Upload pixcell_controlnet.py

pixcell_controlnet.py

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+
+# 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)
+