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# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
# See more details in LICENSE.
from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
from ldm.modules.diffusionmodules.util import checkpoint
def exists(val):
return val is not None
def uniq(arr):
return{el: True for el in arr}.keys()
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
def max_neg_value(t):
return -torch.finfo(t.dtype).max
def init_(tensor):
dim = tensor.shape[-1]
std = 1 / math.sqrt(dim)
tensor.uniform_(-std, std)
return tensor
# feedforward
class GEGLU(nn.Module):
def __init__(self, dim_in, dim_out):
super().__init__()
self.proj = nn.Linear(dim_in, dim_out * 2)
def forward(self, x):
x, gate = self.proj(x).chunk(2, dim=-1)
return x * F.gelu(gate)
class FeedForward(nn.Module):
def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
super().__init__()
inner_dim = int(dim * mult)
dim_out = default(dim_out, dim)
project_in = nn.Sequential(
nn.Linear(dim, inner_dim),
nn.GELU()
) if not glu else GEGLU(dim, inner_dim)
self.net = nn.Sequential(
project_in,
nn.Dropout(dropout),
nn.Linear(inner_dim, dim_out)
)
def forward(self, x):
return self.net(x)
def zero_module(module):
"""
Zero out the parameters of a module and return it.
"""
for p in module.parameters():
p.detach().zero_()
return module
def Normalize(in_channels, default_eps):
if default_eps:
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, affine=True)
else:
return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
class LinearAttention(nn.Module):
def __init__(self, dim, heads=4, dim_head=32):
super().__init__()
self.heads = heads
hidden_dim = dim_head * heads
self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
self.to_out = nn.Conv2d(hidden_dim, dim, 1)
def forward(self, x):
b, c, h, w = x.shape
qkv = self.to_qkv(x)
q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
k = torch.softmax(k.float(), dim=-1).type(k.dtype)
# k = k.softmax(dim=-1)
context = torch.einsum('bhdn,bhen->bhde', k, v)
out = torch.einsum('bhde,bhdn->bhen', context, q)
out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
return self.to_out(out)
class SpatialSelfAttention(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.k = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.v = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
self.proj_out = torch.nn.Conv2d(in_channels,
in_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b,c,h,w = q.shape
q = rearrange(q, 'b c h w -> b (h w) c')
k = rearrange(k, 'b c h w -> b c (h w)')
w_ = torch.einsum('bij,bjk->bik', q, k)
w_ = w_ * (int(c)**(-0.5))
w_ = torch.softmax(w_.float(), dim=2).type(w_.dtype)
# w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
v = rearrange(v, 'b c h w -> b c (h w)')
w_ = rearrange(w_, 'b i j -> b j i')
h_ = torch.einsum('bij,bjk->bik', v, w_)
h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
h_ = self.proj_out(h_)
return x+h_
class CrossAttention(nn.Module):
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
super().__init__()
inner_dim = dim_head * heads
context_dim = default(context_dim, query_dim)
self.scale = dim_head ** -0.5
self.heads = heads
self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, query_dim),
nn.Dropout(dropout)
)
self.prompt_to_prompt = False
def forward(self, x, context=None, mask=None):
is_self_attn = context is None
h = self.heads
q = self.to_q(x)
context = default(context, x)
k = self.to_k(context)
v = self.to_v(context)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
if self.prompt_to_prompt and is_self_attn:
# Unlike the original Prompt-to-Prompt which uses cross-attention layers, we copy attention maps for self-attention layers.
# There must be 4 elements in the batch: {conditional, unconditional} x {prompt 1, prompt 2}
assert x.size(0) == 4
sims = sim.chunk(4)
sim = torch.cat((sims[0], sims[0], sims[2], sims[2]))
if exists(mask):
mask = rearrange(mask, 'b ... -> b (...)')
max_neg_value = -torch.finfo(sim.dtype).max
mask = repeat(mask, 'b j -> (b h) () j', h=h)
sim.masked_fill_(~mask, max_neg_value)
# attention, what we cannot get enough of
# attn = sim.softmax(dim=-1)
attn = torch.softmax(sim.float(), dim=-1).type(sim.dtype)
out = einsum('b i j, b j d -> b i d', attn, v)
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
return self.to_out(out)
# class BasicTransformerBlock(nn.Module):
# def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
# super().__init__()
# self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout) # is a self-attention
# self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
# self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
# heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
# self.norm1 = nn.LayerNorm(dim)
# self.norm2 = nn.LayerNorm(dim)
# self.norm3 = nn.LayerNorm(dim)
# self.checkpoint = checkpoint
# def forward(self, x, context=None):
# # return checkpoint(self._forward, (x, context), self.checkpoint)
# return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
# def _forward(self, x, context=None):
# x = x.type(self.norm1.weight.dtype)
# if context is not None:
# context = context.type(self.norm1.weight.dtype)
# x = self.attn1(self.norm1(x)) + x
# x = self.attn2(self.norm2(x), context=context) + x
# x = self.ff(self.norm3(x)) + x
# return x
class BasicTransformerBlock(nn.Module):
ATTENTION_MODES = {
"softmax": CrossAttention, # vanilla attention
}
def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
disable_self_attn=False):
super().__init__()
attn_mode = "softmax"
assert attn_mode in self.ATTENTION_MODES
attn_cls = self.ATTENTION_MODES[attn_mode]
self.disable_self_attn = disable_self_attn
self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn
self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim,
heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.norm3 = nn.LayerNorm(dim)
self.checkpoint = checkpoint
def forward(self, x, context=None):
return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
def _forward(self, x, context=None):
x = x.type(self.norm1.weight.dtype)
if context is not None:
context = context.type(self.norm1.weight.dtype)
x = self.attn1(self.norm1(x)) + x
x = self.attn2(self.norm2(x), context=context) + x
x = self.ff(self.norm3(x)) + x
return x
# x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
# x = self.attn2(self.norm2(x), context=context) + x
# x = self.ff(self.norm3(x)) + x
# return x
# class SpatialTransformer(nn.Module):
# """
# Transformer block for image-like data.
# First, project the input (aka embedding)
# and reshape to b, t, d.
# Then apply standard transformer action.
# Finally, reshape to image
# """
# def __init__(self, in_channels, n_heads, d_head, default_eps, force_type_convert,
# depth=1, dropout=0., context_dim=None):
# super().__init__()
# self.in_channels = in_channels
# inner_dim = n_heads * d_head
# self.force_type_convert = force_type_convert
# self.norm = Normalize(in_channels, default_eps)
# self.proj_in = nn.Conv2d(in_channels,
# inner_dim,
# kernel_size=1,
# stride=1,
# padding=0)
# self.transformer_blocks = nn.ModuleList(
# [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
# for d in range(depth)]
# )
# self.proj_out = zero_module(nn.Conv2d(inner_dim,
# in_channels,
# kernel_size=1,
# stride=1,
# padding=0))
# def forward(self, x, context=None):
# # note: if no context is given, cross-attention defaults to self-attention
# b, c, h, w = x.shape
# x_in = x
# if self.force_type_convert:
# x = self.norm.float()(x.float())
# x = x.half()
# else:
# x = self.norm(x)
# x = self.proj_in(x)
# x = rearrange(x, 'b c h w -> b (h w) c')
# for block in self.transformer_blocks:
# x = block(x, context=context)
# x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
# x = self.proj_out(x)
# return x + x_in
class SpatialTransformer(nn.Module):
"""
Transformer block for image-like data.
First, project the input (aka embedding)
and reshape to b, t, d.
Then apply standard transformer action.
Finally, reshape to image
NEW: use_linear for more efficiency instead of the 1x1 convs
"""
def __init__(self, in_channels, n_heads, d_head, default_eps, force_type_convert,
depth=1, dropout=0., context_dim=None,
disable_self_attn=False, use_linear=False,
use_checkpoint=True):
super().__init__()
if exists(context_dim) and not isinstance(context_dim, list):
context_dim = [context_dim]
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.force_type_convert = force_type_convert
self.norm = Normalize(in_channels, default_eps)
if not use_linear:
self.proj_in = nn.Conv2d(in_channels,
inner_dim,
kernel_size=1,
stride=1,
padding=0)
else:
self.proj_in = nn.Linear(in_channels, inner_dim)
self.transformer_blocks = nn.ModuleList(
[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
disable_self_attn=disable_self_attn, checkpoint=use_checkpoint)
for d in range(depth)]
)
if not use_linear:
self.proj_out = zero_module(nn.Conv2d(inner_dim,
in_channels,
kernel_size=1,
stride=1,
padding=0))
else:
self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
self.use_linear = use_linear
def forward(self, x, context=None):
# note: if no context is given, cross-attention defaults to self-attention
if not isinstance(context, list):
context = [context]
b, c, h, w = x.shape
x_in = x
if self.force_type_convert:
x = self.norm.float()(x.float())
# if torch.cuda.is_available():
# x = x.half()
else:
x = self.norm(x)
if not self.use_linear:
x = self.proj_in(x)
x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
if self.use_linear:
x = self.proj_in(x)
for i, block in enumerate(self.transformer_blocks):
x = block(x, context=context[i])
if self.use_linear:
x = self.proj_out(x)
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
if not self.use_linear:
x = self.proj_out(x)
return x + x_in |