#AuraFlow MMDiT
#Originally written by the AuraFlow Authors
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from comfy.ldm.modules.attention import optimized_attention
import comfy.ops
import comfy.ldm.common_dit
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
def find_multiple(n: int, k: int) -> int:
if n % k == 0:
return n
return n + k - (n % k)
class MLP(nn.Module):
def __init__(self, dim, hidden_dim=None, dtype=None, device=None, operations=None) -> None:
super().__init__()
if hidden_dim is None:
hidden_dim = 4 * dim
n_hidden = int(2 * hidden_dim / 3)
n_hidden = find_multiple(n_hidden, 256)
self.c_fc1 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)
self.c_fc2 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)
self.c_proj = operations.Linear(n_hidden, dim, bias=False, dtype=dtype, device=device)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
x = self.c_proj(x)
return x
class MultiHeadLayerNorm(nn.Module):
def __init__(self, hidden_size=None, eps=1e-5, dtype=None, device=None):
# Copy pasta from https://github.com/huggingface/transformers/blob/e5f71ecaae50ea476d1e12351003790273c4b2ed/src/transformers/models/cohere/modeling_cohere.py#L78
super().__init__()
self.weight = nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
mean = hidden_states.mean(-1, keepdim=True)
variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
hidden_states = (hidden_states - mean) * torch.rsqrt(
variance + self.variance_epsilon
)
hidden_states = self.weight.to(torch.float32) * hidden_states
return hidden_states.to(input_dtype)
class SingleAttention(nn.Module):
def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):
super().__init__()
self.n_heads = n_heads
self.head_dim = dim // n_heads
# this is for cond
self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
self.q_norm1 = (
MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
if mh_qknorm
else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
)
self.k_norm1 = (
MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
if mh_qknorm
else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
)
#@torch.compile()
def forward(self, c):
bsz, seqlen1, _ = c.shape
q, k, v = self.w1q(c), self.w1k(c), self.w1v(c)
q = q.view(bsz, seqlen1, self.n_heads, self.head_dim)
k = k.view(bsz, seqlen1, self.n_heads, self.head_dim)
v = v.view(bsz, seqlen1, self.n_heads, self.head_dim)
q, k = self.q_norm1(q), self.k_norm1(k)
output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
c = self.w1o(output)
return c
class DoubleAttention(nn.Module):
def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):
super().__init__()
self.n_heads = n_heads
self.head_dim = dim // n_heads
# this is for cond
self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
# this is for x
self.w2q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
self.w2k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
self.w2v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
self.w2o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
self.q_norm1 = (
MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
if mh_qknorm
else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
)
self.k_norm1 = (
MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
if mh_qknorm
else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
)
self.q_norm2 = (
MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
if mh_qknorm
else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
)
self.k_norm2 = (
MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
if mh_qknorm
else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
)
#@torch.compile()
def forward(self, c, x):
bsz, seqlen1, _ = c.shape
bsz, seqlen2, _ = x.shape
cq, ck, cv = self.w1q(c), self.w1k(c), self.w1v(c)
cq = cq.view(bsz, seqlen1, self.n_heads, self.head_dim)
ck = ck.view(bsz, seqlen1, self.n_heads, self.head_dim)
cv = cv.view(bsz, seqlen1, self.n_heads, self.head_dim)
cq, ck = self.q_norm1(cq), self.k_norm1(ck)
xq, xk, xv = self.w2q(x), self.w2k(x), self.w2v(x)
xq = xq.view(bsz, seqlen2, self.n_heads, self.head_dim)
xk = xk.view(bsz, seqlen2, self.n_heads, self.head_dim)
xv = xv.view(bsz, seqlen2, self.n_heads, self.head_dim)
xq, xk = self.q_norm2(xq), self.k_norm2(xk)
# concat all
q, k, v = (
torch.cat([cq, xq], dim=1),
torch.cat([ck, xk], dim=1),
torch.cat([cv, xv], dim=1),
)
output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
c, x = output.split([seqlen1, seqlen2], dim=1)
c = self.w1o(c)
x = self.w2o(x)
return c, x
class MMDiTBlock(nn.Module):
def __init__(self, dim, heads=8, global_conddim=1024, is_last=False, dtype=None, device=None, operations=None):
super().__init__()
self.normC1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
self.normC2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
if not is_last:
self.mlpC = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
self.modC = nn.Sequential(
nn.SiLU(),
operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
)
else:
self.modC = nn.Sequential(
nn.SiLU(),
operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),
)
self.normX1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
self.normX2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
self.mlpX = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
self.modX = nn.Sequential(
nn.SiLU(),
operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
)
self.attn = DoubleAttention(dim, heads, dtype=dtype, device=device, operations=operations)
self.is_last = is_last
#@torch.compile()
def forward(self, c, x, global_cond, **kwargs):
cres, xres = c, x
cshift_msa, cscale_msa, cgate_msa, cshift_mlp, cscale_mlp, cgate_mlp = (
self.modC(global_cond).chunk(6, dim=1)
)
c = modulate(self.normC1(c), cshift_msa, cscale_msa)
# xpath
xshift_msa, xscale_msa, xgate_msa, xshift_mlp, xscale_mlp, xgate_mlp = (
self.modX(global_cond).chunk(6, dim=1)
)
x = modulate(self.normX1(x), xshift_msa, xscale_msa)
# attention
c, x = self.attn(c, x)
c = self.normC2(cres + cgate_msa.unsqueeze(1) * c)
c = cgate_mlp.unsqueeze(1) * self.mlpC(modulate(c, cshift_mlp, cscale_mlp))
c = cres + c
x = self.normX2(xres + xgate_msa.unsqueeze(1) * x)
x = xgate_mlp.unsqueeze(1) * self.mlpX(modulate(x, xshift_mlp, xscale_mlp))
x = xres + x
return c, x
class DiTBlock(nn.Module):
# like MMDiTBlock, but it only has X
def __init__(self, dim, heads=8, global_conddim=1024, dtype=None, device=None, operations=None):
super().__init__()
self.norm1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
self.norm2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
self.modCX = nn.Sequential(
nn.SiLU(),
operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
)
self.attn = SingleAttention(dim, heads, dtype=dtype, device=device, operations=operations)
self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
#@torch.compile()
def forward(self, cx, global_cond, **kwargs):
cxres = cx
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX(
global_cond
).chunk(6, dim=1)
cx = modulate(self.norm1(cx), shift_msa, scale_msa)
cx = self.attn(cx)
cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx)
mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp))
cx = gate_mlp.unsqueeze(1) * mlpout
cx = cxres + cx
return cx
class TimestepEmbedder(nn.Module):
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, dtype=dtype, device=device),
nn.SiLU(),
operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
half = dim // 2
freqs = 1000 * torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half) / half
).to(t.device)
args = t[:, None] * 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
#@torch.compile()
def forward(self, t, dtype):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
t_emb = self.mlp(t_freq)
return t_emb
class MMDiT(nn.Module):
def __init__(
self,
in_channels=4,
out_channels=4,
patch_size=2,
dim=3072,
n_layers=36,
n_double_layers=4,
n_heads=12,
global_conddim=3072,
cond_seq_dim=2048,
max_seq=32 * 32,
device=None,
dtype=None,
operations=None,
):
super().__init__()
self.dtype = dtype
self.t_embedder = TimestepEmbedder(global_conddim, dtype=dtype, device=device, operations=operations)
self.cond_seq_linear = operations.Linear(
cond_seq_dim, dim, bias=False, dtype=dtype, device=device
) # linear for something like text sequence.
self.init_x_linear = operations.Linear(
patch_size * patch_size * in_channels, dim, dtype=dtype, device=device
) # init linear for patchified image.
self.positional_encoding = nn.Parameter(torch.empty(1, max_seq, dim, dtype=dtype, device=device))
self.register_tokens = nn.Parameter(torch.empty(1, 8, dim, dtype=dtype, device=device))
self.double_layers = nn.ModuleList([])
self.single_layers = nn.ModuleList([])
for idx in range(n_double_layers):
self.double_layers.append(
MMDiTBlock(dim, n_heads, global_conddim, is_last=(idx == n_layers - 1), dtype=dtype, device=device, operations=operations)
)
for idx in range(n_double_layers, n_layers):
self.single_layers.append(
DiTBlock(dim, n_heads, global_conddim, dtype=dtype, device=device, operations=operations)
)
self.final_linear = operations.Linear(
dim, patch_size * patch_size * out_channels, bias=False, dtype=dtype, device=device
)
self.modF = nn.Sequential(
nn.SiLU(),
operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),
)
self.out_channels = out_channels
self.patch_size = patch_size
self.n_double_layers = n_double_layers
self.n_layers = n_layers
self.h_max = round(max_seq**0.5)
self.w_max = round(max_seq**0.5)
@torch.no_grad()
def extend_pe(self, init_dim=(16, 16), target_dim=(64, 64)):
# extend pe
pe_data = self.positional_encoding.data.squeeze(0)[: init_dim[0] * init_dim[1]]
pe_as_2d = pe_data.view(init_dim[0], init_dim[1], -1).permute(2, 0, 1)
# now we need to extend this to target_dim. for this we will use interpolation.
# we will use torch.nn.functional.interpolate
pe_as_2d = F.interpolate(
pe_as_2d.unsqueeze(0), size=target_dim, mode="bilinear"
)
pe_new = pe_as_2d.squeeze(0).permute(1, 2, 0).flatten(0, 1)
self.positional_encoding.data = pe_new.unsqueeze(0).contiguous()
self.h_max, self.w_max = target_dim
def pe_selection_index_based_on_dim(self, h, w):
h_p, w_p = h // self.patch_size, w // self.patch_size
original_pe_indexes = torch.arange(self.positional_encoding.shape[1])
original_pe_indexes = original_pe_indexes.view(self.h_max, self.w_max)
starth = self.h_max // 2 - h_p // 2
endh =starth + h_p
startw = self.w_max // 2 - w_p // 2
endw = startw + w_p
original_pe_indexes = original_pe_indexes[
starth:endh, startw:endw
]
return original_pe_indexes.flatten()
def unpatchify(self, x, h, w):
c = self.out_channels
p = self.patch_size
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 patchify(self, x):
B, C, H, W = x.size()
x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
x = x.view(
B,
C,
(H + 1) // self.patch_size,
self.patch_size,
(W + 1) // self.patch_size,
self.patch_size,
)
x = x.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2)
return x
def apply_pos_embeds(self, x, h, w):
h = (h + 1) // self.patch_size
w = (w + 1) // self.patch_size
max_dim = max(h, w)
cur_dim = self.h_max
pos_encoding = comfy.ops.cast_to_input(self.positional_encoding.reshape(1, cur_dim, cur_dim, -1), x)
if max_dim > cur_dim:
pos_encoding = F.interpolate(pos_encoding.movedim(-1, 1), (max_dim, max_dim), mode="bilinear").movedim(1, -1)
cur_dim = max_dim
from_h = (cur_dim - h) // 2
from_w = (cur_dim - w) // 2
pos_encoding = pos_encoding[:,from_h:from_h+h,from_w:from_w+w]
return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1])
def forward(self, x, timestep, context, transformer_options={}, **kwargs):
patches_replace = transformer_options.get("patches_replace", {})
# patchify x, add PE
b, c, h, w = x.shape
# pe_indexes = self.pe_selection_index_based_on_dim(h, w)
# print(pe_indexes, pe_indexes.shape)
x = self.init_x_linear(self.patchify(x)) # B, T_x, D
x = self.apply_pos_embeds(x, h, w)
# x = x + self.positional_encoding[:, : x.size(1)].to(device=x.device, dtype=x.dtype)
# x = x + self.positional_encoding[:, pe_indexes].to(device=x.device, dtype=x.dtype)
# process conditions for MMDiT Blocks
c_seq = context # B, T_c, D_c
t = timestep
c = self.cond_seq_linear(c_seq) # B, T_c, D
c = torch.cat([comfy.ops.cast_to_input(self.register_tokens, c).repeat(c.size(0), 1, 1), c], dim=1)
global_cond = self.t_embedder(t, x.dtype) # B, D
blocks_replace = patches_replace.get("dit", {})
if len(self.double_layers) > 0:
for i, layer in enumerate(self.double_layers):
if ("double_block", i) in blocks_replace:
def block_wrap(args):
out = {}
out["txt"], out["img"] = layer(args["txt"],
args["img"],
args["vec"])
return out
out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond}, {"original_block": block_wrap})
c = out["txt"]
x = out["img"]
else:
c, x = layer(c, x, global_cond, **kwargs)
if len(self.single_layers) > 0:
c_len = c.size(1)
cx = torch.cat([c, x], dim=1)
for i, layer in enumerate(self.single_layers):
if ("single_block", i) in blocks_replace:
def block_wrap(args):
out = {}
out["img"] = layer(args["img"], args["vec"])
return out
out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond}, {"original_block": block_wrap})
cx = out["img"]
else:
cx = layer(cx, global_cond, **kwargs)
x = cx[:, c_len:]
fshift, fscale = self.modF(global_cond).chunk(2, dim=1)
x = modulate(x, fshift, fscale)
x = self.final_linear(x)
x = self.unpatchify(x, (h + 1) // self.patch_size, (w + 1) // self.patch_size)[:,:,:h,:w]
return x