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import math
from dataclasses import dataclass
import torch
from torch import Tensor, nn
from .math import attention, rope
import comfy.ops
import comfy.ldm.common_dit
class EmbedND(nn.Module):
def __init__(self, dim: int, theta: int, axes_dim: list):
super().__init__()
self.dim = dim
self.theta = theta
self.axes_dim = axes_dim
def forward(self, ids: Tensor) -> Tensor:
n_axes = ids.shape[-1]
emb = torch.cat(
[rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
dim=-3,
)
return emb.unsqueeze(1)
def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
"""
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.
"""
t = time_factor * t
half = dim // 2
freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half)
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(t)
return embedding
class MLPEmbedder(nn.Module):
def __init__(self, in_dim: int, hidden_dim: int, dtype=None, device=None, operations=None):
super().__init__()
self.in_layer = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device)
self.silu = nn.SiLU()
self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=True, dtype=dtype, device=device)
def forward(self, x: Tensor) -> Tensor:
return self.out_layer(self.silu(self.in_layer(x)))
class RMSNorm(torch.nn.Module):
def __init__(self, dim: int, dtype=None, device=None, operations=None):
super().__init__()
self.scale = nn.Parameter(torch.empty((dim), dtype=dtype, device=device))
def forward(self, x: Tensor):
return comfy.ldm.common_dit.rms_norm(x, self.scale, 1e-6)
class QKNorm(torch.nn.Module):
def __init__(self, dim: int, dtype=None, device=None, operations=None):
super().__init__()
self.query_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
self.key_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple:
q = self.query_norm(q)
k = self.key_norm(k)
return q.to(v), k.to(v)
class SelfAttention(nn.Module):
def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, dtype=None, device=None, operations=None):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
@dataclass
class ModulationOut:
shift: Tensor
scale: Tensor
gate: Tensor
class Modulation(nn.Module):
def __init__(self, dim: int, double: bool, dtype=None, device=None, operations=None):
super().__init__()
self.is_double = double
self.multiplier = 6 if double else 3
self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device)
def forward(self, vec: Tensor) -> tuple:
out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
return (
ModulationOut(*out[:3]),
ModulationOut(*out[3:]) if self.is_double else None,
)
class DoubleStreamBlock(nn.Module):
def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, dtype=None, device=None, operations=None):
super().__init__()
mlp_hidden_dim = int(hidden_size * mlp_ratio)
self.num_heads = num_heads
self.hidden_size = hidden_size
self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
self.img_mlp = nn.Sequential(
operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
nn.GELU(approximate="tanh"),
operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
)
self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
self.txt_mlp = nn.Sequential(
operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
nn.GELU(approximate="tanh"),
operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
)
def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor):
img_mod1, img_mod2 = self.img_mod(vec)
txt_mod1, txt_mod2 = self.txt_mod(vec)
# prepare image for attention
img_modulated = self.img_norm1(img)
img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
img_qkv = self.img_attn.qkv(img_modulated)
img_q, img_k, img_v = img_qkv.view(img_qkv.shape[0], img_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
# prepare txt for attention
txt_modulated = self.txt_norm1(txt)
txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
txt_qkv = self.txt_attn.qkv(txt_modulated)
txt_q, txt_k, txt_v = txt_qkv.view(txt_qkv.shape[0], txt_qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
# run actual attention
attn = attention(torch.cat((txt_q, img_q), dim=2),
torch.cat((txt_k, img_k), dim=2),
torch.cat((txt_v, img_v), dim=2), pe=pe)
txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
# calculate the img bloks
img = img + img_mod1.gate * self.img_attn.proj(img_attn)
img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
# calculate the txt bloks
txt += txt_mod1.gate * self.txt_attn.proj(txt_attn)
txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
if txt.dtype == torch.float16:
txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504)
return img, txt
class SingleStreamBlock(nn.Module):
"""
A DiT block with parallel linear layers as described in
https://arxiv.org/abs/2302.05442 and adapted modulation interface.
"""
def __init__(
self,
hidden_size: int,
num_heads: int,
mlp_ratio: float = 4.0,
qk_scale: float = None,
dtype=None,
device=None,
operations=None
):
super().__init__()
self.hidden_dim = hidden_size
self.num_heads = num_heads
head_dim = hidden_size // num_heads
self.scale = qk_scale or head_dim**-0.5
self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
# qkv and mlp_in
self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device)
# proj and mlp_out
self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device)
self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
self.hidden_size = hidden_size
self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
self.mlp_act = nn.GELU(approximate="tanh")
self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)
def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
mod, _ = self.modulation(vec)
x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k = self.norm(q, k, v)
# compute attention
attn = attention(q, k, v, pe=pe)
# compute activation in mlp stream, cat again and run second linear layer
output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
x += mod.gate * output
if x.dtype == torch.float16:
x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
return x
class LastLayer(nn.Module):
def __init__(self, hidden_size: int, patch_size: int, out_channels: int, 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)
self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device))
def forward(self, x: Tensor, vec: Tensor) -> Tensor:
shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
x = self.linear(x)
return x
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