Spaces:
Running
Running
File size: 2,088 Bytes
797fd08 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
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
)
# Zero-initialize the modulation
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
|