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# 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 hymm_sp.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.,
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
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