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on
Zero
Running
on
Zero
File size: 6,752 Bytes
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import torch
from torch import Tensor
import torch.nn.functional as F
from typing import Optional, Union, Tuple
from utils import normalize
from model import freq_exp, gen_nn_map
from src.ddpm_step import deterministic_ddpm_step
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
# Kernel sizes for the DIFT correction at successive time-ranges
DIFT_KERNELS: Tuple[int, int, int, int] = (12, 7, 5, 3)
def _get_kernel_for_timestep(timestep: int) -> Tuple[int, int]:
if timestep >= 799:
return DIFT_KERNELS[0], 1
if timestep >= 599:
return DIFT_KERNELS[1], 1
if timestep >= 299:
return DIFT_KERNELS[2], 1
return DIFT_KERNELS[3], 1
def step_save_latents(
self,
model_output: torch.FloatTensor,
timestep: int,
sample: torch.FloatTensor,
return_dict: bool = True,
noise_pred_uncond: Optional[torch.FloatTensor] = None,
**kwargs,
):
timestep_index = self._timesteps.index(timestep)
next_timestep_index = timestep_index + 1
u_hat_t, beta_coef = deterministic_ddpm_step(
model_output=model_output,
timestep=timestep,
sample=sample,
scheduler=self,
)
x_t_minus_1 = self.x_ts[next_timestep_index]
self.x_ts_c_predicted.append(u_hat_t)
z_t = x_t_minus_1 - u_hat_t
self.latents.append(z_t)
z_t, _ = normalize(z_t, timestep_index, self._config.max_norm_zs)
x_t_minus_1_predicted = u_hat_t + z_t
if not return_dict:
return (x_t_minus_1_predicted,)
return DDIMSchedulerOutput(prev_sample=x_t_minus_1, pred_original_sample=None)
def step_use_latents(
self,
model_output: torch.FloatTensor,
timestep: int,
sample: torch.FloatTensor,
return_dict: bool = True,
noise_pred_uncond: Optional[torch.FloatTensor] = None,
**kwargs,
):
timestep_index = self._timesteps.index(timestep)
next_timestep_index = timestep_index + 1
z_t = self.latents[next_timestep_index]
_, normalize_coefficient = normalize(
z_t,
timestep_index,
self._config.max_norm_zs,
)
x_t_hat_c_hat, beta_coef = deterministic_ddpm_step(
model_output=model_output,
timestep=timestep,
sample=sample,
scheduler=self,
)
x_t_minus_1_exact = self.x_ts[next_timestep_index]
x_t_minus_1_exact = x_t_minus_1_exact.expand_as(x_t_hat_c_hat)
x_t_c_predicted: torch.Tensor = self.x_ts_c_predicted[next_timestep_index]
x_t_c = x_t_c_predicted[0].expand_as(x_t_hat_c_hat)
mask: Optional[Tensor] = kwargs.get("mask", None)
if mask is not None and timestep > 300:
mask = mask.to(x_t_hat_c_hat.device)
movement_intensifier = kwargs.get("movement_intensifier", 0.0)
if timestep > 900 and movement_intensifier > 0.0:
latent_mask_h, *_ = freq_exp(
x_t_hat_c_hat[1:],
"auto_mask",
None,
mask.unsqueeze(0),
movement_intensifier
)
x_t_hat_c_hat[1:] = latent_mask_h
x_t_hat_c_hat[-1] = x_t_hat_c_hat[-1] * mask + (1-mask) * x_t_c[-1]
edit_prompts_num = model_output.size(0) // 2
x_t_hat_c_indices = (
0,
edit_prompts_num,
)
edit_images_indices = (
edit_prompts_num,
(model_output.size(0)),
)
x_t_hat_c = torch.zeros_like(x_t_hat_c_hat)
x_t_hat_c[edit_images_indices[0] : edit_images_indices[1]] = x_t_hat_c_hat[
x_t_hat_c_indices[0] : x_t_hat_c_indices[1]
]
w1 = kwargs.get("w1", 1.9)
cross_prompt_term = x_t_hat_c_hat - x_t_hat_c
cross_trajectory_term = x_t_hat_c - normalize_coefficient * x_t_c
x_t_minus_1_hat_ = (
normalize_coefficient * x_t_minus_1_exact
+ cross_trajectory_term
+ w1 * cross_prompt_term
)
x_t_minus_1_hat_[x_t_hat_c_indices[0] : x_t_hat_c_indices[1]] = x_t_minus_1_hat_[
edit_images_indices[0] : edit_images_indices[1]
]
dift_timestep = kwargs.get("dift_timestep", 700)
if timestep < dift_timestep and kwargs.get("apply_dift_correction", False):
z_t = torch.cat([z_t]*x_t_hat_c_hat.shape[0], dim=0)
dift_features: Optional[Tensor] = kwargs.get("dift_features", None)
dift_s, _, dift_t = dift_features.chunk(3)
resized_src_features = F.interpolate(dift_s[0].unsqueeze(0), size=z_t.shape[-1], mode='bilinear', align_corners=False).squeeze(0)
resized_tgt_features = F.interpolate(dift_t[0].unsqueeze(0), size=z_t.shape[-1], mode='bilinear', align_corners=False).squeeze(0)
kernel_size, stride = _get_kernel_for_timestep(timestep)
torch.cuda.empty_cache()
updated_z_t = gen_nn_map(z_t[1], resized_src_features, resized_tgt_features,
kernel_size=kernel_size, stride=stride,
device=z_t.device, timestep=timestep)
alpha = 1.0
z_t[1] = alpha * updated_z_t + (1 - alpha) * z_t[1]
x_t_minus_1_hat = x_t_hat_c_hat + z_t * normalize_coefficient
else:
x_t_minus_1_hat = x_t_minus_1_hat_
if not return_dict:
return (x_t_minus_1_hat,)
return DDIMSchedulerOutput(
prev_sample=x_t_minus_1_hat,
pred_original_sample=None,
)
def get_ddpm_inversion_scheduler(
scheduler,
config,
timesteps,
latents,
x_ts,
**kwargs,
):
def step(
model_output: torch.FloatTensor,
timestep: int,
sample: torch.FloatTensor,
eta: float = 0.0,
use_clipped_model_output: bool = False,
generator=None,
variance_noise: Optional[torch.FloatTensor] = None,
noise_pred_uncond: Optional[torch.FloatTensor] = None,
dift_features: Optional[torch.FloatTensor] = None,
return_dict: bool = True,
):
# predict and save x_t_c
res_inv = step_save_latents(
scheduler,
model_output[:1, :, :, :],
timestep,
sample[:1, :, :, :],
return_dict,
noise_pred_uncond[:1, :, :, :],
**kwargs,
)
res_inf = step_use_latents(
scheduler,
model_output[1:, :, :, :],
timestep,
sample[1:, :, :, :],
return_dict,
noise_pred_uncond[1:, :, :, :],
dift_features=dift_features,
**kwargs,
)
res = (torch.cat((res_inv[0], res_inf[0]), dim=0),)
return res
scheduler._timesteps = timesteps
scheduler._config = config
scheduler.latents = latents
scheduler.x_ts = x_ts
scheduler.x_ts_c_predicted = [None]
scheduler.step = step
return scheduler
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