|
from typing import Callable
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
|
|
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) -> torch.Tensor:
|
|
return self.linear(self.act(x))
|
|
|
|
|
|
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 apply_gate(x, gate=None, tanh=False):
|
|
"""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 gate is None:
|
|
return x
|
|
if tanh:
|
|
return x * gate.unsqueeze(1).tanh()
|
|
else:
|
|
return x * gate.unsqueeze(1)
|
|
|
|
|
|
def ckpt_wrapper(module):
|
|
def ckpt_forward(*inputs):
|
|
outputs = module(*inputs)
|
|
return outputs
|
|
|
|
return ckpt_forward
|
|
|
