controlnet/pixcell_controlnet.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

import torch
from torch import nn

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils import is_torch_version, logging
from diffusers.models.attention import BasicTransformerBlock
from diffusers.models.attention_processor import Attention, AttentionProcessor, AttnProcessor, FusedAttnProcessor2_0
from diffusers.models.embeddings import PatchEmbed
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.normalization import AdaLayerNormSingle
from diffusers.models.activations import deprecate, FP32SiLU

from diffusers.models.controlnet import zero_module
from diffusers.models.embeddings import PatchEmbed
from dataclasses import dataclass

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


# PixCell UNI conditioning
def pixcell_get_2d_sincos_pos_embed(
    embed_dim,
    grid_size,
    cls_token=False,
    extra_tokens=0,
    interpolation_scale=1.0,
    base_size=16,
    device: Optional[torch.device] = None,
    phase=0,
    output_type: str = "np",
):
    """
    Creates 2D sinusoidal positional embeddings.

    Args:
        embed_dim (`int`):
            The embedding dimension.
        grid_size (`int`):
            The size of the grid height and width.
        cls_token (`bool`, defaults to `False`):
            Whether or not to add a classification token.
        extra_tokens (`int`, defaults to `0`):
            The number of extra tokens to add.
        interpolation_scale (`float`, defaults to `1.0`):
            The scale of the interpolation.

    Returns:
        pos_embed (`torch.Tensor`):
            Shape is either `[grid_size * grid_size, embed_dim]` if not using cls_token, or `[1 + grid_size*grid_size,
            embed_dim]` if using cls_token
    """
    if output_type == "np":
        deprecation_message = (
            "`get_2d_sincos_pos_embed` uses `torch` and supports `device`."
            " `from_numpy` is no longer required."
            "  Pass `output_type='pt' to use the new version now."
        )
        deprecate("output_type=='np'", "0.33.0", deprecation_message, standard_warn=False)
        raise ValueError("Not supported")
    if isinstance(grid_size, int):
        grid_size = (grid_size, grid_size)

    grid_h = (
        torch.arange(grid_size[0], device=device, dtype=torch.float32)
        / (grid_size[0] / base_size)
        / interpolation_scale
    )
    grid_w = (
        torch.arange(grid_size[1], device=device, dtype=torch.float32)
        / (grid_size[1] / base_size)
        / interpolation_scale
    )
    grid = torch.meshgrid(grid_w, grid_h, indexing="xy")  # here w goes first
    grid = torch.stack(grid, dim=0)

    grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
    pos_embed = pixcell_get_2d_sincos_pos_embed_from_grid(embed_dim, grid, phase=phase, output_type=output_type)
    if cls_token and extra_tokens > 0:
        pos_embed = torch.concat([torch.zeros([extra_tokens, embed_dim]), pos_embed], dim=0)
    return pos_embed


def pixcell_get_2d_sincos_pos_embed_from_grid(embed_dim, grid, phase=0, output_type="np"):
    r"""
    This function generates 2D sinusoidal positional embeddings from a grid.

    Args:
        embed_dim (`int`): The embedding dimension.
        grid (`torch.Tensor`): Grid of positions with shape `(H * W,)`.

    Returns:
        `torch.Tensor`: The 2D sinusoidal positional embeddings with shape `(H * W, embed_dim)`
    """
    if output_type == "np":
        deprecation_message = (
            "`get_2d_sincos_pos_embed_from_grid` uses `torch` and supports `device`."
            " `from_numpy` is no longer required."
            "  Pass `output_type='pt' to use the new version now."
        )
        deprecate("output_type=='np'", "0.33.0", deprecation_message, standard_warn=False)
        raise ValueError("Not supported")
    if embed_dim % 2 != 0:
        raise ValueError("embed_dim must be divisible by 2")

    # use half of dimensions to encode grid_h
    emb_h = pixcell_get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0], phase=phase, output_type=output_type)  # (H*W, D/2)
    emb_w = pixcell_get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1], phase=phase, output_type=output_type)  # (H*W, D/2)

    emb = torch.concat([emb_h, emb_w], dim=1)  # (H*W, D)
    return emb


def pixcell_get_1d_sincos_pos_embed_from_grid(embed_dim, pos, phase=0, output_type="np"):
    """
    This function generates 1D positional embeddings from a grid.

    Args:
        embed_dim (`int`): The embedding dimension `D`
        pos (`torch.Tensor`): 1D tensor of positions with shape `(M,)`

    Returns:
        `torch.Tensor`: Sinusoidal positional embeddings of shape `(M, D)`.
    """
    if output_type == "np":
        deprecation_message = (
            "`get_1d_sincos_pos_embed_from_grid` uses `torch` and supports `device`."
            " `from_numpy` is no longer required."
            "  Pass `output_type='pt' to use the new version now."
        )
        deprecate("output_type=='np'", "0.34.0", deprecation_message, standard_warn=False)
        raise ValueError("Not supported")
    if embed_dim % 2 != 0:
        raise ValueError("embed_dim must be divisible by 2")

    omega = torch.arange(embed_dim // 2, device=pos.device, dtype=torch.float64)
    omega /= embed_dim / 2.0
    omega = 1.0 / 10000**omega  # (D/2,)

    pos = pos.reshape(-1) + phase  # (M,)
    out = torch.outer(pos, omega)  # (M, D/2), outer product

    emb_sin = torch.sin(out)  # (M, D/2)
    emb_cos = torch.cos(out)  # (M, D/2)

    emb = torch.concat([emb_sin, emb_cos], dim=1)  # (M, D)
    return emb


class PixcellUNIProjection(nn.Module):
    """
    Projects UNI embeddings. Also handles dropout for classifier-free guidance.

    Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
    """

    def __init__(self, in_features, hidden_size, out_features=None, act_fn="gelu_tanh", num_tokens=1):
        super().__init__()
        if out_features is None:
            out_features = hidden_size
        self.linear_1 = nn.Linear(in_features=in_features, out_features=hidden_size, bias=True)
        if act_fn == "gelu_tanh":
            self.act_1 = nn.GELU(approximate="tanh")
        elif act_fn == "silu":
            self.act_1 = nn.SiLU()
        elif act_fn == "silu_fp32":
            self.act_1 = FP32SiLU()
        else:
            raise ValueError(f"Unknown activation function: {act_fn}")
        self.linear_2 = nn.Linear(in_features=hidden_size, out_features=out_features, bias=True)

        self.register_buffer("uncond_embedding", nn.Parameter(torch.randn(num_tokens, in_features) / in_features ** 0.5))

    def forward(self, caption):
        hidden_states = self.linear_1(caption)
        hidden_states = self.act_1(hidden_states)
        hidden_states = self.linear_2(hidden_states)
        return hidden_states

class UNIPosEmbed(nn.Module):
    """
    Adds positional embeddings to the UNI conditions.

    Args:
        height (`int`, defaults to `224`): The height of the image.
        width (`int`, defaults to `224`): The width of the image.
        patch_size (`int`, defaults to `16`): The size of the patches.
        in_channels (`int`, defaults to `3`): The number of input channels.
        embed_dim (`int`, defaults to `768`): The output dimension of the embedding.
        layer_norm (`bool`, defaults to `False`): Whether or not to use layer normalization.
        flatten (`bool`, defaults to `True`): Whether or not to flatten the output.
        bias (`bool`, defaults to `True`): Whether or not to use bias.
        interpolation_scale (`float`, defaults to `1`): The scale of the interpolation.
        pos_embed_type (`str`, defaults to `"sincos"`): The type of positional embedding.
        pos_embed_max_size (`int`, defaults to `None`): The maximum size of the positional embedding.
    """

    def __init__(
        self,
        height=1,
        width=1,
        base_size=16,
        embed_dim=768,
        interpolation_scale=1,
        pos_embed_type="sincos",
    ):
        super().__init__()

        num_embeds = height*width
        grid_size = int(num_embeds ** 0.5)

        if pos_embed_type == "sincos":
            y_pos_embed = pixcell_get_2d_sincos_pos_embed(
                embed_dim,
                grid_size,
                base_size=base_size,
                interpolation_scale=interpolation_scale,
                output_type="pt",
                phase = base_size // num_embeds
            )
            self.register_buffer("y_pos_embed", y_pos_embed.float().unsqueeze(0))
        else:
            raise ValueError("`pos_embed_type` not supported")

    def forward(self, uni_embeds):
        return (uni_embeds + self.y_pos_embed).to(uni_embeds.dtype)

from diffusers.utils import BaseOutput, is_torch_version
@dataclass
class PixCellControlNetOutput(BaseOutput):
    controlnet_block_samples: Tuple[torch.Tensor]

class PixCellControlNet(ModelMixin, ConfigMixin):
    r"""
    A 2D Transformer ControlNet model as introduced in PixArt family of models (https://arxiv.org/abs/2310.00426,
    https://arxiv.org/abs/2403.04692). Modified for the pathology domain.

    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,
        caption_num_tokens: int = 1,
        attention_type: Optional[str] = "default",
        n_controlnet_blocks: Optional[int] = 28,
    ):
        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)
            ]
        )

        # Initialize the positional embedding for the conditions for >1 UNI embeddings 
        if self.config.caption_num_tokens == 1:
            self.y_pos_embed = None
        else:
            # 1:1 aspect ratio
            self.uni_height = int(self.config.caption_num_tokens ** 0.5)
            self.uni_width = int(self.config.caption_num_tokens ** 0.5)

            self.y_pos_embed = UNIPosEmbed(
                height=self.uni_height,
                width=self.uni_width,
                base_size=self.config.sample_size // self.config.patch_size,
                embed_dim=self.config.caption_channels,
                interpolation_scale=2, # Should this be fixed?
                pos_embed_type="sincos", # This is fixed
            )

        # 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 = PixcellUNIProjection(
                in_features=self.config.caption_channels, hidden_size=self.inner_dim, num_tokens=self.config.caption_num_tokens,
            )


        # 4. ControlNet blocks
        # Condition patch embedding
        self.cond_pos_embed = zero_module(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,
        ))
        # Can use a subset of the transformer blocks for ControLNet
        self.n_controlnet_blocks = n_controlnet_blocks
        if self.n_controlnet_blocks is not None:
            self.transformer_blocks = self.transformer_blocks[:self.n_controlnet_blocks]

        # ControlNet layers
        self.controlnet_blocks = nn.ModuleList([])
        for i in range(len(self.transformer_blocks)):
            controlnet_block = nn.Linear(self.inner_dim, self.inner_dim)
            controlnet_block = zero_module(controlnet_block)
            self.controlnet_blocks.append(controlnet_block)

            if self.n_controlnet_blocks is not None:
                if i+1 == self.n_controlnet_blocks:
                    break
        


    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,
        conditioning: torch.Tensor,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        timestep: Optional[torch.LongTensor] = None,
        conditioning_scale: float = 1.0,
        added_cond_kwargs: Dict[str, torch.Tensor] = None,
        cross_attention_kwargs: Dict[str, Any] = None,
        attention_mask: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        return_dict: bool = True,
    ):
        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`.")

        # 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)

        # 1. Input
        batch_size = hidden_states.shape[0]
        height, width = (
            hidden_states.shape[-2] // self.config.patch_size,
            hidden_states.shape[-1] // self.config.patch_size,
        )
        hidden_states = self.pos_embed(hidden_states)

        # Conditioning
        hidden_states = hidden_states + self.cond_pos_embed(conditioning)

        timestep, embedded_timestep = self.adaln_single(
            timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype
        )

        if self.caption_projection is not None:
            # Add positional embeddings to conditions if >1 UNI are given
            if self.y_pos_embed is not None:
                encoder_hidden_states = self.y_pos_embed(encoder_hidden_states)
            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
        block_outputs = ()

        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,
                    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,
                    timestep=timestep,
                    cross_attention_kwargs=cross_attention_kwargs,
                    class_labels=None,
                )
            
            block_outputs = block_outputs + (hidden_states,)

        # 3. controlnet blocks
        controlnet_outputs = ()
        for t_output, controlnet_block in zip(block_outputs, self.controlnet_blocks):
            b_output = controlnet_block(t_output)
            controlnet_outputs = controlnet_outputs + (b_output,)

        controlnet_outputs = [sample * conditioning_scale for sample in controlnet_outputs]

        if not return_dict:
            return (controlnet_outputs,)

        return PixCellControlNetOutput(controlnet_block_samples=controlnet_outputs)