models/local_adapter.py

import torch
import torch as th
import torch.nn as nn
import torch.nn.functional as F

from ldm.modules.diffusionmodules.util import (
 checkpoint,
 conv_nd,
 linear,
 zero_module,
 timestep_embedding,
)
from ldm.modules.diffusionmodules.openaimodel import (
 UNetModel, 
 TimestepBlock, 
 TimestepEmbedSequential, 
 ResBlock, 
 Downsample, 
 AttentionBlock
)
from ldm.modules.attention import SpatialTransformer
from ldm.util import exists


def layer_norm(tensor, drop=0.5, eps=1e-6):
 mean = tensor.mean(dim=(1,2)).squeeze()
 std = tensor.std(dim=(1,2)).squeeze()
 var = tensor.var(dim=(1,2))
 tensor = (tensor-mean) / (var+eps) ** 0.5
 neg = (tensor * (tensor < 0).float()).abs().sum() / (tensor<0).float().sum()
 pos = (tensor * (tensor > 0).float()).abs().sum() / (tensor>0).float().sum()


class LocalTimestepEmbedSequential(nn.Sequential, TimestepBlock):
 def forward(self, x, emb, context=None, local_control=None, content_control=None, color_control=None, content_w=1.0, color_w=1.0):
 for layer in self:
 if isinstance(layer, TimestepBlock):
 x = layer(x, emb)
 elif isinstance(layer, SpatialTransformer):
 x = layer(x, context, content_control, color_control, content_w, color_w)
 elif isinstance(layer, LocalResBlock):
 x = layer(x, emb, local_control)
 else:
 x = layer(x)
 return x


class FDN(nn.Module):
 def __init__(self, norm_nc, label_nc):
 super().__init__()
 ks = 3
 pw = ks // 2
 self.param_free_norm = nn.GroupNorm(32, norm_nc, affine=False)
 self.conv_gamma = nn.Conv2d(label_nc, norm_nc, kernel_size=ks, padding=pw)
 self.conv_beta = nn.Conv2d(label_nc, norm_nc, kernel_size=ks, padding=pw)

 def forward(self, x, local_features):
 normalized = self.param_free_norm(x)
 assert local_features.size()[2:] == x.size()[2:]
 gamma = self.conv_gamma(local_features)
 beta = self.conv_beta(local_features)
 out = normalized * (1 + gamma) + beta
 return out


class LocalResBlock(nn.Module):
 def __init__(
 self,
 channels,
 emb_channels,
 dropout,
 out_channels=None,
 dims=2,
 use_checkpoint=False,
 inject_channels=None,
 ):
 super().__init__()
 self.channels = channels
 self.emb_channels = emb_channels
 self.dropout = dropout
 self.out_channels = out_channels or channels
 self.use_checkpoint = use_checkpoint
 self.norm_in = FDN(channels, inject_channels)
 self.norm_out = FDN(self.out_channels, inject_channels)

 self.in_layers = nn.Sequential(
 nn.Identity(),
 nn.SiLU(),
 conv_nd(dims, channels, self.out_channels, 3, padding=1),
 )

 self.emb_layers = nn.Sequential(
 nn.SiLU(),
 linear(
 emb_channels,
 self.out_channels,
 ),
 )
 self.out_layers = nn.Sequential(
 nn.Identity(),
 nn.SiLU(),
 nn.Dropout(p=dropout),
 zero_module(
 conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
 ),
 )

 if self.out_channels == channels:
 self.skip_connection = nn.Identity()
 else:
 self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)

 def forward(self, x, emb, local_conditions):
 return checkpoint(
 self._forward, (x, emb, local_conditions), self.parameters(), self.use_checkpoint
 )

 def _forward(self, x, emb, local_conditions):
 local_conditions = F.interpolate(local_conditions, x.shape[-2:], mode="bilinear")
 h = self.norm_in(x, local_conditions)
 h = self.in_layers(h)
 
 emb_out = self.emb_layers(emb).type(h.dtype)
 while len(emb_out.shape) < len(h.shape):
 emb_out = emb_out[..., None]
 
 h = h + emb_out
 h = self.norm_out(h, local_conditions)
 h = self.out_layers(h)
 
 return self.skip_connection(x) + h


class LocalAdapter(nn.Module):
 def __init__(
 self,
 in_channels,
 model_channels,
 local_channels,
 inject_channels,
 inject_layers,
 query_channels,
 query_layers,
 query_scales,
 num_res_blocks,
 attention_resolutions,
 dropout=0,
 channel_mult=(1, 2, 4, 8),
 conv_resample=True,
 dims=2,
 use_checkpoint=False,
 use_fp16=False,
 num_heads=-1,
 num_head_channels=-1,
 num_heads_upsample=-1,
 use_scale_shift_norm=False,
 resblock_updown=False,
 use_new_attention_order=False,
 use_spatial_transformer=False, # custom transformer support
 transformer_depth=1, # custom transformer support
 context_dim=None, # custom transformer support
 n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
 legacy=True,
 disable_self_attentions=None,
 num_attention_blocks=None,
 disable_middle_self_attn=False,
 use_linear_in_transformer=False,
 ):
 super().__init__()
 if use_spatial_transformer:
 assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'

 if context_dim is not None:
 assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
 from omegaconf.listconfig import ListConfig
 if type(context_dim) == ListConfig:
 context_dim = list(context_dim)

 if num_heads_upsample == -1:
 num_heads_upsample = num_heads

 if num_heads == -1:
 assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'

 if num_head_channels == -1:
 assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'

 self.dims = dims
 self.in_channels = in_channels
 self.model_channels = model_channels
 self.inject_layers = inject_layers
 if isinstance(num_res_blocks, int):
 self.num_res_blocks = len(channel_mult) * [num_res_blocks]
 else:
 if len(num_res_blocks) != len(channel_mult):
 raise ValueError("provide num_res_blocks either as an int (globally constant) or "
 "as a list/tuple (per-level) with the same length as channel_mult")
 self.num_res_blocks = num_res_blocks
 if disable_self_attentions is not None:
 # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
 assert len(disable_self_attentions) == len(channel_mult)
 if num_attention_blocks is not None:
 assert len(num_attention_blocks) == len(self.num_res_blocks)
 assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
 print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
 f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
 f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
 f"attention will still not be set.")

 self.attention_resolutions = attention_resolutions
 self.dropout = dropout
 self.channel_mult = channel_mult
 self.conv_resample = conv_resample
 self.use_checkpoint = use_checkpoint
 self.dtype = th.float16 if use_fp16 else th.float32
 self.num_heads = num_heads
 self.num_head_channels = num_head_channels
 self.num_heads_upsample = num_heads_upsample
 self.predict_codebook_ids = n_embed is not None

 self.query_channels = query_channels
 self.query_layers = query_layers
 self.query_scales = query_scales
 visual_projs = []
 for query_channel, inject_channel in zip(query_channels, inject_channels):
 layer_proj = zero_module(linear(query_channel, inject_channel))
 visual_projs.append(layer_proj)
 self.visual_projs = nn.ModuleList(visual_projs)

 time_embed_dim = model_channels * 4
 self.time_embed = nn.Sequential(
 linear(model_channels, time_embed_dim),
 nn.SiLU(),
 linear(time_embed_dim, time_embed_dim),
 )

 self.input_blocks = nn.ModuleList(
 [
 LocalTimestepEmbedSequential(
 conv_nd(dims, in_channels, model_channels, 3, padding=1)
 )
 ]
 )
 self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])

 self._feature_size = model_channels
 input_block_chans = [model_channels]
 ch = model_channels
 ds = 1
 for level, mult in enumerate(channel_mult):
 for nr in range(self.num_res_blocks[level]):
 if (1 + 3*level + nr) in self.inject_layers:
 layers = [
 LocalResBlock(
 ch,
 time_embed_dim,
 dropout,
 out_channels=mult * model_channels,
 dims=dims,
 use_checkpoint=use_checkpoint,
 inject_channels=inject_channels[level],
 )
 ]
 else:
 layers = [
 ResBlock(
 ch,
 time_embed_dim,
 dropout,
 out_channels=mult * model_channels,
 dims=dims,
 use_checkpoint=use_checkpoint,
 use_scale_shift_norm=use_scale_shift_norm,
 )
 ]
 ch = mult * model_channels
 if ds in attention_resolutions:
 if num_head_channels == -1:
 dim_head = ch // num_heads
 else:
 num_heads = ch // num_head_channels
 dim_head = num_head_channels
 if legacy:
 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
 if exists(disable_self_attentions):
 disabled_sa = disable_self_attentions[level]
 else:
 disabled_sa = False

 if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
 layers.append(
 AttentionBlock(
 ch,
 use_checkpoint=use_checkpoint,
 num_heads=num_heads,
 num_head_channels=dim_head,
 use_new_attention_order=use_new_attention_order,
 ) if not use_spatial_transformer else SpatialTransformer(
 ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
 disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
 use_checkpoint=use_checkpoint
 )
 )
 self.input_blocks.append(LocalTimestepEmbedSequential(*layers))
 self.zero_convs.append(self.make_zero_conv(ch))
 self._feature_size += ch
 input_block_chans.append(ch)
 if level != len(channel_mult) - 1:
 out_ch = ch
 self.input_blocks.append(
 LocalTimestepEmbedSequential(
 ResBlock(
 ch,
 time_embed_dim,
 dropout,
 out_channels=out_ch,
 dims=dims,
 use_checkpoint=use_checkpoint,
 use_scale_shift_norm=use_scale_shift_norm,
 down=True,
 )
 if resblock_updown
 else Downsample(
 ch, conv_resample, dims=dims, out_channels=out_ch
 )
 )
 )
 ch = out_ch
 input_block_chans.append(ch)
 self.zero_convs.append(self.make_zero_conv(ch))
 ds *= 2
 self._feature_size += ch

 if num_head_channels == -1:
 dim_head = ch // num_heads
 else:
 num_heads = ch // num_head_channels
 dim_head = num_head_channels
 if legacy:
 dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
 self.middle_block = LocalTimestepEmbedSequential(
 ResBlock(
 ch,
 time_embed_dim,
 dropout,
 dims=dims,
 use_checkpoint=use_checkpoint,
 use_scale_shift_norm=use_scale_shift_norm,
 ),
 AttentionBlock(
 ch,
 use_checkpoint=use_checkpoint,
 num_heads=num_heads,
 num_head_channels=dim_head,
 use_new_attention_order=use_new_attention_order,
 ) if not use_spatial_transformer else SpatialTransformer(
 ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
 disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
 use_checkpoint=use_checkpoint
 ),
 ResBlock(
 ch,
 time_embed_dim,
 dropout,
 dims=dims,
 use_checkpoint=use_checkpoint,
 use_scale_shift_norm=use_scale_shift_norm,
 ),
 )
 self.middle_block_out = self.make_zero_conv(ch)
 self._feature_size += ch

 def make_zero_conv(self, channels):
 return LocalTimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))

 def extract_local_features(self, q_former, text, local_conditions):
 # extract local features
 bs, chn, h, w = local_conditions.shape
 n = chn // 3
 image_features_frozen, image_atts = q_former.forward_visual_encoder(local_conditions.view(bs * n, 3, h, w))
 bs_n, seq_len, v_chn = image_features_frozen[0].shape 

 # with pos embed
 image_features_frozen = [q_former.crossattn_embeddings(image_feat) for image_feat in image_features_frozen]

 # image_features_frozen: [bs * n, seq_len, c]
 image_features_frozen = [image_feat.view(bs, n*seq_len, v_chn) for image_feat in image_features_frozen]
 image_atts = [image_att.view(bs, -1) for image_att in image_atts]

 local_embeddings = q_former.forward_qformer(text, image_features_frozen, image_atts)

 # process qformer features
 local_features = []
 for lvl, scale_factor, visual_proj in zip(self.query_layers, self.query_scales, self.visual_projs):
 local_emb = local_embeddings[lvl]
 _, seq_len, ndim = local_emb.shape
 l = int(seq_len ** 0.5)
 local_emb = F.interpolate(local_emb.transpose(1,2).view(bs, -1, l, l), None, scale_factor=scale_factor, mode="bilinear")
 local_emb = visual_proj(local_emb.transpose(1,2).transpose(2,3).flatten(1,2))
 local_emb = local_emb.view(bs, int(l*scale_factor), int(l*scale_factor), -1).transpose(2,3).transpose(1,2)
 local_features.append(local_emb)
 return local_features

 def forward(self, x, timesteps, context, local_features, **kwargs):
 t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
 emb = self.time_embed(t_emb)

 outs = []
 h = x.type(self.dtype)
 for layer_idx, (module, zero_conv) in enumerate(zip(self.input_blocks, self.zero_convs)):
 if layer_idx in self.inject_layers:
 h = module(h, emb, context, local_control=local_features[self.inject_layers.index(layer_idx)])
 else:
 h = module(h, emb, context)
 outs.append(zero_conv(h, emb, context))

 h = self.middle_block(h, emb, context)
 outs.append(self.middle_block_out(h, emb, context))

 return outs


class LocalControlUNetModel(UNetModel):
 def forward(self, x, timesteps=None, context=None, local_control=None, content_control=None, color_control=None, local_w=1.0, content_w=1.0, color_w=1.0, **kwargs):
 hs = []

 with torch.no_grad():
 t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
 emb = self.time_embed(t_emb)
 h = x.type(self.dtype)
 for module in self.input_blocks:
 h = module(h, emb, context, content_control=content_control, color_control=color_control, content_w=content_w, color_w=color_w)
 hs.append(h)
 h = self.middle_block(h, emb, context, content_control=content_control, color_control=color_control, content_w=content_w, color_w=color_w)

 h += local_w * local_control.pop()

 for module in self.output_blocks:
 h = torch.cat([h, hs.pop() + local_w * local_control.pop()], dim=1)
 h = module(h, emb, context, content_control=content_control, color_control=color_control, content_w=content_w, color_w=color_w)

 h = h.type(x.dtype)
 return self.out(h)