transformer/pixart_transformer_modified.py

# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, Optional, Union, Tuple, List

import torch
from torch import nn
import torch.nn.functional as F

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils import is_torch_version, logging, deprecate
from diffusers.utils.torch_utils import maybe_allow_in_graph
from diffusers.models.attention_processor import Attention, AttentionProcessor, AttnProcessor, FusedAttnProcessor2_0, JointAttnProcessor2_0
from diffusers.models.embeddings import PatchEmbed, PixArtAlphaTextProjection
from diffusers.models.modeling_outputs import Transformer2DModelOutput
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import AdaLayerNorm, AdaLayerNormContinuous, AdaLayerNormZero, RMSNorm, SD35AdaLayerNormZeroX, AdaLayerNormSingle
from torch.nn.utils.rnn import pad_sequence
from einops import rearrange
import numpy as np
from diffusers.models.activations import GEGLU, GELU, ApproximateGELU, FP32SiLU, LinearActivation, SwiGLU
from diffusers.models.embeddings import SinusoidalPositionalEmbedding


logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


class PixArtTransformer2DModel(ModelMixin, ConfigMixin):
    r"""
    A 2D Transformer model as introduced in PixArt family of models (https://arxiv.org/abs/2310.00426,
    https://arxiv.org/abs/2403.04692).

    Parameters:
        num_attention_heads (int, optional, defaults to 16): The number of heads to use for multi-head attention.
        attention_head_dim (int, optional, defaults to 72): The number of channels in each head.
        in_channels (int, defaults to 4): The number of channels in the input.
        out_channels (int, optional):
            The number of channels in the output. Specify this parameter if the output channel number differs from the
            input.
        num_layers (int, optional, defaults to 28): The number of layers of Transformer blocks to use.
        dropout (float, optional, defaults to 0.0): The dropout probability to use within the Transformer blocks.
        norm_num_groups (int, optional, defaults to 32):
            Number of groups for group normalization within Transformer blocks.
        cross_attention_dim (int, optional):
            The dimensionality for cross-attention layers, typically matching the encoder's hidden dimension.
        attention_bias (bool, optional, defaults to True):
            Configure if the Transformer blocks' attention should contain a bias parameter.
        sample_size (int, defaults to 128):
            The width of the latent images. This parameter is fixed during training.
        patch_size (int, defaults to 2):
            Size of the patches the model processes, relevant for architectures working on non-sequential data.
        activation_fn (str, optional, defaults to "gelu-approximate"):
            Activation function to use in feed-forward networks within Transformer blocks.
        num_embeds_ada_norm (int, optional, defaults to 1000):
            Number of embeddings for AdaLayerNorm, fixed during training and affects the maximum denoising steps during
            inference.
        upcast_attention (bool, optional, defaults to False):
            If true, upcasts the attention mechanism dimensions for potentially improved performance.
        norm_type (str, optional, defaults to "ada_norm_zero"):
            Specifies the type of normalization used, can be 'ada_norm_zero'.
        norm_elementwise_affine (bool, optional, defaults to False):
            If true, enables element-wise affine parameters in the normalization layers.
        norm_eps (float, optional, defaults to 1e-6):
            A small constant added to the denominator in normalization layers to prevent division by zero.
        interpolation_scale (int, optional): Scale factor to use during interpolating the position embeddings.
        use_additional_conditions (bool, optional): If we're using additional conditions as inputs.
        attention_type (str, optional, defaults to "default"): Kind of attention mechanism to be used.
        caption_channels (int, optional, defaults to None):
            Number of channels to use for projecting the caption embeddings.
        use_linear_projection (bool, optional, defaults to False):
            Deprecated argument. Will be removed in a future version.
        num_vector_embeds (bool, optional, defaults to False):
            Deprecated argument. Will be removed in a future version.
    """

    _supports_gradient_checkpointing = True
    _no_split_modules = ["BasicTransformerBlock", "PatchEmbed"]

    @register_to_config
    def __init__(
        self,
        num_attention_heads: int = 16,
        attention_head_dim: int = 72,
        in_channels: int = 4,
        out_channels: Optional[int] = 8,
        num_layers: int = 28,
        dropout: float = 0.0,
        norm_num_groups: int = 32,
        cross_attention_dim: Optional[int] = 1152,
        attention_bias: bool = True,
        sample_size: int = 128,
        patch_size: int = 2,
        activation_fn: str = "gelu-approximate",
        num_embeds_ada_norm: Optional[int] = 1000,
        upcast_attention: bool = False,
        norm_type: str = "ada_norm_single",
        norm_elementwise_affine: bool = False,
        norm_eps: float = 1e-6,
        interpolation_scale: Optional[int] = None,
        use_additional_conditions: Optional[bool] = None,
        caption_channels: Optional[int] = None,
        attention_type: Optional[str] = "default",
    ):
        super().__init__()

        # Validate inputs.
        if norm_type != "ada_norm_single":
            raise NotImplementedError(
                f"Forward pass is not implemented when `patch_size` is not None and `norm_type` is '{norm_type}'."
            )
        elif norm_type == "ada_norm_single" and num_embeds_ada_norm is None:
            raise ValueError(
                f"When using a `patch_size` and this `norm_type` ({norm_type}), `num_embeds_ada_norm` cannot be None."
            )

        # Set some common variables used across the board.
        self.attention_head_dim = attention_head_dim
        self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
        self.out_channels = in_channels if out_channels is None else out_channels
        if use_additional_conditions is None:
            if sample_size == 128:
                use_additional_conditions = True
            else:
                use_additional_conditions = False
        self.use_additional_conditions = use_additional_conditions

        self.gradient_checkpointing = False

        # 2. Initialize the position embedding and transformer blocks.
        self.height = self.config.sample_size
        self.width = self.config.sample_size

        interpolation_scale = (
            self.config.interpolation_scale
            if self.config.interpolation_scale is not None
            else max(self.config.sample_size // 64, 1)
        )
        self.pos_embed = PatchEmbed(
            height=self.config.sample_size,
            width=self.config.sample_size,
            patch_size=self.config.patch_size,
            in_channels=self.config.in_channels,
            embed_dim=self.inner_dim,
            interpolation_scale=interpolation_scale,
        )

        self.transformer_blocks = nn.ModuleList(
            [
                BasicTransformerBlock(
                    self.inner_dim,
                    self.config.num_attention_heads,
                    self.config.attention_head_dim,
                    dropout=self.config.dropout,
                    cross_attention_dim=self.config.cross_attention_dim,
                    activation_fn=self.config.activation_fn,
                    num_embeds_ada_norm=self.config.num_embeds_ada_norm,
                    attention_bias=self.config.attention_bias,
                    upcast_attention=self.config.upcast_attention,
                    norm_type=norm_type,
                    norm_elementwise_affine=self.config.norm_elementwise_affine,
                    norm_eps=self.config.norm_eps,
                    attention_type=self.config.attention_type,
                )
                for _ in range(self.config.num_layers)
            ]
        )

        # 3. Output blocks.
        self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6)
        self.scale_shift_table = nn.Parameter(torch.randn(2, self.inner_dim) / self.inner_dim**0.5)
        self.proj_out = nn.Linear(self.inner_dim, self.config.patch_size * self.config.patch_size * self.out_channels)

        self.adaln_single = AdaLayerNormSingle(
            self.inner_dim, use_additional_conditions=self.use_additional_conditions
        )
        self.caption_projection = None
        if self.config.caption_channels is not None:
            self.caption_projection = PixArtAlphaTextProjection(
                in_features=self.config.caption_channels, hidden_size=self.inner_dim
            )
        self.ip_adapter = IPAdapter()

    def _set_gradient_checkpointing(self, module, value=False):
        if hasattr(module, "gradient_checkpointing"):
            module.gradient_checkpointing = value

    @property
    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
    def attn_processors(self) -> Dict[str, AttentionProcessor]:
        r"""
        Returns:
            `dict` of attention processors: A dictionary containing all attention processors used in the model with
            indexed by its weight name.
        """
        # set recursively
        processors = {}

        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
            if hasattr(module, "get_processor"):
                processors[f"{name}.processor"] = module.get_processor()

            for sub_name, child in module.named_children():
                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)

            return processors

        for name, module in self.named_children():
            fn_recursive_add_processors(name, module, processors)

        return processors

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
    def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
        r"""
        Sets the attention processor to use to compute attention.

        Parameters:
            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
                The instantiated processor class or a dictionary of processor classes that will be set as the processor
                for **all** `Attention` layers.

                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
                processor. This is strongly recommended when setting trainable attention processors.

        """
        count = len(self.attn_processors.keys())

        if isinstance(processor, dict) and len(processor) != count:
            raise ValueError(
                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
            )

        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
            if hasattr(module, "set_processor"):
                if not isinstance(processor, dict):
                    module.set_processor(processor)
                else:
                    module.set_processor(processor.pop(f"{name}.processor"))

            for sub_name, child in module.named_children():
                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)

        for name, module in self.named_children():
            fn_recursive_attn_processor(name, module, processor)

    def set_default_attn_processor(self):
        """
        Disables custom attention processors and sets the default attention implementation.

        Safe to just use `AttnProcessor()` as PixArt doesn't have any exotic attention processors in default model.
        """
        self.set_attn_processor(AttnProcessor())

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
    def fuse_qkv_projections(self):
        """
        Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
        are fused. For cross-attention modules, key and value projection matrices are fused.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>
        """
        self.original_attn_processors = None

        for _, attn_processor in self.attn_processors.items():
            if "Added" in str(attn_processor.__class__.__name__):
                raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")

        self.original_attn_processors = self.attn_processors

        for module in self.modules():
            if isinstance(module, Attention):
                module.fuse_projections(fuse=True)

        self.set_attn_processor(FusedAttnProcessor2_0())

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
    def unfuse_qkv_projections(self):
        """Disables the fused QKV projection if enabled.

        <Tip warning={true}>

        This API is 🧪 experimental.

        </Tip>

        """
        if self.original_attn_processors is not None:
            self.set_attn_processor(self.original_attn_processors)

    def forward(
        self,
        hidden_states: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
        encoder_attention_mask: torch.Tensor,
        ip_hidden_states: torch.Tensor = None,
        ip_attention_mask: torch.Tensor = None,
        text_bboxes = None,
        character_bboxes = None,
        reference_embeddings = None,
        cfg_on_10_percent = False,
        timestep: Optional[torch.LongTensor] = None,
        added_cond_kwargs: Dict[str, torch.Tensor] = None,
        cross_attention_kwargs: Dict[str, Any] = None,
        return_dict: bool = True,
    ):
        """
        The [`PixArtTransformer2DModel`] forward method.

        Args:
            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
                Input `hidden_states`.
            encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
                self-attention.
            timestep (`torch.LongTensor`, *optional*):
                Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
            added_cond_kwargs: (`Dict[str, Any]`, *optional*): Additional conditions to be used as inputs.
            cross_attention_kwargs ( `Dict[str, Any]`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            encoder_attention_mask ( `torch.Tensor`, *optional*):
                Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:

                    * Mask `(batch, sequence_length)` True = keep, False = discard.
                    * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.

                If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
                above. This bias will be added to the cross-attention scores.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
                tuple.

        Returns:
            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
            `tuple` where the first element is the sample tensor.
        """
        if self.use_additional_conditions and added_cond_kwargs is None:
            raise ValueError("`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`.")
        # 0. Prompt Embedding Modification
        assert (ip_hidden_states is None) ^ (text_bboxes is None and character_bboxes is None and reference_embeddings is None)
        if ip_hidden_states is None:
            ip_hidden_states, ip_attention_mask = self.ip_adapter(text_bboxes, character_bboxes, reference_embeddings, cfg_on_10_percent)
        
        # 1. Input
        batch_size = len(hidden_states)
        heights = [h.shape[-2] // self.config.patch_size for h in hidden_states]
        widths = [w.shape[-1] // self.config.patch_size for w in hidden_states]
        hidden_states = [self.pos_embed(hs[None])[0] for hs in hidden_states]
        attention_mask = [torch.ones(x.shape[0]) for x in hidden_states]
        hidden_states = pad_sequence(hidden_states, batch_first=True)
        attention_mask = pad_sequence(attention_mask, batch_first=True, padding_value=0).bool().to(hidden_states.device)
        original_attention_mask = attention_mask

        timestep, embedded_timestep = self.adaln_single(
            timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype
        )
        # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
        #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
        #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
        # expects mask of shape:
        #   [batch, key_tokens]
        # adds singleton query_tokens dimension:
        #   [batch,                    1, key_tokens]
        # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
        #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
        #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
        if attention_mask is not None and attention_mask.ndim == 2:
            # assume that mask is expressed as:
            #   (1 = keep,      0 = discard)
            # convert mask into a bias that can be added to attention scores:
            #       (keep = +0,     discard = -10000.0)
            attention_mask = (1 - attention_mask.to(hidden_states.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 and encoder_attention_mask.ndim == 2:
            encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0
            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)

        if self.caption_projection is not None:
            encoder_hidden_states = self.caption_projection(encoder_hidden_states)
            encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1])

        # 2. Blocks
        for block in self.transformer_blocks:
            if torch.is_grad_enabled() and self.gradient_checkpointing:

                def create_custom_forward(module, return_dict=None):
                    def custom_forward(*inputs):
                        if return_dict is not None:
                            return module(*inputs, return_dict=return_dict)
                        else:
                            return module(*inputs)

                    return custom_forward

                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(block),
                    hidden_states,
                    attention_mask,
                    encoder_hidden_states,
                    encoder_attention_mask,
                    ip_hidden_states,
                    ip_attention_mask,
                    timestep,
                    cross_attention_kwargs,
                    None,
                    **ckpt_kwargs,
                )
            else:
                hidden_states = block(
                    hidden_states,
                    attention_mask=attention_mask,
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=encoder_attention_mask,
                    ip_hidden_states=ip_hidden_states,
                    ip_attention_mask=ip_attention_mask,
                    timestep=timestep,
                    cross_attention_kwargs=cross_attention_kwargs,
                    class_labels=None,
                )

        # 3. Output
        shift, scale = (
            self.scale_shift_table[None] + embedded_timestep[:, None].to(self.scale_shift_table.device)
        ).chunk(2, dim=1)
        hidden_states = self.norm_out(hidden_states)
        # Modulation
        hidden_states = hidden_states * (1 + scale.to(hidden_states.device)) + shift.to(hidden_states.device)
        hidden_states = self.proj_out(hidden_states)
        hidden_states = hidden_states.squeeze(1)

        # unpatchify
        outputs = []
        for idx, (height, width) in enumerate(zip(heights, widths)):
            _hidden_state = hidden_states[idx][original_attention_mask[idx]].reshape(
                shape=(height, width, self.config.patch_size, self.config.patch_size, self.out_channels)
            )
            _hidden_state = torch.einsum("hwpqc->chpwq", _hidden_state)
            outputs.append(_hidden_state.reshape(
                shape=(self.out_channels, height * self.config.patch_size, width * self.config.patch_size)
            ))
        
        if len(set([x.shape for x in outputs])) == 1:
            outputs = torch.stack(outputs)

        if not return_dict:
            return (outputs,)

        return Transformer2DModelOutput(sample=outputs)


class RBFEmbedding(nn.Module):
    def __init__(self, output_dim, num_kernels=32):
        super().__init__()
        self.means = nn.Parameter(torch.linspace(0, 1, num_kernels))
        self.scales = nn.Parameter(torch.ones(num_kernels) * 20)
        self.proj = nn.Linear(num_kernels * 4, output_dim)
    
    def forward(self, box):
        box = torch.tensor(box, dtype=self.means.dtype, device=self.means.device)
        x = box.unsqueeze(-1) - self.means
        x = torch.exp(-0.5 * (x * self.scales.unsqueeze(0)) ** 2)
        x = x.reshape(-1)
        return self.proj(x)
    
    def participate_in_grad(self):
        return self.proj.weight.sum() + self.proj.bias.sum() + self.means.sum() + self.scales.sum()

class RoPEPositionalEmbedding(nn.Module):
    def __init__(self, embedding_dim, base=10000):
        super().__init__()
        self.embedding_dim = embedding_dim
        assert embedding_dim % 2 == 0, "Embedding dimension must be even"
        half_dim = embedding_dim // 2
        freqs = 1.0 / (base ** (torch.arange(0, half_dim).float() / half_dim))
        self.register_buffer("freqs", freqs)
    
    def forward(self, x, positions):
        orig_dtype = x.dtype
        x = x.float()
        positions = positions.float()
        x_2d = rearrange(x, '... (d two) -> ... d two', two=2)  # [..., dim/2, 2]
        positions = positions.unsqueeze(-1) * self.freqs.float()  # [seq_len, dim/2]
        sin = positions.sin().unsqueeze(-1)  # [seq_len, dim/2, 1]
        cos = positions.cos().unsqueeze(-1)  # [seq_len, dim/2, 1]
        x_out = torch.cat([
            x_2d[..., 0:1] * cos - x_2d[..., 1:2] * sin,
            x_2d[..., 0:1] * sin + x_2d[..., 1:2] * cos,
        ], dim=-1)
        output = rearrange(x_out, '... d two -> ... (d two)')
        return output.to(orig_dtype)

class IPAdapter(ModelMixin):
    def __init__(self):
        super().__init__()
        self.embedding_dim = 1152
        self.box_embedding = RBFEmbedding(self.embedding_dim)
        self.pos_embedding = RoPEPositionalEmbedding(self.embedding_dim)
        self.text_cls_embedding = nn.Embedding(1, self.embedding_dim)
        self.character_cls_embedding = nn.Embedding(4, self.embedding_dim)
        self.ref_embedding_proj = nn.Linear(768, 4 * self.embedding_dim)
        self.void_ip_embed = nn.Embedding(1, self.embedding_dim)
        self.negative_ip_embed = nn.Embedding(1, self.embedding_dim)
        self.norm = nn.LayerNorm(self.embedding_dim)

    def participate_in_grad(self):
        return sum([
            self.box_embedding.participate_in_grad(),
            self.text_cls_embedding.weight.sum(),
            self.character_cls_embedding.weight.sum(),
            self.ref_embedding_proj.weight.sum(),
            self.ref_embedding_proj.bias.sum(),
            self.void_ip_embed.weight.sum(),
            self.negative_ip_embed.weight.sum(),
            self.norm.weight.sum(),
            self.norm.bias.sum()
        ])

    def embed_text(self, box):
        box_embedding = self.box_embedding(box)
        return torch.stack([
            box_embedding,
            *self.text_cls_embedding.weight,
        ])
    
    def embed_character(self, character_bbox, reference_embedding):
        box_embedding = self.box_embedding(character_bbox)
        if reference_embedding is None:
            character_embedding = self.character_cls_embedding.weight
        else:
            character_embedding = self.ref_embedding_proj(reference_embedding.unsqueeze(0))
            character_embedding = rearrange(character_embedding, "1 (c h) -> h c", h=4)
        return torch.stack([
            box_embedding,
            *character_embedding
        ])
    
    def apply_position_embedding(self, embeddings):
        seq_length = embeddings.shape[0]
        positions = torch.arange(seq_length, device=embeddings.device, dtype=embeddings.dtype)
        return self.pos_embedding(embeddings, positions)

    def forward(self, batch_text_bboxes, batch_character_bboxes, batch_reference_embeddings, cfg_on_10_percent):
        ip_embeddings = []
        for batch_idx, (text_bboxes, character_bboxes, reference_embeddings) in enumerate(zip(batch_text_bboxes, batch_character_bboxes, batch_reference_embeddings)):
            text_embeddings = [self.embed_text(box) for box in text_bboxes]
            character_embeddings = [self.embed_character(box, reference_embeddings[i]) for i, box in enumerate(character_bboxes)]
            if len(text_embeddings) + len(character_embeddings) == 0:
                ip_embeddings.append(self.void_ip_embed.weight)
                continue
            ip_embedding = torch.cat(text_embeddings + character_embeddings, dim=0)
            ip_embeddings.append(self.apply_position_embedding(ip_embedding))

        ip_mask = [torch.ones(x.shape[0], dtype=torch.bool, device=x.device) for x in ip_embeddings]
        ip_embeddings = pad_sequence(ip_embeddings, batch_first=True, padding_value=0)
        ip_mask = pad_sequence(ip_mask, batch_first=True, padding_value=0).bool()
        if cfg_on_10_percent:
            last_10_percent = int(len(ip_embeddings) * 0.1)
            ip_embeddings[-last_10_percent:] = self.negative_ip_embed.weight
            ip_mask[-last_10_percent:] = 0
            ip_mask[-last_10_percent:, :1] = 1
        return self.norm(ip_embeddings), ip_mask


def _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int):
    # "feed_forward_chunk_size" can be used to save memory
    if hidden_states.shape[chunk_dim] % chunk_size != 0:
        raise ValueError(
            f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
        )

    num_chunks = hidden_states.shape[chunk_dim] // chunk_size
    ff_output = torch.cat(
        [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
        dim=chunk_dim,
    )
    return ff_output


@maybe_allow_in_graph
class GatedSelfAttentionDense(nn.Module):
    r"""
    A gated self-attention dense layer that combines visual features and object features.

    Parameters:
        query_dim (`int`): The number of channels in the query.
        context_dim (`int`): The number of channels in the context.
        n_heads (`int`): The number of heads to use for attention.
        d_head (`int`): The number of channels in each head.
    """

    def __init__(self, query_dim: int, context_dim: int, n_heads: int, d_head: int):
        super().__init__()

        # we need a linear projection since we need cat visual feature and obj feature
        self.linear = nn.Linear(context_dim, query_dim)

        self.attn = Attention(query_dim=query_dim, heads=n_heads, dim_head=d_head)
        self.ff = FeedForward(query_dim, activation_fn="geglu")

        self.norm1 = nn.LayerNorm(query_dim)
        self.norm2 = nn.LayerNorm(query_dim)

        self.register_parameter("alpha_attn", nn.Parameter(torch.tensor(0.0)))
        self.register_parameter("alpha_dense", nn.Parameter(torch.tensor(0.0)))

        self.enabled = True

    def forward(self, x: torch.Tensor, objs: torch.Tensor) -> torch.Tensor:
        if not self.enabled:
            return x

        n_visual = x.shape[1]
        objs = self.linear(objs)

        x = x + self.alpha_attn.tanh() * self.attn(self.norm1(torch.cat([x, objs], dim=1)))[:, :n_visual, :]
        x = x + self.alpha_dense.tanh() * self.ff(self.norm2(x))

        return x


@maybe_allow_in_graph
class BasicTransformerBlock(nn.Module):
    r"""
    A basic Transformer block.

    Parameters:
        dim (`int`): The number of channels in the input and output.
        num_attention_heads (`int`): The number of heads to use for multi-head attention.
        attention_head_dim (`int`): The number of channels in each head.
        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
        num_embeds_ada_norm (:
            obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
        attention_bias (:
            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
        only_cross_attention (`bool`, *optional*):
            Whether to use only cross-attention layers. In this case two cross attention layers are used.
        double_self_attention (`bool`, *optional*):
            Whether to use two self-attention layers. In this case no cross attention layers are used.
        upcast_attention (`bool`, *optional*):
            Whether to upcast the attention computation to float32. This is useful for mixed precision training.
        norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
            Whether to use learnable elementwise affine parameters for normalization.
        norm_type (`str`, *optional*, defaults to `"layer_norm"`):
            The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
        final_dropout (`bool` *optional*, defaults to False):
            Whether to apply a final dropout after the last feed-forward layer.
        attention_type (`str`, *optional*, defaults to `"default"`):
            The type of attention to use. Can be `"default"` or `"gated"` or `"gated-text-image"`.
        positional_embeddings (`str`, *optional*, defaults to `None`):
            The type of positional embeddings to apply to.
        num_positional_embeddings (`int`, *optional*, defaults to `None`):
            The maximum number of positional embeddings to apply.
    """

    def __init__(
        self,
        dim: int,
        num_attention_heads: int,
        attention_head_dim: int,
        dropout=0.0,
        cross_attention_dim: Optional[int] = None,
        activation_fn: str = "geglu",
        num_embeds_ada_norm: Optional[int] = None,
        attention_bias: bool = False,
        only_cross_attention: bool = False,
        double_self_attention: bool = False,
        upcast_attention: bool = False,
        norm_elementwise_affine: bool = True,
        norm_type: str = "layer_norm",  # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single', 'ada_norm_continuous', 'layer_norm_i2vgen'
        norm_eps: float = 1e-5,
        final_dropout: bool = False,
        attention_type: str = "default",
        positional_embeddings: Optional[str] = None,
        num_positional_embeddings: Optional[int] = None,
        ada_norm_continous_conditioning_embedding_dim: Optional[int] = None,
        ada_norm_bias: Optional[int] = None,
        ff_inner_dim: Optional[int] = None,
        ff_bias: bool = True,
        attention_out_bias: bool = True,
    ):
        super().__init__()
        self.dim = dim
        self.num_attention_heads = num_attention_heads
        self.attention_head_dim = attention_head_dim
        self.dropout = dropout
        self.cross_attention_dim = cross_attention_dim
        self.activation_fn = activation_fn
        self.attention_bias = attention_bias
        self.double_self_attention = double_self_attention
        self.norm_elementwise_affine = norm_elementwise_affine
        self.positional_embeddings = positional_embeddings
        self.num_positional_embeddings = num_positional_embeddings
        self.only_cross_attention = only_cross_attention

        # Define 3 blocks. Each block has its own normalization layer.
        # 1. Self-Attn
        self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
        self.attn1 = Attention(
            query_dim=dim,
            heads=num_attention_heads,
            dim_head=attention_head_dim,
            dropout=dropout,
            bias=attention_bias,
            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
            upcast_attention=upcast_attention,
            out_bias=attention_out_bias,
        )

        # 2. Cross-Attn
        self.norm2 = nn.LayerNorm(dim, norm_eps, norm_elementwise_affine)
        self.attn2 = Attention(
            query_dim=dim,
            cross_attention_dim=cross_attention_dim if not double_self_attention else None,
            heads=num_attention_heads,
            dim_head=attention_head_dim,
            dropout=dropout,
            bias=attention_bias,
            upcast_attention=upcast_attention,
            out_bias=attention_out_bias,
        )

        self.ip_attn = Attention(
            query_dim=dim,
            cross_attention_dim=cross_attention_dim if not double_self_attention else None,
            heads=num_attention_heads,
            dim_head=attention_head_dim,
            dropout=dropout,
            bias=attention_bias,
            upcast_attention=upcast_attention,
            out_bias=attention_out_bias,
        )
        self.ip_attn.to_out[0].weight.data.zero_()
        self.ip_attn.to_out[0].bias.data.zero_()

        # 3. Feed-forward
        self.ff = FeedForward(
            dim,
            dropout=dropout,
            activation_fn=activation_fn,
            final_dropout=final_dropout,
            inner_dim=ff_inner_dim,
            bias=ff_bias,
        )

        # 5. Scale-shift for PixArt-Alpha.
        self.scale_shift_table = nn.Parameter(torch.randn(6, dim) / dim**0.5)

        # let chunk size default to None
        self._chunk_size = None
        self._chunk_dim = 0

    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
        # Sets chunk feed-forward
        self._chunk_size = chunk_size
        self._chunk_dim = dim

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        ip_hidden_states: Optional[torch.Tensor] = None,
        ip_attention_mask: Optional[torch.Tensor] = None,
        timestep: Optional[torch.LongTensor] = None,
        cross_attention_kwargs: Dict[str, Any] = None,
        class_labels: Optional[torch.LongTensor] = None,
        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
    ) -> torch.Tensor:
        if cross_attention_kwargs is not None:
            if cross_attention_kwargs.get("scale", None) is not None:
                logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.")

        # Notice that normalization is always applied before the real computation in the following blocks.
        # 0. Self-Attention
        batch_size = hidden_states.shape[0]

        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
            self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1)
        ).chunk(6, dim=1)
        norm_hidden_states = self.norm1(hidden_states)
        norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa

        cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {}

        attn_output = self.attn1(
            norm_hidden_states,
            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
            attention_mask=attention_mask,
            **cross_attention_kwargs,
        )

        attn_output = gate_msa * attn_output

        hidden_states = attn_output + hidden_states
        if hidden_states.ndim == 4:
            hidden_states = hidden_states.squeeze(1)

        # 3. Cross-Attention
        attn_output = self.attn2(
            hidden_states,
            encoder_hidden_states=encoder_hidden_states,
            attention_mask=encoder_attention_mask,
            **cross_attention_kwargs,
        )
        ip_attn_output = self.ip_attn(
            hidden_states,
            encoder_hidden_states=ip_hidden_states,
            attention_mask=ip_attention_mask,
            **cross_attention_kwargs,
        )
        hidden_states = attn_output + ip_attn_output + hidden_states

        # 4. Feed-forward
        norm_hidden_states = self.norm2(hidden_states)
        norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp

        if self._chunk_size is not None:
            # "feed_forward_chunk_size" can be used to save memory
            ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
        else:
            ff_output = self.ff(norm_hidden_states)

        ff_output = gate_mlp * ff_output

        hidden_states = ff_output + hidden_states
        if hidden_states.ndim == 4:
            hidden_states = hidden_states.squeeze(1)

        return hidden_states

class FeedForward(nn.Module):
    r"""
    A feed-forward layer.

    Parameters:
        dim (`int`): The number of channels in the input.
        dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
        mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
        final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
        bias (`bool`, defaults to True): Whether to use a bias in the linear layer.
    """

    def __init__(
        self,
        dim: int,
        dim_out: Optional[int] = None,
        mult: int = 4,
        dropout: float = 0.0,
        activation_fn: str = "geglu",
        final_dropout: bool = False,
        inner_dim=None,
        bias: bool = True,
    ):
        super().__init__()
        if inner_dim is None:
            inner_dim = int(dim * mult)
        dim_out = dim_out if dim_out is not None else dim

        if activation_fn == "gelu":
            act_fn = GELU(dim, inner_dim, bias=bias)
        if activation_fn == "gelu-approximate":
            act_fn = GELU(dim, inner_dim, approximate="tanh", bias=bias)
        elif activation_fn == "geglu":
            act_fn = GEGLU(dim, inner_dim, bias=bias)
        elif activation_fn == "geglu-approximate":
            act_fn = ApproximateGELU(dim, inner_dim, bias=bias)
        elif activation_fn == "swiglu":
            act_fn = SwiGLU(dim, inner_dim, bias=bias)
        elif activation_fn == "linear-silu":
            act_fn = LinearActivation(dim, inner_dim, bias=bias, activation="silu")

        self.net = nn.ModuleList([])
        # project in
        self.net.append(act_fn)
        # project dropout
        self.net.append(nn.Dropout(dropout))
        # project out
        self.net.append(nn.Linear(inner_dim, dim_out, bias=bias))
        # FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
        if final_dropout:
            self.net.append(nn.Dropout(dropout))

    def forward(self, hidden_states: torch.Tensor, *args, **kwargs) -> torch.Tensor:
        if len(args) > 0 or kwargs.get("scale", None) is not None:
            deprecation_message = "The `scale` argument is deprecated and will be ignored. Please remove it, as passing it will raise an error in the future. `scale` should directly be passed while calling the underlying pipeline component i.e., via `cross_attention_kwargs`."
            deprecate("scale", "1.0.0", deprecation_message)
        for module in self.net:
            hidden_states = module(hidden_states)
        return hidden_states