| from typing import Callable
|
|
|
| import torch
|
| import torch.nn as nn
|
| import math
|
|
|
| class ModulateDiT(nn.Module):
|
| """Modulation layer for DiT."""
|
| def __init__(
|
| self,
|
| hidden_size: int,
|
| factor: int,
|
| act_layer: Callable,
|
| dtype=None,
|
| device=None,
|
| ):
|
| factory_kwargs = {"dtype": dtype, "device": device}
|
| super().__init__()
|
| self.act = act_layer()
|
| self.linear = nn.Linear(
|
| hidden_size, factor * hidden_size, bias=True, **factory_kwargs
|
| )
|
|
|
| nn.init.zeros_(self.linear.weight)
|
| nn.init.zeros_(self.linear.bias)
|
|
|
| def forward(self, x: torch.Tensor, condition_type=None, token_replace_vec=None) -> torch.Tensor:
|
| x_out = self.linear(self.act(x))
|
|
|
| if condition_type == "token_replace":
|
| x_token_replace_out = self.linear(self.act(token_replace_vec))
|
| return x_out, x_token_replace_out
|
| else:
|
| return x_out
|
|
|
| def modulate(x, shift=None, scale=None):
|
| """modulate by shift and scale
|
|
|
| Args:
|
| x (torch.Tensor): input tensor.
|
| shift (torch.Tensor, optional): shift tensor. Defaults to None.
|
| scale (torch.Tensor, optional): scale tensor. Defaults to None.
|
|
|
| Returns:
|
| torch.Tensor: the output tensor after modulate.
|
| """
|
| if scale is None and shift is None:
|
| return x
|
| elif shift is None:
|
| return x * (1 + scale.unsqueeze(1))
|
| elif scale is None:
|
| return x + shift.unsqueeze(1)
|
| else:
|
| return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
|
|
|
| def modulate_(x, shift=None, scale=None):
|
|
|
| if scale is None and shift is None:
|
| return x
|
| elif shift is None:
|
| scale = scale + 1
|
| scale = scale.unsqueeze(1)
|
| return x.mul_(scale)
|
| elif scale is None:
|
| return x + shift.unsqueeze(1)
|
| else:
|
| scale = scale + 1
|
| scale = scale.unsqueeze(1)
|
|
|
| torch.addcmul(shift.unsqueeze(1), x, scale, out =x )
|
| return x
|
|
|
| def modulate(x, shift=None, scale=None, condition_type=None,
|
| tr_shift=None, tr_scale=None,
|
| frist_frame_token_num=None):
|
| if condition_type == "token_replace":
|
| x_zero = x[:, :frist_frame_token_num] * (1 + tr_scale.unsqueeze(1)) + tr_shift.unsqueeze(1)
|
| x_orig = x[:, frist_frame_token_num:] * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
|
| x = torch.concat((x_zero, x_orig), dim=1)
|
| return x
|
| else:
|
| if scale is None and shift is None:
|
| return x
|
| elif shift is None:
|
| return x * (1 + scale.unsqueeze(1))
|
| elif scale is None:
|
| return x + shift.unsqueeze(1)
|
| else:
|
| return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
|
|
|
| def apply_gate(x, gate=None, tanh=False, condition_type=None, tr_gate=None, frist_frame_token_num=None):
|
| """AI is creating summary for apply_gate
|
|
|
| Args:
|
| x (torch.Tensor): input tensor.
|
| gate (torch.Tensor, optional): gate tensor. Defaults to None.
|
| tanh (bool, optional): whether to use tanh function. Defaults to False.
|
|
|
| Returns:
|
| torch.Tensor: the output tensor after apply gate.
|
| """
|
| if condition_type == "token_replace":
|
| if gate is None:
|
| return x
|
| if tanh:
|
| x_zero = x[:, :frist_frame_token_num] * tr_gate.unsqueeze(1).tanh()
|
| x_orig = x[:, frist_frame_token_num:] * gate.unsqueeze(1).tanh()
|
| x = torch.concat((x_zero, x_orig), dim=1)
|
| return x
|
| else:
|
| x_zero = x[:, :frist_frame_token_num] * tr_gate.unsqueeze(1)
|
| x_orig = x[:, frist_frame_token_num:] * gate.unsqueeze(1)
|
| x = torch.concat((x_zero, x_orig), dim=1)
|
| return x
|
| else:
|
| if gate is None:
|
| return x
|
| if tanh:
|
| return x * gate.unsqueeze(1).tanh()
|
| else:
|
| return x * gate.unsqueeze(1)
|
|
|
| def apply_gate_and_accumulate_(accumulator, x, gate=None, tanh=False):
|
| if gate is None:
|
| return accumulator
|
| if tanh:
|
| return accumulator.addcmul_(x, gate.unsqueeze(1).tanh())
|
| else:
|
| return accumulator.addcmul_(x, gate.unsqueeze(1))
|
|
|
| def ckpt_wrapper(module):
|
| def ckpt_forward(*inputs):
|
| outputs = module(*inputs)
|
| return outputs
|
|
|
| return ckpt_forward
|
|
|
