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import logging
import math
from typing import Dict, Optional
import numpy as np
import torch
import torch.nn as nn
from .. import attention
from einops import rearrange, repeat
from .util import timestep_embedding
import comfy.ops
def default(x, y):
if x is not None:
return x
return y
class Mlp(nn.Module):
""" MLP as used in Vision Transformer, MLP-Mixer and related networks
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
norm_layer=None,
bias=True,
drop=0.,
use_conv=False,
dtype=None,
device=None,
operations=None,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
drop_probs = drop
linear_layer = partial(operations.Conv2d, kernel_size=1) if use_conv else operations.Linear
self.fc1 = linear_layer(in_features, hidden_features, bias=bias, dtype=dtype, device=device)
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs)
self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
self.fc2 = linear_layer(hidden_features, out_features, bias=bias, dtype=dtype, device=device)
self.drop2 = nn.Dropout(drop_probs)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.norm(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class PatchEmbed(nn.Module):
""" 2D Image to Patch Embedding
"""
dynamic_img_pad: torch.jit.Final[bool]
def __init__(
self,
img_size: Optional[int] = 224,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
norm_layer = None,
flatten: bool = True,
bias: bool = True,
strict_img_size: bool = True,
dynamic_img_pad: bool = True,
padding_mode='circular',
dtype=None,
device=None,
operations=None,
):
super().__init__()
self.patch_size = (patch_size, patch_size)
self.padding_mode = padding_mode
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 = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
B, C, H, W = x.shape
# if self.img_size is not None:
# if self.strict_img_size:
# _assert(H == self.img_size[0], f"Input height ({H}) doesn't match model ({self.img_size[0]}).")
# _assert(W == self.img_size[1], f"Input width ({W}) doesn't match model ({self.img_size[1]}).")
# elif not self.dynamic_img_pad:
# _assert(
# H % self.patch_size[0] == 0,
# f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})."
# )
# _assert(
# W % self.patch_size[1] == 0,
# f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
# )
if self.dynamic_img_pad:
pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode=self.padding_mode)
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # NCHW -> NLC
x = self.norm(x)
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)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos, device=None, dtype=torch.float32):
omega = torch.arange(embed_dim // 2, device=device, dtype=dtype)
omega /= embed_dim / 2.0
omega = 1.0 / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = torch.einsum("m,d->md", pos, omega) # (M, D/2), outer product
emb_sin = torch.sin(out) # (M, D/2)
emb_cos = torch.cos(out) # (M, D/2)
emb = torch.cat([emb_sin, emb_cos], dim=1) # (M, D)
return emb
def get_2d_sincos_pos_embed_torch(embed_dim, w, h, val_center=7.5, val_magnitude=7.5, device=None, dtype=torch.float32):
small = min(h, w)
val_h = (h / small) * val_magnitude
val_w = (w / small) * val_magnitude
grid_h, grid_w = torch.meshgrid(torch.linspace(-val_h + val_center, val_h + val_center, h, device=device, dtype=dtype), torch.linspace(-val_w + val_center, val_w + val_center, w, device=device, dtype=dtype), indexing='ij')
emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype)
emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype)
emb = torch.cat([emb_w, emb_h], dim=1) # (H*W, D)
return emb
#################################################################################
# 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, operations=None):
super().__init__()
self.mlp = nn.Sequential(
operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
nn.SiLU(),
operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
)
self.frequency_embedding_size = frequency_embedding_size
def forward(self, t, dtype, **kwargs):
t_freq = 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, operations=None):
super().__init__()
self.mlp = nn.Sequential(
operations.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device),
nn.SiLU(),
operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
emb = self.mlp(x)
return emb
#################################################################################
# Core DiT Model #
#################################################################################
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.optimized_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,
proj_drop: float = 0.0,
attn_mode: str = "xformers",
pre_only: bool = False,
qk_norm: Optional[str] = None,
rmsnorm: bool = False,
dtype=None,
device=None,
operations=None,
):
super().__init__()
self.num_heads = num_heads
self.head_dim = dim // num_heads
self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
if not pre_only:
self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
self.proj_drop = nn.Dropout(proj_drop)
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 = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
self.ln_k = operations.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) -> 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)
x = self.proj_drop(x)
return x
def forward(self, x: torch.Tensor) -> torch.Tensor:
qkv = self.pre_attention(x)
x = optimized_attention(
qkv, num_heads=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,
operations=None,
**block_kwargs,
):
super().__init__()
assert attn_mode in self.ATTENTION_MODES
if not rmsnorm:
self.norm1 = operations.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,
operations=operations
)
if not pre_only:
if not rmsnorm:
self.norm2 = operations.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=lambda: nn.GELU(approximate="tanh"),
drop=0,
dtype=dtype,
device=device,
operations=operations
)
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(), operations.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) -> torch.Tensor:
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
qkv, intermediates = self.pre_attention(x, c)
attn = optimized_attention(
qkv,
num_heads=self.attn.num_heads,
)
return self.post_attention(attn, *intermediates)
def block_mixing(*args, use_checkpoint=True, **kwargs):
if use_checkpoint:
return torch.utils.checkpoint.checkpoint(
_block_mixing, *args, use_reentrant=False, **kwargs
)
else:
return _block_mixing(*args, **kwargs)
def _block_mixing(context, x, context_block, x_block, c):
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))
qkv = tuple(o)
attn = optimized_attention(
qkv,
num_heads=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,
operations=None,
):
super().__init__()
self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
self.linear = (
operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
if (total_out_channels is None)
else operations.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device)
)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(), operations.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 SelfAttentionContext(nn.Module):
def __init__(self, dim, heads=8, dim_head=64, dtype=None, device=None, operations=None):
super().__init__()
dim_head = dim // heads
inner_dim = dim
self.heads = heads
self.dim_head = dim_head
self.qkv = operations.Linear(dim, dim * 3, bias=True, dtype=dtype, device=device)
self.proj = operations.Linear(inner_dim, dim, dtype=dtype, device=device)
def forward(self, x):
qkv = self.qkv(x)
q, k, v = split_qkv(qkv, self.dim_head)
x = optimized_attention((q.reshape(q.shape[0], q.shape[1], -1), k, v), self.heads)
return self.proj(x)
class ContextProcessorBlock(nn.Module):
def __init__(self, context_size, dtype=None, device=None, operations=None):
super().__init__()
self.norm1 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
self.attn = SelfAttentionContext(context_size, dtype=dtype, device=device, operations=operations)
self.norm2 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
self.mlp = Mlp(in_features=context_size, hidden_features=(context_size * 4), act_layer=lambda: nn.GELU(approximate="tanh"), drop=0, dtype=dtype, device=device, operations=operations)
def forward(self, x):
x += self.attn(self.norm1(x))
x += self.mlp(self.norm2(x))
return x
class ContextProcessor(nn.Module):
def __init__(self, context_size, num_layers, dtype=None, device=None, operations=None):
super().__init__()
self.layers = torch.nn.ModuleList([ContextProcessorBlock(context_size, dtype=dtype, device=device, operations=operations) for i in range(num_layers)])
self.norm = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
def forward(self, x):
for i, l in enumerate(self.layers):
x = l(x)
return self.norm(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,
# hidden_size: Optional[int] = None,
# num_heads: Optional[int] = None,
mlp_ratio: float = 4.0,
learn_sigma: bool = False,
adm_in_channels: Optional[int] = None,
context_embedder_config: Optional[Dict] = None,
compile_core: bool = False,
use_checkpoint: bool = False,
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,
context_processor_layers = None,
context_size = 4096,
num_blocks = None,
final_layer = True,
dtype = None, #TODO
device = None,
operations = 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 = default(out_channels, 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
# hidden_size = default(hidden_size, 64 * depth)
# num_heads = default(num_heads, hidden_size // 64)
# apply magic --> this defines a head_size of 64
self.hidden_size = 64 * depth
num_heads = depth
if num_blocks is None:
num_blocks = depth
self.depth = depth
self.num_heads = num_heads
self.x_embedder = PatchEmbed(
input_size,
patch_size,
in_channels,
self.hidden_size,
bias=True,
strict_img_size=self.pos_embed_max_size is None,
dtype=dtype,
device=device,
operations=operations
)
self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations)
self.y_embedder = None
if adm_in_channels is not None:
assert isinstance(adm_in_channels, int)
self.y_embedder = VectorEmbedder(adm_in_channels, self.hidden_size, dtype=dtype, device=device, operations=operations)
if context_processor_layers is not None:
self.context_processor = ContextProcessor(context_size, context_processor_layers, dtype=dtype, device=device, operations=operations)
else:
self.context_processor = None
self.context_embedder = nn.Identity()
if context_embedder_config is not None:
if context_embedder_config["target"] == "torch.nn.Linear":
self.context_embedder = operations.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, self.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.empty(1, num_patches, self.hidden_size, dtype=dtype, device=device),
)
else:
self.pos_embed = None
self.use_checkpoint = use_checkpoint
self.joint_blocks = nn.ModuleList(
[
JointBlock(
self.hidden_size,
num_heads,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
attn_mode=attn_mode,
pre_only=(i == num_blocks - 1) and final_layer,
rmsnorm=rmsnorm,
scale_mod_only=scale_mod_only,
swiglu=swiglu,
qk_norm=qk_norm,
dtype=dtype,
device=device,
operations=operations
)
for i in range(num_blocks)
]
)
if final_layer:
self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
if compile_core:
assert False
self.forward_core_with_concat = torch.compile(self.forward_core_with_concat)
def cropped_pos_embed(self, hw, device=None):
p = self.x_embedder.patch_size[0]
h, w = hw
# patched size
h = (h + 1) // p
w = (w + 1) // p
if self.pos_embed is None:
return get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=device)
assert self.pos_embed_max_size is not None
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")
# print(spatial_pos_embed, top, left, h, w)
# # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.875, 7.875, device=device) #matches exactly for 1024 res
# t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.5, 7.5, device=device) #scales better
# # print(t)
# return t
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 + 1) // p
w = (w + 1) // 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,
control = None,
) -> torch.Tensor:
if self.register_length > 0:
context = torch.cat(
(
repeat(self.register, "1 ... -> b ...", b=x.shape[0]),
default(context, torch.Tensor([]).type_as(x)),
),
1,
)
# context is B, L', D
# x is B, L, D
blocks = len(self.joint_blocks)
for i in range(blocks):
context, x = self.joint_blocks[i](
context,
x,
c=c_mod,
use_checkpoint=self.use_checkpoint,
)
if control is not None:
control_o = control.get("output")
if i < len(control_o):
add = control_o[i]
if add is not None:
x += add
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,
control = 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
"""
if self.context_processor is not None:
context = self.context_processor(context)
hw = x.shape[-2:]
x = self.x_embedder(x) + comfy.ops.cast_to_input(self.cropped_pos_embed(hw, device=x.device), x)
c = self.t_embedder(t, dtype=x.dtype) # (N, D)
if y is not None and self.y_embedder is not None:
y = self.y_embedder(y) # (N, D)
c = c + y # (N, D)
if context is not None:
context = self.context_embedder(context)
x = self.forward_core_with_concat(x, c, context, control)
x = self.unpatchify(x, hw=hw) # (N, out_channels, H, W)
return x[:,:,:hw[-2],:hw[-1]]
class OpenAISignatureMMDITWrapper(MMDiT):
def forward(
self,
x: torch.Tensor,
timesteps: torch.Tensor,
context: Optional[torch.Tensor] = None,
y: Optional[torch.Tensor] = None,
control = None,
**kwargs,
) -> torch.Tensor:
return super().forward(x, timesteps, context=context, y=y, control=control)
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