mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-04-13 15:03:33 +00:00
188 lines
7.8 KiB
Python
188 lines
7.8 KiB
Python
import math
|
|
from dataclasses import dataclass
|
|
|
|
import torch
|
|
from torch import Tensor, nn
|
|
|
|
from .math import attention, rope
|
|
from comfy.ldm.flux.layers import (
|
|
MLPEmbedder,
|
|
RMSNorm,
|
|
QKNorm,
|
|
SelfAttention,
|
|
ModulationOut,
|
|
)
|
|
import comfy.ldm.common_dit
|
|
|
|
|
|
|
|
class ChromaModulationOut(ModulationOut):
|
|
@classmethod
|
|
def from_offset(cls, tensor: torch.Tensor, offset: int = 0) -> ModulationOut:
|
|
return cls(
|
|
shift=tensor[:, offset : offset + 1, :],
|
|
scale=tensor[:, offset + 1 : offset + 2, :],
|
|
gate=tensor[:, offset + 2 : offset + 3, :],
|
|
)
|
|
|
|
|
|
|
|
|
|
class Approximator(nn.Module):
|
|
def __init__(self, in_dim: int, out_dim: int, hidden_dim: int, n_layers = 5, dtype=None, device=None, operations=None):
|
|
super().__init__()
|
|
self.in_proj = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device)
|
|
self.layers = nn.ModuleList([MLPEmbedder(hidden_dim, hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)])
|
|
self.norms = nn.ModuleList([RMSNorm(hidden_dim, dtype=dtype, device=device, operations=operations) for x in range( n_layers)])
|
|
self.out_proj = operations.Linear(hidden_dim, out_dim, dtype=dtype, device=device)
|
|
|
|
@property
|
|
def device(self):
|
|
# Get the device of the module (assumes all parameters are on the same device)
|
|
return next(self.parameters()).device
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
x = self.in_proj(x)
|
|
|
|
for layer, norms in zip(self.layers, self.norms):
|
|
x = x + layer(norms(x))
|
|
|
|
x = self.out_proj(x)
|
|
|
|
return x
|
|
|
|
|
|
class DoubleStreamBlock(nn.Module):
|
|
def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, flipped_img_txt=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_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_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),
|
|
)
|
|
self.flipped_img_txt = flipped_img_txt
|
|
|
|
def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None):
|
|
(img_mod1, img_mod2), (txt_mod1, txt_mod2) = 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, mask=attn_mask)
|
|
|
|
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")
|
|
|
|
def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor:
|
|
mod = 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, mask=attn_mask)
|
|
# 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, out_channels, bias=True, dtype=dtype, device=device)
|
|
|
|
def forward(self, x: Tensor, vec: Tensor) -> Tensor:
|
|
shift, scale = vec
|
|
shift = shift.squeeze(1)
|
|
scale = scale.squeeze(1)
|
|
x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
|
|
x = self.linear(x)
|
|
return x
|