src/models/stable_diffusion3/transformer_sd3_dynamic.py

# Copyright 2024 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 math
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from torch.nn.utils.rnn import pad_sequence
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin, SD3Transformer2DLoadersMixin
from diffusers.models.attention import FeedForward, JointTransformerBlock, _chunked_feed_forward
from diffusers.models.attention_processor import (
    Attention,
    AttentionProcessor,
    FusedJointAttnProcessor2_0,
    JointAttnProcessor2_0,
)
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import AdaLayerNormContinuous, AdaLayerNormZero
from diffusers.utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
from diffusers.utils.torch_utils import maybe_allow_in_graph
from diffusers.models.embeddings import CombinedTimestepTextProjEmbeddings, PatchEmbed
from diffusers.models.modeling_outputs import Transformer2DModelOutput


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


class CustomJointAttnProcessor2_0:
    """Attention processor used typically in processing the SD3-like self-attention projections."""

    def __init__(self):
        if not hasattr(F, "scaled_dot_product_attention"):
            raise ImportError("JointAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")

    def __call__(
        self,
        attn: Attention,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: torch.FloatTensor = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        *args,
        **kwargs,
    ) -> torch.FloatTensor:
        residual = hidden_states

        batch_size = hidden_states.shape[0]

        # `sample` projections.
        query = attn.to_q(hidden_states)
        key = attn.to_k(hidden_states)
        value = attn.to_v(hidden_states)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        if attn.norm_q is not None:
            query = attn.norm_q(query)
        if attn.norm_k is not None:
            key = attn.norm_k(key)

        # `context` projections.
        if encoder_hidden_states is not None:
            ctx_len = encoder_hidden_states.shape[1]
            encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
            encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
            encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)

            encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
                batch_size, -1, attn.heads, head_dim
            ).transpose(1, 2)
            encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
                batch_size, -1, attn.heads, head_dim
            ).transpose(1, 2)
            encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
                batch_size, -1, attn.heads, head_dim
            ).transpose(1, 2)

            if attn.norm_added_q is not None:
                encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
            if attn.norm_added_k is not None:
                encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)

            query = torch.cat([query, encoder_hidden_states_query_proj], dim=2)
            key = torch.cat([key, encoder_hidden_states_key_proj], dim=2)
            value = torch.cat([value, encoder_hidden_states_value_proj], dim=2)

            if attention_mask is not None:
                # import pdb; pdb.set_trace()
                encoder_attention_mask = torch.ones(
                    batch_size, ctx_len, dtype=torch.bool, device=hidden_states.device)
                attention_mask = torch.cat([attention_mask, encoder_attention_mask], dim=1)


        # import pdb; pdb.set_trace()
        if attention_mask is not None:
            attention_mask = attention_mask[:, None] * attention_mask[..., None]   # bsz, seqlen, seqlen
            indices = range(attention_mask.shape[1])
            attention_mask[:, indices, indices] = True
            attention_mask = attention_mask[:, None]

        hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False,
                                                       attn_mask=attention_mask)
        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)

        if encoder_hidden_states is not None:
            # import pdb; pdb.set_trace()
            # Split the attention outputs.
            hidden_states, encoder_hidden_states = (
                hidden_states[:, : residual.shape[1]],
                hidden_states[:, residual.shape[1] :],
            )
            if not attn.context_pre_only:
                encoder_hidden_states = attn.to_add_out(encoder_hidden_states)

        # linear proj
        hidden_states = attn.to_out[0](hidden_states)
        # dropout
        hidden_states = attn.to_out[1](hidden_states)

        if encoder_hidden_states is not None:
            return hidden_states, encoder_hidden_states
        else:
            return hidden_states


class CustomJointTransformerBlock(JointTransformerBlock):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.attn.set_processor(CustomJointAttnProcessor2_0())
        if self.attn2 is not None:
            self.attn2.set_processor(CustomJointAttnProcessor2_0())

    def forward(
        self,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: torch.FloatTensor,
        temb: torch.FloatTensor,
        attention_mask: Optional[torch.BoolTensor] = None,
        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
    ):
        joint_attention_kwargs = joint_attention_kwargs or {}
        if self.use_dual_attention:
            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp, norm_hidden_states2, gate_msa2 = self.norm1(
                hidden_states, emb=temb
            )
        else:
            norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(hidden_states, emb=temb)

        if self.context_pre_only:
            norm_encoder_hidden_states = self.norm1_context(encoder_hidden_states, temb)
        else:
            norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = self.norm1_context(
                encoder_hidden_states, emb=temb
            )

        # Attention.
        attn_output, context_attn_output = self.attn(
            hidden_states=norm_hidden_states,
            attention_mask=attention_mask,
            encoder_hidden_states=norm_encoder_hidden_states,
            **joint_attention_kwargs,
        )

        # Process attention outputs for the `hidden_states`.
        attn_output = gate_msa.unsqueeze(1) * attn_output
        hidden_states = hidden_states + attn_output

        if self.use_dual_attention:
            attn_output2 = self.attn2(hidden_states=norm_hidden_states2, attention_mask=attention_mask,
                                      **joint_attention_kwargs)
            attn_output2 = gate_msa2.unsqueeze(1) * attn_output2
            hidden_states = hidden_states + attn_output2

        norm_hidden_states = self.norm2(hidden_states)
        norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
        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.unsqueeze(1) * ff_output

        hidden_states = hidden_states + ff_output

        # Process attention outputs for the `encoder_hidden_states`.
        if self.context_pre_only:
            encoder_hidden_states = None
        else:
            context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output
            encoder_hidden_states = encoder_hidden_states + context_attn_output

            norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
            norm_encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_mlp[:, None]) + c_shift_mlp[:, None]
            if self._chunk_size is not None:
                # "feed_forward_chunk_size" can be used to save memory
                context_ff_output = _chunked_feed_forward(
                    self.ff_context, norm_encoder_hidden_states, self._chunk_dim, self._chunk_size
                )
            else:
                context_ff_output = self.ff_context(norm_encoder_hidden_states)
            encoder_hidden_states = encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output

        return encoder_hidden_states, hidden_states


@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", "CustomJointTransformerBlock"]
    _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(
            [
                CustomJointTransformerBlock(
                    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,
        cond_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."
                )

        latent_sizes = [hs.shape[-2:] for hs in hidden_states]
        bsz = len(hidden_states)

        hidden_states_list = []
        for idx in range(bsz):
            hidden_states_per_sample = self.pos_embed(hidden_states[idx][None])[0]
            if cond_hidden_states is not None:
                for ref in cond_hidden_states[idx]:
                    hidden_states_per_sample = torch.cat(
                        [hidden_states_per_sample, self.pos_embed(ref[None])[0]])

            hidden_states_list.append(hidden_states_per_sample)

        max_len = max([len(hs) for hs in hidden_states_list])
        attention_mask = torch.zeros(bsz, max_len, dtype=torch.bool, device=self.device)
        for i, hs in enumerate(hidden_states_list):
            attention_mask[i, :len(hs)] = True  # right padding
        # import pdb; pdb.set_trace()
        hidden_states = pad_sequence(hidden_states_list, batch_first=True, padding_value=0.0, padding_side='right')

        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,
                    attention_mask,
                    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,
                    attention_mask=attention_mask,
                    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)

        patch_size = self.config.patch_size
        latent_sizes = [(latent_size[0] // patch_size, latent_size[1] // patch_size)
                        for latent_size in latent_sizes]

        # import pdb; pdb.set_trace()
        # unpatchify
        output = [rearrange(hs[:math.prod(latent_size)], '(h w) (p q c) -> c (h p) (w q)',
                            h=latent_size[0], w=latent_size[1], p=patch_size, q=patch_size)
                  for hs, latent_size in zip(hidden_states, latent_sizes)]

        try:
            output = torch.stack(output)    # can be staked if all have the save shape
        except:
            # cannot be stacked
            pass

        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)