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# This code is built from the Stable Diffusion repository: https://github.com/CompVis/stable-diffusion, and
# Paint-by-Example repo https://github.com/Fantasy-Studio/Paint-by-Example
# Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors.
# CreativeML Open RAIL-M
#
# ==========================================================================================
#
# Adobe’s modifications are Copyright 2024 Adobe Research. All rights reserved.
# Adobe’s modifications are licensed under the Adobe Research License. To view a copy of the license, visit
# LICENSE.md.
#
# ==========================================================================================
from inspect import isfunction
import math
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat
import glob
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):
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 = 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.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., only_crossref=False):
super().__init__()
inner_dim = dim_head * heads
# forcing attention to only attend on vectors of same size
# breaking the image2text attention
context_dim = default(context_dim, query_dim)
# print('creating cross attention. Query dim', query_dim, ' context dim', context_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.only_crossref = only_crossref
if only_crossref:
self.merge_attentions = zero_module(nn.Conv2d(self.heads * 2,
self.heads,
kernel_size=1,
stride=1,
padding=0))
else:
self.merge_attentions = zero_module(nn.Conv2d(self.heads * 3,
self.heads,
kernel_size=1,
stride=1,
padding=0))
self.merge_attentions_missing = zero_module(nn.Conv2d(self.heads * 2,
self.heads,
kernel_size=1,
stride=1,
padding=0))
def forward(self, x, context=None, mask=None, passed_qkv=None, masks=None, corresp=None, missing_region=None):
is_self_attention = context is None
# if masks is not None:
# print(is_self_attention, masks.keys())
h = self.heads
# if passed_qkv is not None:
# assert context is None
# _,_,_,_, x_features = passed_qkv
# assert x_features is not None
# # print('x shape', x.shape, 'x features', x_features.shape)
# # breakpoint()
# x = torch.concat([x, x_features], dim=1)
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 exists(mask):
assert False
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)
out = einsum('b i j, b j d -> b i d', attn, v)
inter_out = rearrange(out, '(b h) n d -> b h n d', h=h)
combined_attention = inter_out
out = rearrange(combined_attention, 'b h n d -> b n (h d)', h=h)
final_out = self.to_out(out)
if is_self_attention:
return final_out, q, k, v, inter_out #TODO add attn out
else:
return final_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.attn3 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout)
self.norm1 = nn.LayerNorm(dim)
self.norm2 = nn.LayerNorm(dim)
self.norm3 = nn.LayerNorm(dim)
self.checkpoint = checkpoint
# TODO add attn in
def forward(self, x, context=None, passed_qkv=None, masks=None, corresp=None):
if passed_qkv is None:
return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
else:
q, k, v, attn, x_features = passed_qkv
d = int(np.sqrt(q.shape[1]))
current_mask = masks[d]
if corresp:
current_corresp, missing_region = corresp[d]
current_corresp = current_corresp.float()
missing_region = missing_region.float()
else:
raise ValueError('cannot have empty corresp')
current_corresp = None
missing_region = current_mask.float()
# breakpoint()
stuff = [q, k, v, attn, x_features, current_mask, current_corresp, missing_region]
for element in stuff:
assert element is not None
return checkpoint(self._forward, (x, context, q, k, v, attn, x_features, current_mask, current_corresp, missing_region), self.parameters(), self.checkpoint)
# TODO add attn in
def _forward(self, x, context=None, q=None, k=None, v=None, attn=None, passed_x=None, masks=None, corresp=None, missing_region=None):
if q is not None:
passed_qkv = (q, k, v, attn, passed_x)
else:
passed_qkv = None
x_features = self.norm1(x)
attended_x, q, k, v, attn = self.attn1(x_features, passed_qkv=passed_qkv, masks=masks, corresp=corresp, missing_region=missing_region)
x = attended_x + x
# killing CLIP features
if passed_x is not None:
normed_x = self.norm2(x)
attn_out = self.attn3(normed_x, context=passed_x)
x = attn_out + x
# then use y + x
# print('y shape', y.shape, ' x shape', x.shape)
x = self.ff(self.norm3(x)) + x
return x, q, k, v, attn, x_features
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,
depth=1, dropout=0., context_dim=None):
super().__init__()
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = Normalize(in_channels)
# print('creating spatial transformer')
# print('in channels', in_channels, 'inner dim', inner_dim)
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))
# TODO add attn in and corresp
def forward(self, x, context=None, passed_qkv=None, masks=None, corresp=None):
# note: if no context is given, cross-attention defaults to self-attention
b, c, h, w = x.shape
# print('spatial transformer x shape given', x.shape)
# if context is not None:
# print('also context was provided with shape ', context.shape)
x_in = x
x = self.norm(x)
x = self.proj_in(x)
x = rearrange(x, 'b c h w -> b (h w) c')
qkvs = []
for block in self.transformer_blocks:
x, q, k, v, attn, x_features = block(x, context=context, passed_qkv=passed_qkv, masks=masks, corresp=corresp)
qkv = (q,k,v,attn, x_features)
qkvs.append(qkv)
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
x = self.proj_out(x)
return x + x_in, qkvs |