### This file contains impls for MM-DiT, the core model component of SD3
import math
from typing import Dict, Optional
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange, repeat
from modules.models.sd3.other_impls import attention, Mlp
class PatchEmbed(nn.Module):
""" 2D Image to Patch Embedding"""
def __init__(
self,
img_size: Optional[int] = 224,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
flatten: bool = True,
bias: bool = True,
strict_img_size: bool = True,
dynamic_img_pad: bool = False,
dtype=None,
device=None,
):
super().__init__()
self.patch_size = (patch_size, patch_size)
if img_size is not None:
self.img_size = (img_size, img_size)
self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)])
self.num_patches = self.grid_size[0] * self.grid_size[1]
else:
self.img_size = None
self.grid_size = None
self.num_patches = None
# flatten spatial dim and transpose to channels last, kept for bwd compat
self.flatten = flatten
self.strict_img_size = strict_img_size
self.dynamic_img_pad = dynamic_img_pad
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device)
def forward(self, x):
B, C, H, W = x.shape
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # NCHW -> NLC
return x
def modulate(x, shift, scale):
if shift is None:
shift = torch.zeros_like(scale)
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
#################################################################################
# Sine/Cosine Positional Embedding Functions #
#################################################################################
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0, scaling_factor=None, offset=None):
"""
grid_size: int of the grid height and width
return:
pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
if scaling_factor is not None:
grid = grid / scaling_factor
if offset is not None:
grid = grid - offset
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token and extra_tokens > 0:
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.0
omega = 1.0 / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
return np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
#################################################################################
# Embedding Layers for Timesteps and Class Labels #
#################################################################################
class TimestepEmbedder(nn.Module):
"""Embeds scalar timesteps into vector representations."""
def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
)
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.
"""
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)
if torch.is_floating_point(t):
embedding = embedding.to(dtype=t.dtype)
return embedding
def forward(self, t, dtype, **kwargs):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
t_emb = self.mlp(t_freq)
return t_emb
class VectorEmbedder(nn.Module):
"""Embeds a flat vector of dimension input_dim"""
def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.mlp(x)
#################################################################################
# Core DiT Model #
#################################################################################
class QkvLinear(torch.nn.Linear):
pass
def split_qkv(qkv, head_dim):
qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0)
return qkv[0], qkv[1], qkv[2]
def optimized_attention(qkv, num_heads):
return attention(qkv[0], qkv[1], qkv[2], num_heads)
class SelfAttention(nn.Module):
ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
def __init__(
self,
dim: int,
num_heads: int = 8,
qkv_bias: bool = False,
qk_scale: Optional[float] = None,
attn_mode: str = "xformers",
pre_only: bool = False,
qk_norm: Optional[str] = None,
rmsnorm: bool = False,
dtype=None,
device=None,
):
super().__init__()
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.qkv = QkvLinear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
if not pre_only:
self.proj = nn.Linear(dim, dim, dtype=dtype, device=device)
assert attn_mode in self.ATTENTION_MODES
self.attn_mode = attn_mode
self.pre_only = pre_only
if qk_norm == "rms":
self.ln_q = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
self.ln_k = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
elif qk_norm == "ln":
self.ln_q = nn.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
self.ln_k = nn.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
elif qk_norm is None:
self.ln_q = nn.Identity()
self.ln_k = nn.Identity()
else:
raise ValueError(qk_norm)
def pre_attention(self, x: torch.Tensor):
B, L, C = x.shape
qkv = self.qkv(x)
q, k, v = split_qkv(qkv, self.head_dim)
q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1)
k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1)
return (q, k, v)
def post_attention(self, x: torch.Tensor) -> torch.Tensor:
assert not self.pre_only
x = self.proj(x)
return x
def forward(self, x: torch.Tensor) -> torch.Tensor:
(q, k, v) = self.pre_attention(x)
x = attention(q, k, v, self.num_heads)
x = self.post_attention(x)
return x
class RMSNorm(torch.nn.Module):
def __init__(
self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None
):
"""
Initialize the RMSNorm normalization layer.
Args:
dim (int): The dimension of the input tensor.
eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
Attributes:
eps (float): A small value added to the denominator for numerical stability.
weight (nn.Parameter): Learnable scaling parameter.
"""
super().__init__()
self.eps = eps
self.learnable_scale = elementwise_affine
if self.learnable_scale:
self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
else:
self.register_parameter("weight", None)
def _norm(self, x):
"""
Apply the RMSNorm normalization to the input tensor.
Args:
x (torch.Tensor): The input tensor.
Returns:
torch.Tensor: The normalized tensor.
"""
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
"""
Forward pass through the RMSNorm layer.
Args:
x (torch.Tensor): The input tensor.
Returns:
torch.Tensor: The output tensor after applying RMSNorm.
"""
x = self._norm(x)
if self.learnable_scale:
return x * self.weight.to(device=x.device, dtype=x.dtype)
else:
return x
class SwiGLUFeedForward(nn.Module):
def __init__(
self,
dim: int,
hidden_dim: int,
multiple_of: int,
ffn_dim_multiplier: Optional[float] = None,
):
"""
Initialize the FeedForward module.
Args:
dim (int): Input dimension.
hidden_dim (int): Hidden dimension of the feedforward layer.
multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None.
Attributes:
w1 (ColumnParallelLinear): Linear transformation for the first layer.
w2 (RowParallelLinear): Linear transformation for the second layer.
w3 (ColumnParallelLinear): Linear transformation for the third layer.
"""
super().__init__()
hidden_dim = int(2 * hidden_dim / 3)
# custom dim factor multiplier
if ffn_dim_multiplier is not None:
hidden_dim = int(ffn_dim_multiplier * hidden_dim)
hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
self.w1 = nn.Linear(dim, hidden_dim, bias=False)
self.w2 = nn.Linear(hidden_dim, dim, bias=False)
self.w3 = nn.Linear(dim, hidden_dim, bias=False)
def forward(self, x):
return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x))
class DismantledBlock(nn.Module):
"""A DiT block with gated adaptive layer norm (adaLN) conditioning."""
ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
def __init__(
self,
hidden_size: int,
num_heads: int,
mlp_ratio: float = 4.0,
attn_mode: str = "xformers",
qkv_bias: bool = False,
pre_only: bool = False,
rmsnorm: bool = False,
scale_mod_only: bool = False,
swiglu: bool = False,
qk_norm: Optional[str] = None,
dtype=None,
device=None,
**block_kwargs,
):
super().__init__()
assert attn_mode in self.ATTENTION_MODES
if not rmsnorm:
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
else:
self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, attn_mode=attn_mode, pre_only=pre_only, qk_norm=qk_norm, rmsnorm=rmsnorm, dtype=dtype, device=device)
if not pre_only:
if not rmsnorm:
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
else:
self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
mlp_hidden_dim = int(hidden_size * mlp_ratio)
if not pre_only:
if not swiglu:
self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=nn.GELU(approximate="tanh"), dtype=dtype, device=device)
else:
self.mlp = SwiGLUFeedForward(dim=hidden_size, hidden_dim=mlp_hidden_dim, multiple_of=256)
self.scale_mod_only = scale_mod_only
if not scale_mod_only:
n_mods = 6 if not pre_only else 2
else:
n_mods = 4 if not pre_only else 1
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device))
self.pre_only = pre_only
def pre_attention(self, x: torch.Tensor, c: torch.Tensor):
assert x is not None, "pre_attention called with None input"
if not self.pre_only:
if not self.scale_mod_only:
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1)
else:
shift_msa = None
shift_mlp = None
scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(4, dim=1)
qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
return qkv, (x, gate_msa, shift_mlp, scale_mlp, gate_mlp)
else:
if not self.scale_mod_only:
shift_msa, scale_msa = self.adaLN_modulation(c).chunk(2, dim=1)
else:
shift_msa = None
scale_msa = self.adaLN_modulation(c)
qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
return qkv, None
def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp):
assert not self.pre_only
x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn)
x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
return x
def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
assert not self.pre_only
(q, k, v), intermediates = self.pre_attention(x, c)
attn = attention(q, k, v, self.attn.num_heads)
return self.post_attention(attn, *intermediates)
def block_mixing(context, x, context_block, x_block, c):
assert context is not None, "block_mixing called with None context"
context_qkv, context_intermediates = context_block.pre_attention(context, c)
x_qkv, x_intermediates = x_block.pre_attention(x, c)
o = []
for t in range(3):
o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1))
q, k, v = tuple(o)
attn = attention(q, k, v, x_block.attn.num_heads)
context_attn, x_attn = (attn[:, : context_qkv[0].shape[1]], attn[:, context_qkv[0].shape[1] :])
if not context_block.pre_only:
context = context_block.post_attention(context_attn, *context_intermediates)
else:
context = None
x = x_block.post_attention(x_attn, *x_intermediates)
return context, x
class JointBlock(nn.Module):
"""just a small wrapper to serve as a fsdp unit"""
def __init__(self, *args, **kwargs):
super().__init__()
pre_only = kwargs.pop("pre_only")
qk_norm = kwargs.pop("qk_norm", None)
self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs)
self.x_block = DismantledBlock(*args, pre_only=False, qk_norm=qk_norm, **kwargs)
def forward(self, *args, **kwargs):
return block_mixing(*args, context_block=self.context_block, x_block=self.x_block, **kwargs)
class FinalLayer(nn.Module):
"""
The final layer of DiT.
"""
def __init__(self, hidden_size: int, patch_size: int, out_channels: int, total_out_channels: Optional[int] = None, dtype=None, device=None):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
self.linear = (
nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
if (total_out_channels is None)
else nn.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device)
)
self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device))
def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
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 MMDiT(nn.Module):
"""Diffusion model with a Transformer backbone."""
def __init__(
self,
input_size: int = 32,
patch_size: int = 2,
in_channels: int = 4,
depth: int = 28,
mlp_ratio: float = 4.0,
learn_sigma: bool = False,
adm_in_channels: Optional[int] = None,
context_embedder_config: Optional[Dict] = None,
register_length: int = 0,
attn_mode: str = "torch",
rmsnorm: bool = False,
scale_mod_only: bool = False,
swiglu: bool = False,
out_channels: Optional[int] = None,
pos_embed_scaling_factor: Optional[float] = None,
pos_embed_offset: Optional[float] = None,
pos_embed_max_size: Optional[int] = None,
num_patches = None,
qk_norm: Optional[str] = None,
qkv_bias: bool = True,
dtype = None,
device = None,
):
super().__init__()
self.dtype = dtype
self.learn_sigma = learn_sigma
self.in_channels = in_channels
default_out_channels = in_channels * 2 if learn_sigma else in_channels
self.out_channels = out_channels if out_channels is not None else default_out_channels
self.patch_size = patch_size
self.pos_embed_scaling_factor = pos_embed_scaling_factor
self.pos_embed_offset = pos_embed_offset
self.pos_embed_max_size = pos_embed_max_size
# apply magic --> this defines a head_size of 64
hidden_size = 64 * depth
num_heads = depth
self.num_heads = num_heads
self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True, strict_img_size=self.pos_embed_max_size is None, dtype=dtype, device=device)
self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device)
if adm_in_channels is not None:
assert isinstance(adm_in_channels, int)
self.y_embedder = VectorEmbedder(adm_in_channels, hidden_size, dtype=dtype, device=device)
self.context_embedder = nn.Identity()
if context_embedder_config is not None:
if context_embedder_config["target"] == "torch.nn.Linear":
self.context_embedder = nn.Linear(**context_embedder_config["params"], dtype=dtype, device=device)
self.register_length = register_length
if self.register_length > 0:
self.register = nn.Parameter(torch.randn(1, register_length, hidden_size, dtype=dtype, device=device))
# num_patches = self.x_embedder.num_patches
# Will use fixed sin-cos embedding:
# just use a buffer already
if num_patches is not None:
self.register_buffer(
"pos_embed",
torch.zeros(1, num_patches, hidden_size, dtype=dtype, device=device),
)
else:
self.pos_embed = None
self.joint_blocks = nn.ModuleList(
[
JointBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, attn_mode=attn_mode, pre_only=i == depth - 1, rmsnorm=rmsnorm, scale_mod_only=scale_mod_only, swiglu=swiglu, qk_norm=qk_norm, dtype=dtype, device=device)
for i in range(depth)
]
)
self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels, dtype=dtype, device=device)
def cropped_pos_embed(self, hw):
assert self.pos_embed_max_size is not None
p = self.x_embedder.patch_size[0]
h, w = hw
# patched size
h = h // p
w = w // p
assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size)
assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size)
top = (self.pos_embed_max_size - h) // 2
left = (self.pos_embed_max_size - w) // 2
spatial_pos_embed = rearrange(
self.pos_embed,
"1 (h w) c -> 1 h w c",
h=self.pos_embed_max_size,
w=self.pos_embed_max_size,
)
spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :]
spatial_pos_embed = rearrange(spatial_pos_embed, "1 h w c -> 1 (h w) c")
return spatial_pos_embed
def unpatchify(self, x, hw=None):
"""
x: (N, T, patch_size**2 * C)
imgs: (N, H, W, C)
"""
c = self.out_channels
p = self.x_embedder.patch_size[0]
if hw is None:
h = w = int(x.shape[1] ** 0.5)
else:
h, w = hw
h = h // p
w = w // 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_core_with_concat(self, x: torch.Tensor, c_mod: torch.Tensor, context: Optional[torch.Tensor] = None) -> torch.Tensor:
if self.register_length > 0:
context = torch.cat((repeat(self.register, "1 ... -> b ...", b=x.shape[0]), context if context is not None else torch.Tensor([]).type_as(x)), 1)
# context is B, L', D
# x is B, L, D
for block in self.joint_blocks:
context, x = block(context, x, c=c_mod)
x = self.final_layer(x, c_mod) # (N, T, patch_size ** 2 * out_channels)
return x
def forward(self, x: torch.Tensor, t: torch.Tensor, y: Optional[torch.Tensor] = None, context: Optional[torch.Tensor] = None) -> torch.Tensor:
"""
Forward pass of DiT.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
y: (N,) tensor of class labels
"""
hw = x.shape[-2:]
x = self.x_embedder(x) + self.cropped_pos_embed(hw)
c = self.t_embedder(t, dtype=x.dtype) # (N, D)
if y is not None:
y = self.y_embedder(y) # (N, D)
c = c + y # (N, D)
context = self.context_embedder(context)
x = self.forward_core_with_concat(x, c, context)
x = self.unpatchify(x, hw=hw) # (N, out_channels, H, W)
return x