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# Copyright 2025 Stability AI, The HuggingFace Team and The InstantX 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, List, Optional, Tuple, Union

import torch
import torch.nn as nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...loaders import FromOriginalModelMixin, PeftAdapterMixin, SD3Transformer2DLoadersMixin
from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
from ...utils.torch_utils import maybe_allow_in_graph
from ..attention import FeedForward, JointTransformerBlock
from ..attention_processor import (
    Attention,
    AttentionProcessor,
    FusedJointAttnProcessor2_0,
    JointAttnProcessor2_0,
)
from ..embeddings import CombinedTimestepTextProjEmbeddings, PatchEmbed
from ..modeling_outputs import Transformer2DModelOutput
from ..modeling_utils import ModelMixin
from ..normalization import AdaLayerNormContinuous, AdaLayerNormZero


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


@maybe_allow_in_graph
class SD3SingleTransformerBlock(nn.Module):
    def __init__(
        self,
        dim: int,
        num_attention_heads: int,
        attention_head_dim: int,
    ):
        super().__init__()

        self.norm1 = AdaLayerNormZero(dim)
        self.attn = Attention(
            query_dim=dim,
            dim_head=attention_head_dim,
            heads=num_attention_heads,
            out_dim=dim,
            bias=True,
            processor=JointAttnProcessor2_0(),
            eps=1e-6,
        )

        self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
        self.ff = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")

    def forward(self, hidden_states: torch.Tensor, temb: torch.Tensor):
        # 1. Attention
        norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
        attn_output = self.attn(hidden_states=norm_hidden_states, encoder_hidden_states=None)
        attn_output = gate_msa.unsqueeze(1) * attn_output
        hidden_states = hidden_states + attn_output

        # 2. Feed Forward
        norm_hidden_states = self.norm2(hidden_states)
        norm_hidden_states = norm_hidden_states * (1 + scale_mlp.unsqueeze(1)) + shift_mlp.unsqueeze(1)
        ff_output = self.ff(norm_hidden_states)
        ff_output = gate_mlp.unsqueeze(1) * ff_output
        hidden_states = hidden_states + ff_output

        return hidden_states


class SD3Transformer2DModel(
    ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, SD3Transformer2DLoadersMixin
):
    """
    The Transformer model introduced in [Stable Diffusion 3](https://huggingface.co/papers/2403.03206).
    Parameters:
        sample_size (`int`, defaults to `128`):
            The width/height of the latents. This is fixed during training since it is used to learn a number of
            position embeddings.
        patch_size (`int`, defaults to `2`):
            Patch size to turn the input data into small patches.
        in_channels (`int`, defaults to `16`):
            The number of latent channels in the input.
        num_layers (`int`, defaults to `18`):
            The number of layers of transformer blocks to use.
        attention_head_dim (`int`, defaults to `64`):
            The number of channels in each head.
        num_attention_heads (`int`, defaults to `18`):
            The number of heads to use for multi-head attention.
        joint_attention_dim (`int`, defaults to `4096`):
            The embedding dimension to use for joint text-image attention.
        caption_projection_dim (`int`, defaults to `1152`):
            The embedding dimension of caption embeddings.
        pooled_projection_dim (`int`, defaults to `2048`):
            The embedding dimension of pooled text projections.
        out_channels (`int`, defaults to `16`):
            The number of latent channels in the output.
        pos_embed_max_size (`int`, defaults to `96`):
            The maximum latent height/width of positional embeddings.
        dual_attention_layers (`Tuple[int, ...]`, defaults to `()`):
            The number of dual-stream transformer blocks to use.
        qk_norm (`str`, *optional*, defaults to `None`):
            The normalization to use for query and key in the attention layer. If `None`, no normalization is used.
    """

    _supports_gradient_checkpointing = True
    _no_split_modules = ["JointTransformerBlock"]
    _skip_layerwise_casting_patterns = ["pos_embed", "norm"]

    @register_to_config
    def __init__(
        self,
        sample_size: int = 128,
        patch_size: int = 2,
        in_channels: int = 16,
        num_layers: int = 18,
        attention_head_dim: int = 64,
        num_attention_heads: int = 18,
        joint_attention_dim: int = 4096,
        caption_projection_dim: int = 1152,
        pooled_projection_dim: int = 2048,
        out_channels: int = 16,
        pos_embed_max_size: int = 96,
        dual_attention_layers: Tuple[
            int, ...
        ] = (),  # () for sd3.0; (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) for sd3.5
        qk_norm: Optional[str] = None,
    ):
        super().__init__()
        self.out_channels = out_channels if out_channels is not None else in_channels
        self.inner_dim = num_attention_heads * attention_head_dim

        self.pos_embed = PatchEmbed(
            height=sample_size,
            width=sample_size,
            patch_size=patch_size,
            in_channels=in_channels,
            embed_dim=self.inner_dim,
            pos_embed_max_size=pos_embed_max_size,  # hard-code for now.
        )
        self.time_text_embed = CombinedTimestepTextProjEmbeddings(
            embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
        )
        self.context_embedder = nn.Linear(joint_attention_dim, caption_projection_dim)

        self.transformer_blocks = nn.ModuleList(
            [
                JointTransformerBlock(
                    dim=self.inner_dim,
                    num_attention_heads=num_attention_heads,
                    attention_head_dim=attention_head_dim,
                    context_pre_only=i == num_layers - 1,
                    qk_norm=qk_norm,
                    use_dual_attention=True if i in dual_attention_layers else False,
                )
                for i in range(num_layers)
            ]
        )

        self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)
        self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)

        self.gradient_checkpointing = False

    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
    def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
        """
        Sets the attention processor to use [feed forward
        chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).
        Parameters:
            chunk_size (`int`, *optional*):
                The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
                over each tensor of dim=`dim`.
            dim (`int`, *optional*, defaults to `0`):
                The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
                or dim=1 (sequence length).
        """
        if dim not in [0, 1]:
            raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")

        # By default chunk size is 1
        chunk_size = chunk_size or 1

        def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
            if hasattr(module, "set_chunk_feed_forward"):
                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)

            for child in module.children():
                fn_recursive_feed_forward(child, chunk_size, dim)

        for module in self.children():
            fn_recursive_feed_forward(module, chunk_size, dim)

    # Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking
    def disable_forward_chunking(self):
        def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
            if hasattr(module, "set_chunk_feed_forward"):
                module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)

            for child in module.children():
                fn_recursive_feed_forward(child, chunk_size, dim)

        for module in self.children():
            fn_recursive_feed_forward(module, None, 0)

    @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)

    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedJointAttnProcessor2_0
    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(FusedJointAttnProcessor2_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 = None,
        pooled_projections: torch.Tensor = None,
        timestep: torch.LongTensor = None,
        block_controlnet_hidden_states: List = None,
        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
        return_dict: bool = True,
        skip_layers: Optional[List[int]] = None,
    ) -> Union[torch.Tensor, Transformer2DModelOutput]:
        """
        The [`SD3Transformer2DModel`] forward method.
        Args:
            hidden_states (`torch.Tensor` of shape `(batch size, channel, height, width)`):
                Input `hidden_states`.
            encoder_hidden_states (`torch.Tensor` of shape `(batch size, sequence_len, embed_dims)`):
                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
            pooled_projections (`torch.Tensor` of shape `(batch_size, projection_dim)`):
                Embeddings projected from the embeddings of input conditions.
            timestep (`torch.LongTensor`):
                Used to indicate denoising step.
            block_controlnet_hidden_states (`list` of `torch.Tensor`):
                A list of tensors that if specified are added to the residuals of transformer blocks.
            joint_attention_kwargs (`dict`, *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).
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
                tuple.
            skip_layers (`list` of `int`, *optional*):
                A list of layer indices to skip during the forward pass.
        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 joint_attention_kwargs is not None:
            joint_attention_kwargs = joint_attention_kwargs.copy()
            lora_scale = joint_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)
        else:
            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
                logger.warning(
                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
                )

        height, width = hidden_states.shape[-2:]

        hidden_states = self.pos_embed(hidden_states)  # takes care of adding positional embeddings too.
        temb = self.time_text_embed(timestep, pooled_projections)
        encoder_hidden_states = self.context_embedder(encoder_hidden_states)

        if joint_attention_kwargs is not None and "ip_adapter_image_embeds" in joint_attention_kwargs:
            ip_adapter_image_embeds = joint_attention_kwargs.pop("ip_adapter_image_embeds")
            ip_hidden_states, ip_temb = self.image_proj(ip_adapter_image_embeds, timestep)

            joint_attention_kwargs.update(ip_hidden_states=ip_hidden_states, temb=ip_temb)

        for index_block, block in enumerate(self.transformer_blocks):
            # Skip specified layers
            is_skip = True if skip_layers is not None and index_block in skip_layers else False

            if torch.is_grad_enabled() and self.gradient_checkpointing and not is_skip:
                encoder_hidden_states, hidden_states = self._gradient_checkpointing_func(
                    block,
                    hidden_states,
                    encoder_hidden_states,
                    temb,
                    joint_attention_kwargs,
                )
            elif not is_skip:
                encoder_hidden_states, hidden_states = block(
                    hidden_states=hidden_states,
                    encoder_hidden_states=encoder_hidden_states,
                    temb=temb,
                    joint_attention_kwargs=joint_attention_kwargs,
                )

            # controlnet residual
            if block_controlnet_hidden_states is not None and block.context_pre_only is False:
                interval_control = len(self.transformer_blocks) / len(block_controlnet_hidden_states)
                hidden_states = hidden_states + block_controlnet_hidden_states[int(index_block / interval_control)]

        hidden_states = self.norm_out(hidden_states, temb)
        hidden_states = self.proj_out(hidden_states)

        # unpatchify
        patch_size = self.config.patch_size
        height = height // patch_size
        width = width // patch_size

        hidden_states = hidden_states.reshape(
            shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels)
        )
        hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
        output = hidden_states.reshape(
            shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size)
        )

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

        if not return_dict:
            return (output,)

        return Transformer2DModelOutput(sample=output)

	# Copyright 2025 Stability AI, The HuggingFace Team and The InstantX 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, List, Optional, Tuple, Union

	import torch
	import torch.nn as nn

	from ...configuration_utils import ConfigMixin, register_to_config
	from ...loaders import FromOriginalModelMixin, PeftAdapterMixin, SD3Transformer2DLoadersMixin
	from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
	from ...utils.torch_utils import maybe_allow_in_graph
	from ..attention import FeedForward, JointTransformerBlock
	from ..attention_processor import (
	Attention,
	AttentionProcessor,
	FusedJointAttnProcessor2_0,
	JointAttnProcessor2_0,
	)
	from ..embeddings import CombinedTimestepTextProjEmbeddings, PatchEmbed
	from ..modeling_outputs import Transformer2DModelOutput
	from ..modeling_utils import ModelMixin
	from ..normalization import AdaLayerNormContinuous, AdaLayerNormZero


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


	@maybe_allow_in_graph
	class SD3SingleTransformerBlock(nn.Module):
	def __init__(
	self,
	dim: int,
	num_attention_heads: int,
	attention_head_dim: int,
	):
	super().__init__()

	self.norm1 = AdaLayerNormZero(dim)
	self.attn = Attention(
	query_dim=dim,
	dim_head=attention_head_dim,
	heads=num_attention_heads,
	out_dim=dim,
	bias=True,
	processor=JointAttnProcessor2_0(),
	eps=1e-6,
	)

	self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
	self.ff = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")

	def forward(self, hidden_states: torch.Tensor, temb: torch.Tensor):
	# 1. Attention
	norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)
	attn_output = self.attn(hidden_states=norm_hidden_states, encoder_hidden_states=None)
	attn_output = gate_msa.unsqueeze(1) * attn_output
	hidden_states = hidden_states + attn_output

	# 2. Feed Forward
	norm_hidden_states = self.norm2(hidden_states)
	norm_hidden_states = norm_hidden_states * (1 + scale_mlp.unsqueeze(1)) + shift_mlp.unsqueeze(1)
	ff_output = self.ff(norm_hidden_states)
	ff_output = gate_mlp.unsqueeze(1) * ff_output
	hidden_states = hidden_states + ff_output

	return hidden_states


	class SD3Transformer2DModel(
	ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, SD3Transformer2DLoadersMixin
	):
	"""
	The Transformer model introduced in [Stable Diffusion 3](https://huggingface.co/papers/2403.03206).

	Parameters:
	sample_size (`int`, defaults to `128`):
	The width/height of the latents. This is fixed during training since it is used to learn a number of
	position embeddings.
	patch_size (`int`, defaults to `2`):
	Patch size to turn the input data into small patches.
	in_channels (`int`, defaults to `16`):
	The number of latent channels in the input.
	num_layers (`int`, defaults to `18`):
	The number of layers of transformer blocks to use.
	attention_head_dim (`int`, defaults to `64`):
	The number of channels in each head.
	num_attention_heads (`int`, defaults to `18`):
	The number of heads to use for multi-head attention.
	joint_attention_dim (`int`, defaults to `4096`):
	The embedding dimension to use for joint text-image attention.
	caption_projection_dim (`int`, defaults to `1152`):
	The embedding dimension of caption embeddings.
	pooled_projection_dim (`int`, defaults to `2048`):
	The embedding dimension of pooled text projections.
	out_channels (`int`, defaults to `16`):
	The number of latent channels in the output.
	pos_embed_max_size (`int`, defaults to `96`):
	The maximum latent height/width of positional embeddings.
	dual_attention_layers (`Tuple[int, ...]`, defaults to `()`):
	The number of dual-stream transformer blocks to use.
	qk_norm (`str`, optional, defaults to `None`):
	The normalization to use for query and key in the attention layer. If `None`, no normalization is used.
	"""

	_supports_gradient_checkpointing = True
	_no_split_modules = ["JointTransformerBlock"]
	_skip_layerwise_casting_patterns = ["pos_embed", "norm"]

	@register_to_config
	def __init__(
	self,
	sample_size: int = 128,
	patch_size: int = 2,
	in_channels: int = 16,
	num_layers: int = 18,
	attention_head_dim: int = 64,
	num_attention_heads: int = 18,
	joint_attention_dim: int = 4096,
	caption_projection_dim: int = 1152,
	pooled_projection_dim: int = 2048,
	out_channels: int = 16,
	pos_embed_max_size: int = 96,
	dual_attention_layers: Tuple[
	int, ...
	] = (), # () for sd3.0; (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) for sd3.5
	qk_norm: Optional[str] = None,
	):
	super().__init__()
	self.out_channels = out_channels if out_channels is not None else in_channels
	self.inner_dim = num_attention_heads * attention_head_dim

	self.pos_embed = PatchEmbed(
	height=sample_size,
	width=sample_size,
	patch_size=patch_size,
	in_channels=in_channels,
	embed_dim=self.inner_dim,
	pos_embed_max_size=pos_embed_max_size, # hard-code for now.
	)
	self.time_text_embed = CombinedTimestepTextProjEmbeddings(
	embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
	)
	self.context_embedder = nn.Linear(joint_attention_dim, caption_projection_dim)

	self.transformer_blocks = nn.ModuleList(
	[
	JointTransformerBlock(
	dim=self.inner_dim,
	num_attention_heads=num_attention_heads,
	attention_head_dim=attention_head_dim,
	context_pre_only=i == num_layers - 1,
	qk_norm=qk_norm,
	use_dual_attention=True if i in dual_attention_layers else False,
	)
	for i in range(num_layers)
	]
	)

	self.norm_out = AdaLayerNormContinuous(self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6)
	self.proj_out = nn.Linear(self.inner_dim, patch_size * patch_size * self.out_channels, bias=True)

	self.gradient_checkpointing = False

	# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
	def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
	"""
	Sets the attention processor to use [feed forward
	chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).

	Parameters:
	chunk_size (`int`, optional):
	The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
	over each tensor of dim=`dim`.
	dim (`int`, optional, defaults to `0`):
	The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
	or dim=1 (sequence length).
	"""
	if dim not in [0, 1]:
	raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")

	# By default chunk size is 1
	chunk_size = chunk_size or 1

	def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
	if hasattr(module, "set_chunk_feed_forward"):
	module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)

	for child in module.children():
	fn_recursive_feed_forward(child, chunk_size, dim)

	for module in self.children():
	fn_recursive_feed_forward(module, chunk_size, dim)

	# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking
	def disable_forward_chunking(self):
	def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
	if hasattr(module, "set_chunk_feed_forward"):
	module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)

	for child in module.children():
	fn_recursive_feed_forward(child, chunk_size, dim)

	for module in self.children():
	fn_recursive_feed_forward(module, None, 0)

	@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)

	# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections with FusedAttnProcessor2_0->FusedJointAttnProcessor2_0
	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(FusedJointAttnProcessor2_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 = None,
	pooled_projections: torch.Tensor = None,
	timestep: torch.LongTensor = None,
	block_controlnet_hidden_states: List = None,
	joint_attention_kwargs: Optional[Dict[str, Any]] = None,
	return_dict: bool = True,
	skip_layers: Optional[List[int]] = None,
	) -> Union[torch.Tensor, Transformer2DModelOutput]:
	"""
	The [`SD3Transformer2DModel`] forward method.

	Args:
	hidden_states (`torch.Tensor` of shape `(batch size, channel, height, width)`):
	Input `hidden_states`.
	encoder_hidden_states (`torch.Tensor` of shape `(batch size, sequence_len, embed_dims)`):
	Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
	pooled_projections (`torch.Tensor` of shape `(batch_size, projection_dim)`):
	Embeddings projected from the embeddings of input conditions.
	timestep (`torch.LongTensor`):
	Used to indicate denoising step.
	block_controlnet_hidden_states (`list` of `torch.Tensor`):
	A list of tensors that if specified are added to the residuals of transformer blocks.
	joint_attention_kwargs (`dict`, 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).
	return_dict (`bool`, optional, defaults to `True`):
	Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
	tuple.
	skip_layers (`list` of `int`, optional):
	A list of layer indices to skip during the forward pass.

	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 joint_attention_kwargs is not None:
	joint_attention_kwargs = joint_attention_kwargs.copy()
	lora_scale = joint_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)
	else:
	if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
	logger.warning(
	"Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
	)

	height, width = hidden_states.shape[-2:]

	hidden_states = self.pos_embed(hidden_states) # takes care of adding positional embeddings too.
	temb = self.time_text_embed(timestep, pooled_projections)
	encoder_hidden_states = self.context_embedder(encoder_hidden_states)

	if joint_attention_kwargs is not None and "ip_adapter_image_embeds" in joint_attention_kwargs:
	ip_adapter_image_embeds = joint_attention_kwargs.pop("ip_adapter_image_embeds")
	ip_hidden_states, ip_temb = self.image_proj(ip_adapter_image_embeds, timestep)

	joint_attention_kwargs.update(ip_hidden_states=ip_hidden_states, temb=ip_temb)

	for index_block, block in enumerate(self.transformer_blocks):
	# Skip specified layers
	is_skip = True if skip_layers is not None and index_block in skip_layers else False

	if torch.is_grad_enabled() and self.gradient_checkpointing and not is_skip:
	encoder_hidden_states, hidden_states = self._gradient_checkpointing_func(
	block,
	hidden_states,
	encoder_hidden_states,
	temb,
	joint_attention_kwargs,
	)
	elif not is_skip:
	encoder_hidden_states, hidden_states = block(
	hidden_states=hidden_states,
	encoder_hidden_states=encoder_hidden_states,
	temb=temb,
	joint_attention_kwargs=joint_attention_kwargs,
	)

	# controlnet residual
	if block_controlnet_hidden_states is not None and block.context_pre_only is False:
	interval_control = len(self.transformer_blocks) / len(block_controlnet_hidden_states)
	hidden_states = hidden_states + block_controlnet_hidden_states[int(index_block / interval_control)]

	hidden_states = self.norm_out(hidden_states, temb)
	hidden_states = self.proj_out(hidden_states)

	# unpatchify
	patch_size = self.config.patch_size
	height = height // patch_size
	width = width // patch_size

	hidden_states = hidden_states.reshape(
	shape=(hidden_states.shape[0], height, width, patch_size, patch_size, self.out_channels)
	)
	hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
	output = hidden_states.reshape(
	shape=(hidden_states.shape[0], self.out_channels, height * patch_size, width * patch_size)
	)

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

	if not return_dict:
	return (output,)

	return Transformer2DModelOutput(sample=output)