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import torch
from torch import nn, einsum
from .ldm.modules.attention import CrossAttention
from inspect import isfunction
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
# 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 * torch.nn.functional.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)
class GatedCrossAttentionDense(nn.Module):
def __init__(self, query_dim, context_dim, n_heads, d_head):
super().__init__()
self.attn = CrossAttention(
query_dim=query_dim,
context_dim=context_dim,
heads=n_heads,
dim_head=d_head)
self.ff = FeedForward(query_dim, glu=True)
self.norm1 = nn.LayerNorm(query_dim)
self.norm2 = nn.LayerNorm(query_dim)
self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))
self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))
# this can be useful: we can externally change magnitude of tanh(alpha)
# for example, when it is set to 0, then the entire model is same as
# original one
self.scale = 1
def forward(self, x, objs):
x = x + self.scale * \
torch.tanh(self.alpha_attn) * self.attn(self.norm1(x), objs, objs)
x = x + self.scale * \
torch.tanh(self.alpha_dense) * self.ff(self.norm2(x))
return x
class GatedSelfAttentionDense(nn.Module):
def __init__(self, query_dim, context_dim, n_heads, d_head):
super().__init__()
# we need a linear projection since we need cat visual feature and obj
# feature
self.linear = nn.Linear(context_dim, query_dim)
self.attn = CrossAttention(
query_dim=query_dim,
context_dim=query_dim,
heads=n_heads,
dim_head=d_head)
self.ff = FeedForward(query_dim, glu=True)
self.norm1 = nn.LayerNorm(query_dim)
self.norm2 = nn.LayerNorm(query_dim)
self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))
self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))
# this can be useful: we can externally change magnitude of tanh(alpha)
# for example, when it is set to 0, then the entire model is same as
# original one
self.scale = 1
def forward(self, x, objs):
N_visual = x.shape[1]
objs = self.linear(objs)
x = x + self.scale * torch.tanh(self.alpha_attn) * self.attn(
self.norm1(torch.cat([x, objs], dim=1)))[:, 0:N_visual, :]
x = x + self.scale * \
torch.tanh(self.alpha_dense) * self.ff(self.norm2(x))
return x
class GatedSelfAttentionDense2(nn.Module):
def __init__(self, query_dim, context_dim, n_heads, d_head):
super().__init__()
# we need a linear projection since we need cat visual feature and obj
# feature
self.linear = nn.Linear(context_dim, query_dim)
self.attn = CrossAttention(
query_dim=query_dim, context_dim=query_dim, dim_head=d_head)
self.ff = FeedForward(query_dim, glu=True)
self.norm1 = nn.LayerNorm(query_dim)
self.norm2 = nn.LayerNorm(query_dim)
self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))
self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))
# this can be useful: we can externally change magnitude of tanh(alpha)
# for example, when it is set to 0, then the entire model is same as
# original one
self.scale = 1
def forward(self, x, objs):
B, N_visual, _ = x.shape
B, N_ground, _ = objs.shape
objs = self.linear(objs)
# sanity check
size_v = math.sqrt(N_visual)
size_g = math.sqrt(N_ground)
assert int(size_v) == size_v, "Visual tokens must be square rootable"
assert int(size_g) == size_g, "Grounding tokens must be square rootable"
size_v = int(size_v)
size_g = int(size_g)
# select grounding token and resize it to visual token size as residual
out = self.attn(self.norm1(torch.cat([x, objs], dim=1)))[
:, N_visual:, :]
out = out.permute(0, 2, 1).reshape(B, -1, size_g, size_g)
out = torch.nn.functional.interpolate(
out, (size_v, size_v), mode='bicubic')
residual = out.reshape(B, -1, N_visual).permute(0, 2, 1)
# add residual to visual feature
x = x + self.scale * torch.tanh(self.alpha_attn) * residual
x = x + self.scale * \
torch.tanh(self.alpha_dense) * self.ff(self.norm2(x))
return x
class FourierEmbedder():
def __init__(self, num_freqs=64, temperature=100):
self.num_freqs = num_freqs
self.temperature = temperature
self.freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs)
@torch.no_grad()
def __call__(self, x, cat_dim=-1):
"x: arbitrary shape of tensor. dim: cat dim"
out = []
for freq in self.freq_bands:
out.append(torch.sin(freq * x))
out.append(torch.cos(freq * x))
return torch.cat(out, cat_dim)
class PositionNet(nn.Module):
def __init__(self, in_dim, out_dim, fourier_freqs=8):
super().__init__()
self.in_dim = in_dim
self.out_dim = out_dim
self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs)
self.position_dim = fourier_freqs * 2 * 4 # 2 is sin&cos, 4 is xyxy
self.linears = nn.Sequential(
nn.Linear(self.in_dim + self.position_dim, 512),
nn.SiLU(),
nn.Linear(512, 512),
nn.SiLU(),
nn.Linear(512, out_dim),
)
self.null_positive_feature = torch.nn.Parameter(
torch.zeros([self.in_dim]))
self.null_position_feature = torch.nn.Parameter(
torch.zeros([self.position_dim]))
def forward(self, boxes, masks, positive_embeddings):
B, N, _ = boxes.shape
dtype = self.linears[0].weight.dtype
masks = masks.unsqueeze(-1).to(dtype)
positive_embeddings = positive_embeddings.to(dtype)
# embedding position (it may includes padding as placeholder)
xyxy_embedding = self.fourier_embedder(boxes.to(dtype)) # B*N*4 --> B*N*C
# learnable null embedding
positive_null = self.null_positive_feature.view(1, 1, -1)
xyxy_null = self.null_position_feature.view(1, 1, -1)
# replace padding with learnable null embedding
positive_embeddings = positive_embeddings * \
masks + (1 - masks) * positive_null
xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null
objs = self.linears(
torch.cat([positive_embeddings, xyxy_embedding], dim=-1))
assert objs.shape == torch.Size([B, N, self.out_dim])
return objs
class Gligen(nn.Module):
def __init__(self, modules, position_net, key_dim):
super().__init__()
self.module_list = nn.ModuleList(modules)
self.position_net = position_net
self.key_dim = key_dim
self.max_objs = 30
self.current_device = torch.device("cpu")
def _set_position(self, boxes, masks, positive_embeddings):
objs = self.position_net(boxes, masks, positive_embeddings)
def func(x, extra_options):
key = extra_options["transformer_index"]
module = self.module_list[key]
return module(x, objs)
return func
def set_position(self, latent_image_shape, position_params, device):
batch, c, h, w = latent_image_shape
masks = torch.zeros([self.max_objs], device="cpu")
boxes = []
positive_embeddings = []
for p in position_params:
x1 = (p[4]) / w
y1 = (p[3]) / h
x2 = (p[4] + p[2]) / w
y2 = (p[3] + p[1]) / h
masks[len(boxes)] = 1.0
boxes += [torch.tensor((x1, y1, x2, y2)).unsqueeze(0)]
positive_embeddings += [p[0]]
append_boxes = []
append_conds = []
if len(boxes) < self.max_objs:
append_boxes = [torch.zeros(
[self.max_objs - len(boxes), 4], device="cpu")]
append_conds = [torch.zeros(
[self.max_objs - len(boxes), self.key_dim], device="cpu")]
box_out = torch.cat(
boxes + append_boxes).unsqueeze(0).repeat(batch, 1, 1)
masks = masks.unsqueeze(0).repeat(batch, 1)
conds = torch.cat(positive_embeddings +
append_conds).unsqueeze(0).repeat(batch, 1, 1)
return self._set_position(
box_out.to(device),
masks.to(device),
conds.to(device))
def set_empty(self, latent_image_shape, device):
batch, c, h, w = latent_image_shape
masks = torch.zeros([self.max_objs], device="cpu").repeat(batch, 1)
box_out = torch.zeros([self.max_objs, 4],
device="cpu").repeat(batch, 1, 1)
conds = torch.zeros([self.max_objs, self.key_dim],
device="cpu").repeat(batch, 1, 1)
return self._set_position(
box_out.to(device),
masks.to(device),
conds.to(device))
def load_gligen(sd):
sd_k = sd.keys()
output_list = []
key_dim = 768
for a in ["input_blocks", "middle_block", "output_blocks"]:
for b in range(20):
k_temp = filter(lambda k: "{}.{}.".format(a, b)
in k and ".fuser." in k, sd_k)
k_temp = map(lambda k: (k, k.split(".fuser.")[-1]), k_temp)
n_sd = {}
for k in k_temp:
n_sd[k[1]] = sd[k[0]]
if len(n_sd) > 0:
query_dim = n_sd["linear.weight"].shape[0]
key_dim = n_sd["linear.weight"].shape[1]
if key_dim == 768: # SD1.x
n_heads = 8
d_head = query_dim // n_heads
else:
d_head = 64
n_heads = query_dim // d_head
gated = GatedSelfAttentionDense(
query_dim, key_dim, n_heads, d_head)
gated.load_state_dict(n_sd, strict=False)
output_list.append(gated)
if "position_net.null_positive_feature" in sd_k:
in_dim = sd["position_net.null_positive_feature"].shape[0]
out_dim = sd["position_net.linears.4.weight"].shape[0]
class WeightsLoader(torch.nn.Module):
pass
w = WeightsLoader()
w.position_net = PositionNet(in_dim, out_dim)
w.load_state_dict(sd, strict=False)
gligen = Gligen(output_list, w.position_net, key_dim)
return gligen
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