model/unet_sdxl.py

# Modified only lines 360–365; search for 'changed section' to locate the update easily.

from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple, Union

import torch
import torch.nn as nn
import torch.utils.checkpoint

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin
from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers
from diffusers.models.activations import get_activation
from diffusers.models.attention_processor import (
    ADDED_KV_ATTENTION_PROCESSORS,
    CROSS_ATTENTION_PROCESSORS,
    Attention,
    AttentionProcessor,
    AttnAddedKVProcessor,
    AttnProcessor,
)
from diffusers.models.embeddings import (
    GaussianFourierProjection,
    GLIGENTextBoundingboxProjection,
    ImageHintTimeEmbedding,
    ImageProjection,
    ImageTimeEmbedding,
    TextImageProjection,
    TextImageTimeEmbedding,
    TextTimeEmbedding,
    TimestepEmbedding,
    Timesteps,
)
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.unets.unet_2d_blocks import (
    get_down_block,
    get_mid_block,
    get_up_block,
)

from diffusers.models.unets import UNet2DConditionModel
from model.modules.dift_utils import DIFTLatentStore


@dataclass
class UNet2DConditionOutput(BaseOutput):
    sample: torch.FloatTensor = None

class OursUNet2DConditionModel(UNet2DConditionModel):
    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        sample_size: Optional[int] = None,
        in_channels: int = 4,
        out_channels: int = 4,
        center_input_sample: bool = False,
        flip_sin_to_cos: bool = True,
        freq_shift: int = 0,
        down_block_types: Tuple[str] = (
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "CrossAttnDownBlock2D",
            "DownBlock2D",
        ),
        mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
        only_cross_attention: Union[bool, Tuple[bool]] = False,
        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
        layers_per_block: Union[int, Tuple[int]] = 2,
        downsample_padding: int = 1,
        mid_block_scale_factor: float = 1,
        dropout: float = 0.0,
        act_fn: str = "silu",
        norm_num_groups: Optional[int] = 32,
        norm_eps: float = 1e-5,
        cross_attention_dim: Union[int, Tuple[int]] = 1280,
        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
        reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
        encoder_hid_dim: Optional[int] = None,
        encoder_hid_dim_type: Optional[str] = None,
        attention_head_dim: Union[int, Tuple[int]] = 8,
        num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
        dual_cross_attention: bool = False,
        use_linear_projection: bool = False,
        class_embed_type: Optional[str] = None,
        addition_embed_type: Optional[str] = None,
        addition_time_embed_dim: Optional[int] = None,
        num_class_embeds: Optional[int] = None,
        upcast_attention: bool = False,
        resnet_time_scale_shift: str = "default",
        resnet_skip_time_act: bool = False,
        resnet_out_scale_factor: float = 1.0,
        time_embedding_type: str = "positional",
        time_embedding_dim: Optional[int] = None,
        time_embedding_act_fn: Optional[str] = None,
        timestep_post_act: Optional[str] = None,
        time_cond_proj_dim: Optional[int] = None,
        conv_in_kernel: int = 3,
        conv_out_kernel: int = 3,
        projection_class_embeddings_input_dim: Optional[int] = None,
        attention_type: str = "default",
        class_embeddings_concat: bool = False,
        mid_block_only_cross_attention: Optional[bool] = None,
        cross_attention_norm: Optional[str] = None,
        addition_embed_type_num_heads: int = 64,
        steps: List[int] = list(range(1, 1000)),
    ):
        super().__init__(
            sample_size=sample_size,
            in_channels=in_channels,
            out_channels=out_channels,
            center_input_sample=center_input_sample,
            flip_sin_to_cos=flip_sin_to_cos,
            freq_shift=freq_shift,
            down_block_types=down_block_types,
            mid_block_type=mid_block_type,
            up_block_types=up_block_types,
            only_cross_attention=only_cross_attention,
            block_out_channels=block_out_channels,
            layers_per_block=layers_per_block,
            downsample_padding=downsample_padding,
            mid_block_scale_factor=mid_block_scale_factor,
            dropout=dropout,
            act_fn=act_fn,
            norm_num_groups=norm_num_groups,
            norm_eps=norm_eps,
            cross_attention_dim=cross_attention_dim,
            transformer_layers_per_block=transformer_layers_per_block,
            reverse_transformer_layers_per_block=reverse_transformer_layers_per_block,
            encoder_hid_dim=encoder_hid_dim,
            encoder_hid_dim_type=encoder_hid_dim_type,
            attention_head_dim=attention_head_dim,
            num_attention_heads=num_attention_heads,
            dual_cross_attention=dual_cross_attention,
            use_linear_projection=use_linear_projection,
            class_embed_type=class_embed_type,
            addition_embed_type=addition_embed_type,
            addition_time_embed_dim=addition_time_embed_dim,
            num_class_embeds=num_class_embeds,
            upcast_attention=upcast_attention,
            resnet_time_scale_shift=resnet_time_scale_shift,
            resnet_skip_time_act=resnet_skip_time_act,
            resnet_out_scale_factor=resnet_out_scale_factor,
            time_embedding_type=time_embedding_type,
            time_embedding_dim=time_embedding_dim,
            time_embedding_act_fn=time_embedding_act_fn,
            timestep_post_act=timestep_post_act,
            time_cond_proj_dim=time_cond_proj_dim,
            conv_in_kernel=conv_in_kernel,
            conv_out_kernel=conv_out_kernel,
            projection_class_embeddings_input_dim=projection_class_embeddings_input_dim,
            attention_type=attention_type,
            class_embeddings_concat=class_embeddings_concat,
            mid_block_only_cross_attention=mid_block_only_cross_attention,
            cross_attention_norm=cross_attention_norm,
            addition_embed_type_num_heads=addition_embed_type_num_heads,
        )

        self.latent_store = DIFTLatentStore(steps=steps, up_ft_indices=[0, 1, 2])
    
    def forward(
        self,
        sample: torch.FloatTensor,
        timestep: Union[torch.Tensor, float, int],
        encoder_hidden_states: torch.Tensor,
        class_labels: Optional[torch.Tensor] = None,
        timestep_cond: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
        mid_block_additional_residual: Optional[torch.Tensor] = None,
        down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        return_dict: bool = True,
    ) -> Union[UNet2DConditionOutput, Tuple]:
        default_overall_up_factor = 2**self.num_upsamplers

        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
        forward_upsample_size = False
        upsample_size = None

        for dim in sample.shape[-2:]:
            if dim % default_overall_up_factor != 0:
                # Forward upsample size to force interpolation output size.
                forward_upsample_size = True
                break

        if attention_mask is not None:
            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
            attention_mask = attention_mask.unsqueeze(1)

        # convert encoder_attention_mask to a bias the same way we do for attention_mask
        if encoder_attention_mask is not None:
            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)

        # 0. center input if necessary
        if self.config.center_input_sample:
            sample = 2 * sample - 1.0

        # 1. time
        t_emb = self.get_time_embed(sample=sample, timestep=timestep)
        emb = self.time_embedding(t_emb, timestep_cond)
        aug_emb = None

        class_emb = self.get_class_embed(sample=sample, class_labels=class_labels)
        if class_emb is not None:
            if self.config.class_embeddings_concat:
                emb = torch.cat([emb, class_emb], dim=-1)
            else:
                emb = emb + class_emb

        aug_emb = self.get_aug_embed(
            emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
        )
        if self.config.addition_embed_type == "image_hint":
            aug_emb, hint = aug_emb
            sample = torch.cat([sample, hint], dim=1)

        emb = emb + aug_emb if aug_emb is not None else emb

        if self.time_embed_act is not None:
            emb = self.time_embed_act(emb)

        encoder_hidden_states = self.process_encoder_hidden_states(
            encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs
        )

        # 2. pre-process
        sample = self.conv_in(sample)

        # 2.5 GLIGEN position net
        if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None:
            cross_attention_kwargs = cross_attention_kwargs.copy()
            gligen_args = cross_attention_kwargs.pop("gligen")
            cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)}

        # 3. down
        # we're popping the `scale` instead of getting it because otherwise `scale` will be propagated
        # to the internal blocks and will raise deprecation warnings. this will be confusing for our users.
        if cross_attention_kwargs is not None:
            cross_attention_kwargs = cross_attention_kwargs.copy()
            lora_scale = cross_attention_kwargs.pop("scale", 1.0)
        else:
            lora_scale = 1.0

        if USE_PEFT_BACKEND:
            # weight the lora layers by setting `lora_scale` for each PEFT layer
            scale_lora_layers(self, lora_scale)

        is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
        # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets
        is_adapter = down_intrablock_additional_residuals is not None

        if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None:
            deprecate(
                "T2I should not use down_block_additional_residuals",
                "1.3.0",
                "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \
                       and will be removed in diffusers 1.3.0.  `down_block_additional_residuals` should only be used \
                       for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ",
                standard_warn=False,
            )
            down_intrablock_additional_residuals = down_block_additional_residuals
            is_adapter = True

        down_block_res_samples = (sample,)
        for downsample_block in self.down_blocks:
            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
                # For t2i-adapter CrossAttnDownBlock2D
                additional_residuals = {}
                if is_adapter and len(down_intrablock_additional_residuals) > 0:
                    additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0)

                sample, res_samples = downsample_block(
                    hidden_states=sample,
                    temb=emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=attention_mask,
                    cross_attention_kwargs=cross_attention_kwargs,
                    encoder_attention_mask=encoder_attention_mask,
                    **additional_residuals,
                )
            else:
                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
                if is_adapter and len(down_intrablock_additional_residuals) > 0:
                    sample += down_intrablock_additional_residuals.pop(0)

            down_block_res_samples += res_samples

        if is_controlnet:
            new_down_block_res_samples = ()

            for down_block_res_sample, down_block_additional_residual in zip(
                down_block_res_samples, down_block_additional_residuals
            ):
                down_block_res_sample = down_block_res_sample + down_block_additional_residual
                new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)

            down_block_res_samples = new_down_block_res_samples

        # 4. mid
        if self.mid_block is not None:
            if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention:
                sample = self.mid_block(
                    sample,
                    emb,
                    encoder_hidden_states=encoder_hidden_states,
                    attention_mask=attention_mask,
                    cross_attention_kwargs=cross_attention_kwargs,
                    encoder_attention_mask=encoder_attention_mask,
                )
            else:
                sample = self.mid_block(sample, emb)

            # To support T2I-Adapter-XL
            if (
                is_adapter
                and len(down_intrablock_additional_residuals) > 0
                and sample.shape == down_intrablock_additional_residuals[0].shape
            ):
                sample += down_intrablock_additional_residuals.pop(0)

        if is_controlnet:
            sample = sample + mid_block_additional_residual

        # 5. up
        for i, upsample_block in enumerate(self.up_blocks):
            is_final_block = i == len(self.up_blocks) - 1

            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]

            # if we have not reached the final block and need to forward the
            # upsample size, we do it here
            if not is_final_block and forward_upsample_size:
                upsample_size = down_block_res_samples[-1].shape[2:]
            
            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
                sample = upsample_block(
                    hidden_states=sample,
                    temb=emb,
                    res_hidden_states_tuple=res_samples,
                    encoder_hidden_states=encoder_hidden_states,
                    cross_attention_kwargs=cross_attention_kwargs,
                    upsample_size=upsample_size,
                    attention_mask=attention_mask,
                    encoder_attention_mask=encoder_attention_mask,
                )
            else:
                sample = upsample_block(
                    hidden_states=sample,
                    temb=emb,
                    res_hidden_states_tuple=res_samples,
                    upsample_size=upsample_size,
                )
            
            ########################## changed section ##########################
            self.latent_store(sample.detach(), t=timestep, layer_index=i)
            self.latent_store.smooth(kernel_size=3, sigma=1)
            #####################################################################

        # 6. post-process
        if self.conv_norm_out:
            sample = self.conv_norm_out(sample)
            sample = self.conv_act(sample)
        sample = self.conv_out(sample)

        if USE_PEFT_BACKEND:
            # remove `lora_scale` from each PEFT layer
            unscale_lora_layers(self, lora_scale)

        if not return_dict:
            return (sample,)

        return UNet2DConditionOutput(sample=sample)