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# For licensing see accompanying LICENSE file.
# Copyright (c) 2025 Apple Inc. Licensed under MIT License.
#
import torch
from torch import nn
from .layers import modulate, SwiGLUFeedForward
try:
from timm.models.vision_transformer import Mlp
except ModuleNotFoundError:
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.drop1 = nn.Dropout(drop)
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop2 = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class DiTBlock(nn.Module):
"""
A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
"""
def __init__(
self,
self_attention_layer,
hidden_size,
mlp_ratio=4.0,
use_swiglu=True,
):
super().__init__()
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = self_attention_layer()
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
if use_swiglu:
self.mlp = SwiGLUFeedForward(hidden_size, mlp_hidden_dim)
else:
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.mlp = Mlp(
in_features=hidden_size,
hidden_features=mlp_hidden_dim,
act_layer=approx_gelu,
drop=0,
)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True)
)
self.initialize_weights()
def initialize_weights(self):
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
# Zero-out adaLN modulation layers in DiT encoder blocks:
nn.init.constant_(self.adaLN_modulation[-1].weight, 0)
nn.init.constant_(self.adaLN_modulation[-1].bias, 0)
def forward(
self,
latents,
c,
**kwargs,
):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
self.adaLN_modulation(c).chunk(6, dim=1)
)
_latents = self.attn(
modulate(self.norm1(latents), shift_msa, scale_msa), **kwargs
)
latents = latents + gate_msa.unsqueeze(1) * _latents
latents = latents + gate_mlp.unsqueeze(1) * self.mlp(
modulate(self.norm2(latents), shift_mlp, scale_mlp)
)
return latents
class TransformerBlock(nn.Module):
"""
A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
"""
def __init__(
self,
self_attention_layer,
hidden_size,
mlp_ratio=4.0,
use_swiglu=False,
):
super().__init__()
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = self_attention_layer()
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
approx_gelu = lambda: nn.GELU(approximate="tanh")
if use_swiglu:
self.mlp = SwiGLUFeedForward(hidden_size, mlp_hidden_dim)
else:
self.mlp = Mlp(
in_features=hidden_size,
hidden_features=mlp_hidden_dim,
act_layer=approx_gelu,
drop=0,
)
def forward(
self,
latents,
**kwargs,
):
_latents = self.attn(self.norm1(latents), **kwargs)
latents = latents + _latents
latents = latents + self.mlp(self.norm2(latents))
return latents
class HomogenTrunk(nn.Module):
def __init__(self, block, depth):
super().__init__()
self.blocks = nn.ModuleList([block() for _ in range(depth)])
def forward(self, latents, c, **kwargs):
for i, block in enumerate(self.blocks):
kwargs["layer_idx"] = i
latents = block(latents=latents, c=c, **kwargs)
return latents
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