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import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .patch_embed import PatchEmbed
from .mlp import Mlp
from .attention import Attention
from .rope import RotaryPositionEmbedding2D, PositionGetter
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
class TimestepEmbedder(nn.Module):
"""
Embeds scalar timesteps into vector representations.
"""
def __init__(self, hidden_size, frequency_embedding_size=256):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param t: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an (N, D) Tensor of positional embeddings.
"""
# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(device=t.device)
args = t[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
return embedding
def forward(self, t):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
t_emb = self.mlp(t_freq)
return t_emb
class DiTBlock(nn.Module):
"""
A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning.
"""
def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, rope=None, **block_kwargs):
super().__init__()
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=True, rope=rope, **block_kwargs)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
approx_gelu = 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)
)
def forward(self, x, c, pos=None):
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(
c
).chunk(6, dim=1)
x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa), pos=pos)
x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
return x
class FinalLayer(nn.Module):
"""
The final layer of DiT.
"""
def __init__(self, hidden_size, patch_size, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)
)
def forward(self, x, c):
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
x = modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class DiT(nn.Module):
"""
Cascade diffusion model with a transformer backbone.
"""
def __init__(
self,
in_channels=4,
out_channels=1,
hidden_size=1024,
depth=24,
num_heads=16,
mlp_ratio=4.0,
):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.num_heads = num_heads
rope_freq = 100
self.rope = RotaryPositionEmbedding2D(frequency=rope_freq) if rope_freq > 0 else None
self.position_getter = PositionGetter() if self.rope is not None else None
self.x_embedder = PatchEmbed(in_chans=in_channels, embed_dim=hidden_size)
self.t_embedder = TimestepEmbedder(hidden_size)
self.blocks = nn.ModuleList(
[DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, rope=self.rope) for _ in range(depth)]
)
self.proj_fusion = nn.Sequential(
nn.Linear(hidden_size*2, hidden_size*4),
nn.SiLU(),
nn.Linear(hidden_size*4, hidden_size*4),
nn.SiLU(),
nn.Linear(hidden_size*4, hidden_size*4),
)
self.final_layer = FinalLayer(hidden_size, 8, self.out_channels)
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)
# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
w = self.x_embedder.proj.weight.data
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
nn.init.constant_(self.x_embedder.proj.bias, 0)
# Initialize timestep embedding MLP:
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
# Zero-out adaLN modulation layers in DiT blocks:
for block in self.blocks:
nn.init.constant_(block.adaLN_modulation[-1].weight, 0)
nn.init.constant_(block.adaLN_modulation[-1].bias, 0)
# Zero-out output layers:
nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
def unpatchify(self, x, height, width):
"""
x: (N, T, patch_size**2 * C)
imgs: (N, H, W, C)
"""
c = self.out_channels
p = 8
h = height // p
w = width // p
assert h * w == x.shape[1]
x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
x = torch.einsum("nhwpqc->nchpwq", x)
imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
return imgs
def forward(self, x=None, semantics=None, timestep=None, dropout=0.1):
"""
Forward pass of SP-DiT.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
"""
N, C, H, W = x.shape
if len(timestep.shape) == 0:
timestep = timestep[None]
pos0 = None
pos1 = None
if self.rope is not None:
pos0 = self.position_getter(N, H // 16, W // 16, device=x.device)
pos1 = self.position_getter(N, H // 8, W // 8, device=x.device)
x = self.x_embedder(x)
N, T, D = x.shape
t = self.t_embedder(timestep) # (N, D)
# for block in self.blocks:
for i, block in enumerate(self.blocks):
if i < 12:
x = block(x, t, pos0) # (N, T, D)
else:
x = block(x, t, pos1) # (N, T, D)
if i == 11:
semantics = F.normalize(semantics, dim=-1)
x = self.proj_fusion(torch.cat([x, semantics], dim=-1))
p = 16
x = x.reshape(shape=(N, H//p, W//p, 2, 2, D))
x = torch.einsum("nhwpqc->nchpwq", x)
x = x.reshape(shape=(N, D, (H//p)*2, (W//p)*2))
x = x.flatten(2).transpose(1, 2)
x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels)
x = self.unpatchify(x, height=H, width=W) # (N, out_channels, H, W)
return x
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