else:
index = ()
if RK_Method.is_exponential(rk_type):
eps = y0 [index] - x_0[index]
else:
eps = (x_[index] - y0 [index]) / (sigma * s_in)
return eps
finally:
if 's_in' in locals() and s_in is not None and s_in is not x_0:
del s_in
@torch.no_grad
def noise_cossim_guide_tiled(x_list, guide, cossim_mode="forward", tile_size=2, step=0):
guide_tiled = rearrange(guide, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)
x_tiled_list = [
rearrange(x, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)
for x in x_list
]
x_tiled_stack = torch.stack([x_tiled[0] for x_tiled in x_tiled_list]) # [n_x, n_tiles, c, h, w]
guide_flat = guide_tiled[0].view(guide_tiled.shape[1], -1).unsqueeze(0) # [1, n_tiles, c*h*w]
x_flat = x_tiled_stack.view(x_tiled_stack.size(0), x_tiled_stack.size(1), -1) # [n_x, n_tiles, c*h*w]
cossim_tmp_all = F.cosine_similarity(x_flat, guide_flat, dim=-1) # [n_x, n_tiles]
if cossim_mode == "forward":
indices = cossim_tmp_all.argmax(dim=0)
elif cossim_mode == "reverse":
indices = cossim_tmp_all.argmin(dim=0)
elif cossim_mode == "orthogonal":
indices = torch.abs(cossim_tmp_all).argmin(dim=0)
elif cossim_mode == "forward_reverse":
if step % 2 == 0:
indices = cossim_tmp_all.argmax(dim=0)
else:
indices = cossim_tmp_all.argmin(dim=0)
elif cossim_mode == "reverse_forward":
if step % 2 == 1:
indices = cossim_tmp_all.argmax(dim=0)
else:
indices = cossim_tmp_all.argmin(dim=0)
elif cossim_mode == "orthogonal_reverse":
if step % 2 == 0:
indices = torch.abs(cossim_tmp_all).argmin(dim=0)
else:
indices = cossim_tmp_all.argmin(dim=0)
elif cossim_mode == "reverse_orthogonal":
if step % 2 == 1:
indices = torch.abs(cossim_tmp_all).argmin(dim=0)
else:
indices = cossim_tmp_all.argmin(dim=0)
else:
target_value = float(cossim_mode)
indices = torch.abs(cossim_tmp_all - target_value).argmin(dim=0)
x_tiled_out = x_tiled_stack[indices, torch.arange(indices.size(0))] # [n_tiles, c, h, w]
x_tiled_out = x_tiled_out.unsqueeze(0)
x_detiled = rearrange(x_tiled_out, "b (t1 t2) c h w -> b c (h t1) (w t2)", t1=tile_size, t2=tile_size)
return x_detiled
@torch.no_grad
def noise_cossim_eps_tiled(x_list, eps, noise_list, cossim_mode="forward", tile_size=2, step=0):
eps_tiled = rearrange(eps, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)
x_tiled_list = [
rearrange(x, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)
for x in x_list
]
noise_tiled_list = [
rearrange(noise, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)
for noise in noise_list
]
noise_tiled_stack = torch.stack([noise_tiled[0] for noise_tiled in noise_tiled_list]) # [n_x, n_tiles, c, h, w]
eps_expanded = eps_tiled[0].view(eps_tiled.shape[1], -1).unsqueeze(0) # [1, n_tiles, c*h*w]
noise_flat = noise_tiled_stack.view(noise_tiled_stack.size(0), noise_tiled_stack.size(1), -1) # [n_x, n_tiles, c*h*w]
cossim_tmp_all = F.cosine_similarity(noise_flat, eps_expanded, dim=-1) # [n_x, n_tiles]
if cossim_mode == "forward":
indices = cossim_tmp_all.argmax(dim=0)
elif cossim_mode == "reverse":
indices = cossim_tmp_all.argmin(dim=0)
elif cossim_mode == "orthogonal":
indices = torch.abs(cossim_tmp_all).argmin(dim=0)
elif cossim_mode == "orthogonal_pos":
positive_mask = cossim_tmp_all > 0
positive_tmp = torch.where(positive_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('inf')))
indices = positive_tmp.argmin(dim=0)
elif cossim_mode == "orthogonal_neg":
negative_mask = cossim_tmp_all < 0
negative_tmp = torch.where(negative_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('-inf')))
indices = negative_tmp.argmax(dim=0)
elif cossim_mode == "orthogonal_posneg":
if step % 2 == 0:
positive_mask = cossim_tmp_all > 0
positive_tmp = torch.where(positive_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('inf')))
indices = positive_tmp.argmin(dim=0)
else:
negative_mask = cossim_tmp_all < 0
negative_tmp = torch.where(negative_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('-inf')))
indices = negative_tmp.argmax(dim=0)
elif cossim_mode == "orthogonal_negpos":
if step % 2 == 1:
positive_mask = cossim_tmp_all > 0
positive_tmp = torch.where(positive_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('inf')))
indices = positive_tmp.argmin(dim=0)
else:
negative_mask = cossim_tmp_all < 0
negative_tmp = torch.where(negative_mask, cossim_tmp_all, torch.full_like(cossim_tmp_all, float('-inf')))
indices = negative_tmp.argmax(dim=0)
elif cossim_mode == "forward_reverse":
if step % 2 == 0:
indices = cossim_tmp_all.argmax(dim=0)
else:
indices = cossim_tmp_all.argmin(dim=0)
elif cossim_mode == "reverse_forward":
if step % 2 == 1:
indices = cossim_tmp_all.argmax(dim=0)
else:
indices = cossim_tmp_all.argmin(dim=0)
elif cossim_mode == "orthogonal_reverse":
if step % 2 == 0:
indices = torch.abs(cossim_tmp_all).argmin(dim=0)
else:
indices = cossim_tmp_all.argmin(dim=0)
elif cossim_mode == "reverse_orthogonal":
if step % 2 == 1:
indices = torch.abs(cossim_tmp_all).argmin(dim=0)
else:
indices = cossim_tmp_all.argmin(dim=0)
else:
target_value = float(cossim_mode)
indices = torch.abs(cossim_tmp_all - target_value).argmin(dim=0)
#else:
# raise ValueError(f"Unknown cossim_mode: {cossim_mode}")
x_tiled_stack = torch.stack([x_tiled[0] for x_tiled in x_tiled_list]) # [n_x, n_tiles, c, h, w]
x_tiled_out = x_tiled_stack[indices, torch.arange(indices.size(0))] # [n_tiles, c, h, w]
x_tiled_out = x_tiled_out.unsqueeze(0) # restore batch dim
x_detiled = rearrange(x_tiled_out, "b (t1 t2) c h w -> b c (h t1) (w t2)", t1=tile_size, t2=tile_size)
return x_detiled
@torch.no_grad
def noise_cossim_guide_eps_tiled(x_0, x_list, y0, noise_list, cossim_mode="forward", tile_size=2, step=0, sigma=None, rk_type=None):
x_tiled_stack = torch.stack([
rearrange(x, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)[0]
for x in x_list
]) # [n_x, n_tiles, c, h, w]
eps_guide_stack = torch.stack([
rearrange(x - y0, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)[0]
for x in x_list
]) # [n_x, n_tiles, c, h, w]
del x_list
noise_tiled_stack = torch.stack([
rearrange(noise, "b c (h t1) (w t2) -> b (t1 t2) c h w", t1=tile_size, t2=tile_size)[0]
for noise in noise_list
]) # [n_x, n_tiles, c, h, w]
del noise_list