cldm/warping_cldm_network.py

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

from ldm.modules.diffusionmodules.util import (
    conv_nd,
    linear,
    zero_module,
    timestep_embedding
)

from einops import rearrange
from ldm.modules.attention import SpatialTransformer
from ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock
from ldm.util import exists

class StableVITON(UNetModel):
    def __init__(
        self,
        dim_head_denorm=1,
        *args,
        **kwargs,
    ):
        super().__init__(*args, **kwargs)
        warp_flow_blks = []
        warp_zero_convs = []

        self.encode_output_chs = [
            320,
            320,
            640,
            640,
            640,
            1280, 
            1280, 
            1280, 
            1280
        ]

        self.encode_output_chs2 = [
            320,
            320,
            320,
            320,
            640, 
            640, 
            640,
            1280, 
            1280
        ]

        
        for in_ch, cont_ch in zip(self.encode_output_chs, self.encode_output_chs2):
            dim_head = in_ch // self.num_heads
            dim_head = dim_head // dim_head_denorm
            warp_flow_blks.append(SpatialTransformer(
                in_channels=in_ch,
                n_heads=self.num_heads,
                d_head=dim_head,
                depth=self.transformer_depth,
                context_dim=cont_ch,
                use_linear=self.use_linear_in_transformer,
                use_checkpoint=self.use_checkpoint,
            ))
            warp_zero_convs.append(self.make_zero_conv(in_ch))
        self.warp_flow_blks = nn.ModuleList(reversed(warp_flow_blks))
        self.warp_zero_convs = nn.ModuleList(reversed(warp_zero_convs))
    def make_zero_conv(self, channels):
        return zero_module(conv_nd(2, channels, channels, 1, padding=0))
    def forward(self, x, timesteps=None, context=None, control=None, only_mid_control=False, **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)
                hs.append(h)
            h = self.middle_block(h, emb, context)

        if control is not None:                 
            hint = control.pop()
            
        for module in self.output_blocks[:3]:
            control.pop()
            h = torch.cat([h, hs.pop()], dim=1)
            h = module(h, emb, context)

        n_warp = len(self.encode_output_chs)
        for i, (module, warp_blk, warp_zc) in enumerate(zip(self.output_blocks[3:n_warp+3], self.warp_flow_blks, self.warp_zero_convs)):
            if control is None or (h.shape[-2] == 8 and h.shape[-1] == 6):
                assert 0, f"shape is wrong : {h.shape}"
            else:
                hint = control.pop()
                h = self.warp(h, hint, warp_blk, warp_zc)
                h = torch.cat([h, hs.pop()], dim=1)
            h = module(h, emb, context)
        for module in self.output_blocks[n_warp+3:]:
            if control is None:
                h = torch.cat([h, hs.pop()], dim=1)
            else:
                h = torch.cat([h, hs.pop()], dim=1)
            h = module(h, emb, context)
        h = h.type(x.dtype)
        return self.out(h)
    def warp(self, x, hint, crossattn_layer, zero_conv, mask1=None, mask2=None):
        hint = rearrange(hint, "b c h w -> b (h w) c").contiguous()
        output = crossattn_layer(x, hint)
        output = zero_conv(output)
        return output + x
class NoZeroConvControlNet(nn.Module):
    def __init__(
            self,
            image_size,
            in_channels,
            model_channels,
            hint_channels,
            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,  
            legacy=True,
            disable_self_attentions=None,
            num_attention_blocks=None,
            disable_middle_self_attn=False,
            use_linear_in_transformer=False,
            use_VAEdownsample=False,
            cond_first_ch=8,
    ):
        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.image_size = image_size
        self.in_channels = in_channels
        self.model_channels = model_channels
        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 um_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.use_VAEdownsample = use_VAEdownsample

        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(
            [
                TimestepEmbedSequential(
                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
                )
            ]
        )

        self.cond_first_block = TimestepEmbedSequential(
            zero_module(conv_nd(dims, cond_first_ch, model_channels, 3, padding=1))
        )


        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]):
                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:
                        # num_heads = 1
                        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(TimestepEmbedSequential(*layers))
                self._feature_size += ch
                input_block_chans.append(ch)
            if level != len(channel_mult) - 1:
                out_ch = ch
                self.input_blocks.append(
                    TimestepEmbedSequential(
                        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)
                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:
            # num_heads = 1
            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
        self.middle_block = TimestepEmbedSequential(
            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(  # always uses a self-attn
                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._feature_size += ch

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

        if not self.use_VAEdownsample:
            guided_hint = self.input_hint_block(hint, emb, context)
        else:
            guided_hint = self.cond_first_block(hint, emb, context)

        outs = []
        hs = []
        h = x.type(self.dtype)
        for module in self.input_blocks:
            if guided_hint is not None:
                h = module(h, emb, context)
                h += guided_hint
                hs.append(h)
                guided_hint = None
            else:                                                
                h = module(h, emb, context)
                hs.append(h)
            outs.append(h)

        h = self.middle_block(h, emb, context)
        outs.append(h)
        return outs, None