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import torch.nn as nn
from ldm.modules.encoders.modules import OpenCLIPImageEmbedder, FrozenOpenCLIPEmbedder
from ldm.util import instantiate_from_config
import torch
from taming.models.vqgan import VQModelInterfaceEncoder, VQModel
from ldm.modules.attention import SpatialTransformer
from ldm.modules.attention import Normalize, BasicTransformerBlock#, exists
from ldm.modules.diffusionmodules.util import zero_module, identity_init_fc, conv_nd
from einops import rearrange
# from ldm.modules.diffusionmodules.openaimodel import TimestepEmbedSequential
def disabled_train(self, mode=True):
"""Overwrite model.train with this function to make sure train/eval mode
does not change anymore."""
return self
def make_zero_conv(in_channels, out_channels, kernel_size, stride=1, padding=0):
return zero_module(conv_nd(2, in_channels, out_channels, kernel_size, stride=stride, padding=padding))
class SpatialTransformer_v2(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,
depth=1, dropout=0., context_dim=None,
disable_self_attn=False, use_linear=False,
use_checkpoint=True):
super().__init__()
# change:
# if exists(context_dim) and not isinstance(context_dim, list):
if not isinstance(context_dim, list):
context_dim = [context_dim]
self.in_channels = in_channels
inner_dim = n_heads * d_head
self.norm = Normalize(in_channels)
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(inner_dim, in_channels)) # change: switch
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
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
class trans_glyph_emb(nn.Module):
def __init__(self,
type = "fc", # "conv", "attn"
input_dim = 256,
out_dim = 1024,
# fc
fc_init = "zero",
# conv/attn
conv_ks = 3,
conv_pad = 1,
conv_stride = 1,
# attn
ch = 512, # 1024
num_heads = 8, # 16
dim_head = 64,
use_linear_in_transformer = True,
use_checkpoint = False, #True,
):
super().__init__()
if type == "fc":
self.model = torch.nn.Linear(input_dim, out_dim)
if fc_init == "zero":
self.model = zero_module(self.model)
elif fc_init == "identity":
self.model = identity_init_fc(self.model)
elif type == "conv":
self.model = make_zero_conv(input_dim, out_dim, conv_ks, stride = conv_stride, padding = conv_pad)
elif type == "attn":
model = [
# nn.Conv2d(input_dim, ch, 3, stride = 1, padding = 1),
nn.Conv2d(input_dim, ch, conv_ks, stride = conv_stride, padding = conv_pad),
SpatialTransformer_v2( #SpatialTransformer(
ch, num_heads, dim_head, depth=1, context_dim=None, #ch,
disable_self_attn=False, use_linear=use_linear_in_transformer,
use_checkpoint=use_checkpoint, # False if the context is None
),
make_zero_conv(ch, out_dim, 1, stride = 1, padding = 0)
# make_zero_conv(ch, out_dim, conv_ks, stride = conv_stride, padding = conv_pad)
]
self.model = nn.Sequential(*model)
self.model_type = type
def forward(self, x):
if self.model_type == "fc":
# b, c, h, w = x.shape
x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
x = self.model(x)
# x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
# return x
else:
x = self.model(x)
x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
return x
class glyph_control(nn.Module):
def __init__(self,
image_encoder = "CLIP", # "VQGAN"
image_encoder_config = None,
fuse_way = "concat",
load_text_encoder = False,
text_encoder_config = None,
freeze_image_encoder = True,
trans_emb = False,
trans_emb_config = None,
# use_fp16 = False,
):
super().__init__()
if image_encoder_config is not None:
image_encoder_config.params.freeze = freeze_image_encoder
self.image_encoder = instantiate_from_config(image_encoder_config)
else:
if image_encoder == "CLIP":
self.image_encoder = OpenCLIPImageEmbedder(freeze=freeze_image_encoder)
elif image_encoder == "VQGAN":
print("VQGAN glyph image encoder is missing config")
raise ValueError
else:
print("Other types of glyph image encoder are not supported")
raise ValueError
if freeze_image_encoder:
self.freeze_imenc()
self.freeze_image_encoder = freeze_image_encoder
self.image_encoder_type = image_encoder
if load_text_encoder:
if text_encoder_config is None:
self.text_encoder = FrozenOpenCLIPEmbedder()
else:
self.text_encoder = instantiate_from_config(text_encoder_config)
self.fuse_way = fuse_way
# self.dtype = torch.float16 if use_fp16 else torch.float32
if trans_emb:
if trans_emb_config is not None:
self.trans_glyph_emb_model = instantiate_from_config(trans_emb_config)
else:
self.trans_glyph_emb_model = trans_glyph_emb()
else:
self.trans_glyph_emb_model = None
def freeze_imenc(self):
self.image_encoder = self.image_encoder.eval()
self.image_encoder.train = disabled_train
for param in self.image_encoder.parameters():
param.requires_grad = False
def forward(self, glyph_image, text = None, text_embed = None):
clgim_num_list = [img.shape[0] for img in glyph_image]
# image_embeds = self.image_encoder(torch.concat(glyph_image, dim=0))
gim_concat = torch.concat(glyph_image, dim=0)
image_embeds = self.image_encoder(gim_concat)
if self.trans_glyph_emb_model is not None:
image_embeds = self.trans_glyph_emb_model(image_embeds)
image_embeds = torch.split(image_embeds, clgim_num_list)
max_image_tokens = max(clgim_num_list)
pad_image_embeds = []
for image_embed in image_embeds:
if image_embed.shape[0] < max_image_tokens:
image_embed = torch.concat([
image_embed,
torch.zeros(
(max_image_tokens - image_embed.shape[0], *image_embed.shape[1:]), device=image_embed.device, dtype=image_embed.dtype, # add dtype
)], dim=0
)
pad_image_embeds.append(image_embed)
pad_image_embeds = torch.stack(pad_image_embeds, dim = 0)
if text_embed is None:
assert self.text_encoder, text is not None
text_embed = self.text_encoder(text)
if self.fuse_way == "concat":
assert pad_image_embeds.shape[-1] == text_embed.shape[-1]
if len(pad_image_embeds.shape) == 4:
b, _, _ , embdim = pad_image_embeds.shape
pad_image_embeds = pad_image_embeds.view(b, -1, embdim)
out_embed = torch.concat([text_embed, pad_image_embeds], dim= 1)
print("concat glyph_embed with text_embed:", out_embed.shape)
return out_embed
else:
raise ValueError("Not support other fuse ways for now!")
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