File size: 3,919 Bytes
08f0ba3 |
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 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
# Modified from timm library:
# https://github.com/huggingface/pytorch-image-models/blob/648aaa41233ba83eb38faf5ba9d415d574823241/timm/layers/mlp.py#L13
from functools import partial
import torch
import torch.nn as nn
from .modulate_layers import modulate
from ..utils.helpers import to_2tuple
class MLP(nn.Module):
"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
def __init__(
self,
in_channels,
hidden_channels=None,
out_features=None,
act_layer=nn.GELU,
norm_layer=None,
bias=True,
drop=0.0,
use_conv=False,
device=None,
dtype=None,
):
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
out_features = out_features or in_channels
hidden_channels = hidden_channels or in_channels
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
self.fc1 = linear_layer(
in_channels, hidden_channels, bias=bias[0], **factory_kwargs
)
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.norm = (
norm_layer(hidden_channels, **factory_kwargs)
if norm_layer is not None
else nn.Identity()
)
self.fc2 = linear_layer(
hidden_channels, out_features, bias=bias[1], **factory_kwargs
)
self.drop2 = nn.Dropout(drop_probs[1])
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.norm(x)
x = self.fc2(x)
x = self.drop2(x)
return x
#
class MLPEmbedder(nn.Module):
"""copied from https://github.com/black-forest-labs/flux/blob/main/src/flux/modules/layers.py"""
def __init__(self, in_dim: int, hidden_dim: int, device=None, dtype=None):
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True, **factory_kwargs)
self.silu = nn.SiLU()
self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True, **factory_kwargs)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.out_layer(self.silu(self.in_layer(x)))
class FinalLayer(nn.Module):
"""The final layer of DiT."""
def __init__(
self, hidden_size, patch_size, out_channels, act_layer, device=None, dtype=None
):
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
# Just use LayerNorm for the final layer
self.norm_final = nn.LayerNorm(
hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs
)
if isinstance(patch_size, int):
self.linear = nn.Linear(
hidden_size,
patch_size * patch_size * out_channels,
bias=True,
**factory_kwargs
)
else:
self.linear = nn.Linear(
hidden_size,
patch_size[0] * patch_size[1] * patch_size[2] * out_channels,
bias=True,
)
nn.init.zeros_(self.linear.weight)
nn.init.zeros_(self.linear.bias)
# Here we don't distinguish between the modulate types. Just use the simple one.
self.adaLN_modulation = nn.Sequential(
act_layer(),
nn.Linear(hidden_size, 2 * hidden_size, bias=True, **factory_kwargs),
)
# Zero-initialize the modulation
nn.init.zeros_(self.adaLN_modulation[1].weight)
nn.init.zeros_(self.adaLN_modulation[1].bias)
def forward(self, x, c):
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
x = modulate(self.norm_final(x), shift=shift, scale=scale)
x = self.linear(x)
return x
|