global_content_adapter/model.py

"""GlobalContentAdapter - FFN-based adapter for global content conditioning."""

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange


class GEGLU(nn.Module):
    def __init__(self, dim_in, dim_out):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out * 2)

    def forward(self, x):
        x, gate = self.proj(x).chunk(2, dim=-1)
        return x * F.gelu(gate)


class FeedForward(nn.Module):
    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = dim_out if dim_out is not None else dim
        project_in = (
            nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU())
            if not glu
            else GEGLU(dim, inner_dim)
        )
        self.net = nn.Sequential(
            project_in,
            nn.Dropout(dropout),
            nn.Linear(inner_dim, dim_out),
        )

    def forward(self, x):
        return self.net(x)


class GlobalContentAdapter(nn.Module):
    def __init__(self, in_dim, channel_mult=None):
        super().__init__()
        channel_mult = channel_mult or [2, 4]
        dim_out1, mult1 = in_dim * channel_mult[0], channel_mult[0] * 2
        dim_out2, mult2 = in_dim * channel_mult[1], channel_mult[1] * 2 // channel_mult[0]
        self.in_dim = in_dim
        self.channel_mult = channel_mult
        self.ff1 = FeedForward(in_dim, dim_out=dim_out1, mult=mult1, glu=True, dropout=0.0)
        self.ff2 = FeedForward(dim_out1, dim_out=dim_out2, mult=mult2, glu=True, dropout=0.0)
        self.norm1 = nn.LayerNorm(in_dim)
        self.norm2 = nn.LayerNorm(dim_out1)

    def forward(self, x):
        x = self.ff1(self.norm1(x))
        x = self.ff2(self.norm2(x))
        x = rearrange(x, "b (n d) -> b n d", n=self.channel_mult[-1], d=self.in_dim).contiguous()
        return x