model/blocks/DownBlock2D.py

from typing import Optional, Tuple
from torch import nn
from diffusers.models.resnet import Downsample2D
import torch
from diffusers.utils import is_torch_version
from .ResnetBlock2D import ResnetBlock2D

class DownBlock2D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        temb_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_time_scale_shift: str = "default",
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        resnet_pre_norm: bool = True,
        output_scale_factor: float = 1.0,
        add_downsample: bool = True,
        downsample_padding: int = 1,
        normalization_type = None,
        SPADE_chs = (320, 640, 1280, 1280),
        is_crossAttn = False,
    ):
        super().__init__()
        resnets = []

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                ResnetBlock2D(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    temb_channels=temb_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    time_embedding_norm=resnet_time_scale_shift,
                    non_linearity=resnet_act_fn,
                    output_scale_factor=output_scale_factor,
                    pre_norm=resnet_pre_norm,
                    SPADE_chs=SPADE_chs,
                    normalization_type=normalization_type,
                    is_crossAttn = is_crossAttn
                )
            )

        self.resnets = nn.ModuleList(resnets)

        if add_downsample:
            self.downsamplers = nn.ModuleList(
                [
                    Downsample2D(
                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
                    )
                ]
            )
        else:
            self.downsamplers = None

        self.gradient_checkpointing = False

    def forward(
        self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, 
        scale: float = 1.0, segmap=None,
    ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]:
        output_states = ()

        for resnet in self.resnets:
            if self.training and self.gradient_checkpointing:

                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs)

                    return custom_forward

                if is_torch_version(">=", "1.11.0"):
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet), hidden_states, temb, use_reentrant=False
                    )
                else:
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(resnet), hidden_states, temb
                    )
            else:
                hidden_states = resnet(hidden_states, temb, scale=scale, segmaps=segmap)

            output_states = output_states + (hidden_states,)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                hidden_states = downsampler(hidden_states, scale=scale)

            output_states = output_states + (hidden_states,)

        return hidden_states, output_states