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Zero
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import torch
import torch.nn as nn
from torch.utils.checkpoint import checkpoint
from .mask_dit import DiTBlock, FinalBlock, UDiT
from .modules import (
film_modulate,
PatchEmbed,
PE_wrapper,
TimestepEmbedder,
RMSNorm,
)
class AudioDiTBlock(DiTBlock):
"""
A modified DiT block with time aligned context add to latent.
"""
def __init__(
self,
dim,
ta_context_dim,
ta_context_norm=False,
context_dim=None,
num_heads=8,
mlp_ratio=4.,
qkv_bias=False,
qk_scale=None,
qk_norm=None,
act_layer='gelu',
norm_layer=nn.LayerNorm,
ta_context_fusion='add',
time_fusion='none',
ada_sola_rank=None,
ada_sola_alpha=None,
skip=False,
skip_norm=False,
rope_mode='none',
context_norm=False,
use_checkpoint=False
):
super().__init__(
dim=dim,
context_dim=context_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
qk_norm=qk_norm,
act_layer=act_layer,
norm_layer=norm_layer,
time_fusion=time_fusion,
ada_sola_rank=ada_sola_rank,
ada_sola_alpha=ada_sola_alpha,
skip=skip,
skip_norm=skip_norm,
rope_mode=rope_mode,
context_norm=context_norm,
use_checkpoint=use_checkpoint
)
self.ta_context_fusion = ta_context_fusion
self.ta_context_norm = ta_context_norm
if self.ta_context_fusion == "add":
self.ta_context_projection = nn.Linear(ta_context_dim, dim)
self.ta_context_norm = norm_layer(
ta_context_dim
) if self.ta_context_norm else nn.Identity()
elif self.ta_context_fusion == "concat":
self.ta_context_projection = nn.Linear(ta_context_dim + dim, dim)
self.ta_context_norm = norm_layer(
ta_context_dim + dim
) if self.ta_context_norm else nn.Identity()
def forward(
self,
x,
time_aligned_context,
time_token=None,
time_ada=None,
skip=None,
context=None,
x_mask=None,
context_mask=None,
extras=None
):
if self.use_checkpoint:
return checkpoint(
self._forward,
x,
time_aligned_context,
time_token,
time_ada,
skip,
context,
x_mask,
context_mask,
extras,
use_reentrant=False
)
else:
return self._forward(
x,
time_aligned_context,
time_token,
time_ada,
skip,
context,
x_mask,
context_mask,
extras,
)
def _forward(
self,
x,
time_aligned_context,
time_token=None,
time_ada=None,
skip=None,
context=None,
x_mask=None,
context_mask=None,
extras=None
):
B, T, C = x.shape
# # time aligned context
# if self.ta_context_fusion == "add":
# time_aligned_context = self.ta_context_projection(
# self.ta_context_norm(time_aligned_context)
# )
# x = x + time_aligned_context
# elif self.ta_context_fusion == "concat":
# cat = torch.cat([x, time_aligned_context], dim=-1)
# cat = self.ta_context_norm(cat)
# x = self.ta_context_projection(cat)
# skip connection
if self.skip_linear is not None:
assert skip is not None
cat = torch.cat([x, skip], dim=-1)
cat = self.skip_norm(cat)
x = self.skip_linear(cat)
#print('skip')
#print(x)
if self.use_adanorm:
time_ada = self.adaln(time_token, time_ada)
(shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp,
gate_mlp) = time_ada.chunk(6, dim=1)
# self attention
if self.use_adanorm:
x_norm = film_modulate(
self.norm1(x), shift=shift_msa, scale=scale_msa
)
x = x + (1-gate_msa) * self.attn(
x_norm, context=None, context_mask=x_mask, extras=extras
)
else:
# TODO diffusion timestep input is not fused here
x = x + self.attn(
self.norm1(x),
context=None,
context_mask=x_mask,
extras=extras
)
# time aligned context fusion
if self.ta_context_fusion == "add":
time_aligned_context = self.ta_context_projection(
self.ta_context_norm(time_aligned_context)
)
x = x + time_aligned_context
elif self.ta_context_fusion == "concat":
cat = torch.cat([x, time_aligned_context], dim=-1)
cat = self.ta_context_norm(cat)
x = self.ta_context_projection(cat)
# cross attention
if self.use_context:
assert context is not None
x = x + self.cross_attn(
x=self.norm2(x),
context=self.norm_context(context),
context_mask=context_mask,
extras=extras
)
# mlp
if self.use_adanorm:
x_norm = film_modulate(
self.norm3(x), shift=shift_mlp, scale=scale_mlp
)
x = x + (1-gate_mlp) * self.mlp(x_norm)
else:
x = x + self.mlp(self.norm3(x))
return x
class AudioUDiT(UDiT):
def __init__(
self,
img_size=224,
patch_size=16,
in_chans=3,
input_type='2d',
out_chans=None,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4,
qkv_bias=False,
qk_scale=None,
qk_norm=None,
act_layer='gelu',
norm_layer='layernorm',
context_norm=False,
use_checkpoint=False,
time_fusion='token',
ada_sola_rank=None,
ada_sola_alpha=None,
cls_dim=None,
ta_context_dim=768,
ta_context_fusion='concat',
ta_context_norm=True,
context_dim=768,
context_fusion='concat',
context_max_length=128,
context_pe_method='sinu',
pe_method='abs',
rope_mode='none',
use_conv=True,
skip=True,
skip_norm=True
):
nn.Module.__init__(self)
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
# input
self.in_chans = in_chans
self.input_type = input_type
if self.input_type == '2d':
num_patches = (img_size[0] //
patch_size) * (img_size[1] // patch_size)
elif self.input_type == '1d':
num_patches = img_size // patch_size
self.patch_embed = PatchEmbed(
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
input_type=input_type
)
out_chans = in_chans if out_chans is None else out_chans
self.out_chans = out_chans
# position embedding
self.rope = rope_mode
self.x_pe = PE_wrapper(
dim=embed_dim, method=pe_method, length=num_patches
)
# time embed
self.time_embed = TimestepEmbedder(embed_dim)
self.time_fusion = time_fusion
self.use_adanorm = False
# cls embed
if cls_dim is not None:
self.cls_embed = nn.Sequential(
nn.Linear(cls_dim, embed_dim, bias=True),
nn.SiLU(),
nn.Linear(embed_dim, embed_dim, bias=True),
)
else:
self.cls_embed = None
# time fusion
if time_fusion == 'token':
# put token at the beginning of sequence
self.extras = 2 if self.cls_embed else 1
self.time_pe = PE_wrapper(
dim=embed_dim, method='abs', length=self.extras
)
elif time_fusion in ['ada', 'ada_single', 'ada_sola', 'ada_sola_bias']:
self.use_adanorm = True
# aviod repetitive silu for each adaln block
self.time_act = nn.SiLU()
self.extras = 0
self.time_ada_final = nn.Linear(
embed_dim, 2 * embed_dim, bias=True
)
if time_fusion in ['ada_single', 'ada_sola', 'ada_sola_bias']:
# shared adaln
self.time_ada = nn.Linear(embed_dim, 6 * embed_dim, bias=True)
else:
self.time_ada = None
else:
raise NotImplementedError
# context
# use a simple projection
self.use_context = False
self.context_cross = False
self.context_max_length = context_max_length
self.context_fusion = 'none'
if context_dim is not None:
self.use_context = True
self.context_embed = nn.Sequential(
nn.Linear(context_dim, embed_dim, bias=True),
nn.SiLU(),
nn.Linear(embed_dim, embed_dim, bias=True),
)
self.context_fusion = context_fusion
if context_fusion == 'concat' or context_fusion == 'joint':
self.extras += context_max_length
self.context_pe = PE_wrapper(
dim=embed_dim,
method=context_pe_method,
length=context_max_length
)
# no cross attention layers
context_dim = None
elif context_fusion == 'cross':
self.context_pe = PE_wrapper(
dim=embed_dim,
method=context_pe_method,
length=context_max_length
)
self.context_cross = True
context_dim = embed_dim
else:
raise NotImplementedError
self.use_skip = skip
# norm layers
if norm_layer == 'layernorm':
norm_layer = nn.LayerNorm
elif norm_layer == 'rmsnorm':
norm_layer = RMSNorm
else:
raise NotImplementedError
self.in_blocks = nn.ModuleList([
AudioDiTBlock(
dim=embed_dim,
ta_context_dim=ta_context_dim,
ta_context_fusion=ta_context_fusion,
ta_context_norm=ta_context_norm,
context_dim=context_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
qk_norm=qk_norm,
act_layer=act_layer,
norm_layer=norm_layer,
time_fusion=time_fusion,
ada_sola_rank=ada_sola_rank,
ada_sola_alpha=ada_sola_alpha,
skip=False,
skip_norm=False,
rope_mode=self.rope,
context_norm=context_norm,
use_checkpoint=use_checkpoint
) for i in range(depth // 2)
])
self.mid_block = AudioDiTBlock(
dim=embed_dim,
ta_context_dim=ta_context_dim,
context_dim=context_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
qk_norm=qk_norm,
act_layer=act_layer,
norm_layer=norm_layer,
time_fusion=time_fusion,
ada_sola_rank=ada_sola_rank,
ada_sola_alpha=ada_sola_alpha,
ta_context_fusion=ta_context_fusion,
ta_context_norm=ta_context_norm,
skip=False,
skip_norm=False,
rope_mode=self.rope,
context_norm=context_norm,
use_checkpoint=use_checkpoint
)
self.out_blocks = nn.ModuleList([
AudioDiTBlock(
dim=embed_dim,
ta_context_dim=ta_context_dim,
context_dim=context_dim,
num_heads=num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
qk_norm=qk_norm,
act_layer=act_layer,
norm_layer=norm_layer,
time_fusion=time_fusion,
ada_sola_rank=ada_sola_rank,
ada_sola_alpha=ada_sola_alpha,
ta_context_fusion=ta_context_fusion,
ta_context_norm=ta_context_norm,
skip=skip,
skip_norm=skip_norm,
rope_mode=self.rope,
context_norm=context_norm,
use_checkpoint=use_checkpoint
) for i in range(depth // 2)
])
# FinalLayer block
self.use_conv = use_conv
self.final_block = FinalBlock(
embed_dim=embed_dim,
patch_size=patch_size,
img_size=img_size,
in_chans=out_chans,
input_type=input_type,
norm_layer=norm_layer,
use_conv=use_conv,
use_adanorm=self.use_adanorm
)
self.initialize_weights()
def forward(
self,
x,
timesteps,
time_aligned_context,
context,
x_mask=None,
context_mask=None,
cls_token=None,
controlnet_skips=None,
):
# make it compatible with int time step during inference
if timesteps.dim() == 0:
timesteps = timesteps.expand(x.shape[0]
).to(x.device, dtype=torch.long)
x = self.patch_embed(x)
x = self.x_pe(x)
B, L, D = x.shape
if self.use_context:
context_token = self.context_embed(context)
context_token = self.context_pe(context_token)
if self.context_fusion == 'concat' or self.context_fusion == 'joint':
x, x_mask = self._concat_x_context(
x=x,
context=context_token,
x_mask=x_mask,
context_mask=context_mask
)
context_token, context_mask = None, None
else:
context_token, context_mask = None, None
time_token = self.time_embed(timesteps)
if self.cls_embed:
cls_token = self.cls_embed(cls_token)
time_ada = None
time_ada_final = None
if self.use_adanorm:
if self.cls_embed:
time_token = time_token + cls_token
time_token = self.time_act(time_token)
time_ada_final = self.time_ada_final(time_token)
if self.time_ada is not None:
time_ada = self.time_ada(time_token)
else:
time_token = time_token.unsqueeze(dim=1)
if self.cls_embed:
cls_token = cls_token.unsqueeze(dim=1)
time_token = torch.cat([time_token, cls_token], dim=1)
time_token = self.time_pe(time_token)
x = torch.cat((time_token, x), dim=1)
if x_mask is not None:
x_mask = torch.cat([
torch.ones(B, time_token.shape[1],
device=x_mask.device).bool(), x_mask
],
dim=1)
time_token = None
skips = []
for blk in self.in_blocks:
x = blk(
x=x,
time_aligned_context=time_aligned_context,
time_token=time_token,
time_ada=time_ada,
skip=None,
context=context_token,
x_mask=x_mask,
context_mask=context_mask,
extras=self.extras
)
if self.use_skip:
skips.append(x)
x = self.mid_block(
x=x,
time_aligned_context=time_aligned_context,
time_token=time_token,
time_ada=time_ada,
skip=None,
context=context_token,
x_mask=x_mask,
context_mask=context_mask,
extras=self.extras
)
for blk in self.out_blocks:
if self.use_skip:
skip = skips.pop()
if controlnet_skips:
# add to skip like u-net controlnet
skip = skip + controlnet_skips.pop()
else:
skip = None
if controlnet_skips:
# directly add to x
x = x + controlnet_skips.pop()
x = blk(
x=x,
time_aligned_context=time_aligned_context,
time_token=time_token,
time_ada=time_ada,
skip=skip,
context=context_token,
x_mask=x_mask,
context_mask=context_mask,
extras=self.extras
)
x = self.final_block(x, time_ada=time_ada_final, extras=self.extras)
return x
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