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# Copyright (c) 2024-present, BAAI. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------
"""Diffusion Transformer."""
from functools import partial
from typing import Tuple
import torch
from torch import nn
from torch.utils.checkpoint import checkpoint as apply_ckpt
from diffnext.models.embeddings import PatchEmbed, RotaryEmbed3D
from diffnext.models.normalization import AdaLayerNormZero, AdaLayerNormSingle
from diffnext.models.diffusion_mlp import Projector, TimeCondEmbed
class TimeEmbed(TimeCondEmbed):
"""Time embedding layer."""
def __init__(self, embed_dim, freq_dim=256):
nn.Module.__init__(self)
self.timestep_proj = Projector(freq_dim, embed_dim, embed_dim)
self.freq_dim, self.time_freq = freq_dim, None
def forward(self, timestep) -> torch.Tensor:
dtype = self.timestep_proj.fc1.weight.dtype
temb = self.timestep_proj(self.get_freq_embed(timestep, dtype))
return temb.unsqueeze_(1) if temb.dim() == 2 else temb
class MLP(nn.Module):
"""Two layers MLP."""
def __init__(self, dim, mlp_ratio=4):
super(MLP, self).__init__()
self.fc1 = nn.Linear(dim, int(dim * mlp_ratio))
self.fc2 = nn.Linear(int(dim * mlp_ratio), dim)
self.activation = nn.GELU()
def forward(self, x) -> torch.Tensor:
return self.fc2(self.activation(self.fc1(x)))
class Attention(nn.Module):
"""Multihead attention."""
def __init__(self, dim, num_heads, qkv_bias=True):
super(Attention, self).__init__()
self.num_heads, self.head_dim = num_heads, dim // num_heads
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.proj, self.pe_func = nn.Linear(dim, dim), None
def forward(self, x) -> torch.Tensor:
qkv_shape = [-1, x.size(1), 3, self.num_heads, self.head_dim]
q, k, v = self.qkv(x).view(qkv_shape).permute(2, 0, 3, 1, 4).unbind(dim=0)
q, k = (self.pe_func(q), self.pe_func(k)) if self.pe_func else (q, k)
o = nn.functional.scaled_dot_product_attention(q, k, v)
return self.proj(o.transpose(1, 2).flatten(2))
class Block(nn.Module):
"""Transformer block."""
def __init__(self, dim, num_heads, mlp_ratio=4, qkv_bias=True, modulation_type=None):
super(Block, self).__init__()
self.modulation = (modulation_type or AdaLayerNormZero)(dim, num_stats=6, eps=1e-6)
self.norm1, self.norm2 = nn.LayerNorm(dim), nn.LayerNorm(dim)
self.attn = Attention(dim, num_heads, qkv_bias=qkv_bias)
self.mlp = MLP(dim, mlp_ratio=mlp_ratio)
self.attn_checkpointing = self.mlp_checkpointing = self.stg_skip = False
def forward_modulation(self, x, z) -> Tuple[torch.Tensor, Tuple[torch.Tensor]]:
return self.modulation(x, z)
def forward_attn(self, x) -> torch.Tensor:
return self.norm1(self.attn(x))
def forward_mlp(self, x) -> torch.Tensor:
return self.norm2(self.mlp(x))
def forward_ckpt(self, x, name) -> torch.Tensor:
if getattr(self, f"{name}_checkpointing", False) and x.requires_grad:
return apply_ckpt(getattr(self, f"forward_{name}"), x, use_reentrant=False)
return getattr(self, f"forward_{name}")(x)
def forward(self, x, z, pe_func: callable = None) -> torch.Tensor:
self.attn.pe_func = pe_func
stg_x = x.chunk(3)[-1] if self.stg_skip else None
if self.mlp_checkpointing and x.requires_grad:
x, stats = apply_ckpt(self.forward_modulation, x, z, use_reentrant=False)
else:
x, stats = self.forward_modulation(x, z)
gate_msa, scale_mlp, shift_mlp, gate_mlp = stats
x = self.forward_ckpt(x, "attn").mul(gate_msa).add_(x)
x = self.modulation.norm(x).mul(1 + scale_mlp).add_(shift_mlp)
x = self.forward_ckpt(x, "mlp").mul(gate_mlp).add_(x)
return torch.cat(x.chunk(3)[:2] + (stg_x,)) if self.stg_skip else x
class DiffusionTransformer(nn.Module):
"""Diffusion transformer."""
def __init__(
self,
depth,
embed_dim,
num_heads,
mlp_ratio=4,
patch_size=2,
image_size=32,
image_dim=None,
modulation=True,
):
super(DiffusionTransformer, self).__init__()
final_norm = AdaLayerNormSingle if modulation else AdaLayerNormZero
block = partial(Block, modulation_type=AdaLayerNormSingle) if modulation else Block
self.embed_dim, self.image_size, self.image_dim = embed_dim, image_size, image_dim
self.patch_embed = PatchEmbed(image_dim, embed_dim, patch_size)
self.time_embed = TimeEmbed(embed_dim)
self.modulation = AdaLayerNormZero(embed_dim, num_stats=6, eps=1e-6) if modulation else None
self.rope = RotaryEmbed3D(embed_dim // num_heads)
self.blocks = nn.ModuleList(block(embed_dim, num_heads, mlp_ratio) for _ in range(depth))
self.norm = final_norm(embed_dim, num_stats=2, eps=1e-6)
self.head = nn.Linear(embed_dim, patch_size**2 * image_dim)
def prepare_pe(self, c=None, pos=None) -> Tuple[callable, callable]:
return self.rope.get_func(pos, pad=0 if c is None else c.size(1))
def forward(self, x, timestep, c=None, pos=None) -> torch.Tensor:
x = self.patch_embed(x)
t = self.time_embed(timestep)
z = self.modulation.proj(self.modulation.activation(t)) if self.modulation else t
pe = self.prepare_pe(c, pos) if pos is not None else None
x = x if c is None else torch.cat([c, x], dim=1)
for blk in self.blocks:
x = blk(x, z, pe)
x = self.norm(x if c is None else x[:, c.size(1) :], t)[0]
return self.head(x)
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