magic-animate

magicanimate/models/appearance_encoder.py

+# *************************************************************************
+# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
+# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
+# ytedance Inc..  
+# *************************************************************************
+
+# Copyright 2023 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 dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import UNet2DConditionLoadersMixin
+from diffusers.utils import BaseOutput, logging
+from diffusers.models.activations import get_activation
+from diffusers.models.attention_processor import (
+    ADDED_KV_ATTENTION_PROCESSORS,
+    CROSS_ATTENTION_PROCESSORS,
+    AttentionProcessor,
+    AttnAddedKVProcessor,
+    AttnProcessor,
+)
+from diffusers.models.lora import LoRALinearLayer
+from diffusers.models.embeddings import (
+    GaussianFourierProjection,
+    ImageHintTimeEmbedding,
+    ImageProjection,
+    ImageTimeEmbedding,
+    PositionNet,
+    TextImageProjection,
+    TextImageTimeEmbedding,
+    TextTimeEmbedding,
+    TimestepEmbedding,
+    Timesteps,
+)
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.unet_2d_blocks import (
+    UNetMidBlock2DCrossAttn,
+    UNetMidBlock2DSimpleCrossAttn,
+    get_down_block,
+    get_up_block,
+)
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class Identity(torch.nn.Module):
+    r"""A placeholder identity operator that is argument-insensitive.
+
+    Args:
+        args: any argument (unused)
+        kwargs: any keyword argument (unused)
+
+    Shape:
+        - Input: :math:`(*)`, where :math:`*` means any number of dimensions.
+        - Output: :math:`(*)`, same shape as the input.
+
+    Examples::
+
+        >>> m = nn.Identity(54, unused_argument1=0.1, unused_argument2=False)
+        >>> input = torch.randn(128, 20)
+        >>> output = m(input)
+        >>> print(output.size())
+        torch.Size([128, 20])
+
+    """
+    def __init__(self, scale=None, *args, **kwargs) -> None:
+        super(Identity, self).__init__()
+
+    def forward(self, input, *args, **kwargs):
+        return input
+
+
+
+class _LoRACompatibleLinear(nn.Module):
+    """
+    A Linear layer that can be used with LoRA.
+    """
+
+    def __init__(self, *args, lora_layer: Optional[LoRALinearLayer] = None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.lora_layer = lora_layer
+
+    def set_lora_layer(self, lora_layer: Optional[LoRALinearLayer]):
+        self.lora_layer = lora_layer
+
+    def _fuse_lora(self):
+        pass
+
+    def _unfuse_lora(self):
+        pass
+
+    def forward(self, hidden_states, scale=None, lora_scale: int = 1):
+        return hidden_states
+
+
+@dataclass
+class UNet2DConditionOutput(BaseOutput):
+    """
+    The output of [`UNet2DConditionModel`].
+
+    Args:
+        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
+            The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model.
+    """
+
+    sample: torch.FloatTensor = None
+
+
+class AppearanceEncoderModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
+    r"""
+    A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
+    shaped output.
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+    for all models (such as downloading or saving).
+
+    Parameters:
+        sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
+            Height and width of input/output sample.
+        in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample.
+        out_channels (`int`, *optional*, defaults to 4): Number of channels in the output.
+        center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample.
+        flip_sin_to_cos (`bool`, *optional*, defaults to `False`):
+            Whether to flip the sin to cos in the time embedding.
+        freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding.
+        down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
+            The tuple of downsample blocks to use.
+        mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`):
+            Block type for middle of UNet, it can be either `UNetMidBlock2DCrossAttn` or
+            `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped.
+        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
+            The tuple of upsample blocks to use.
+        only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`):
+            Whether to include self-attention in the basic transformer blocks, see
+            [`~models.attention.BasicTransformerBlock`].
+        block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
+            The tuple of output channels for each block.
+        layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
+        downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution.
+        mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block.
+        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
+        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
+            If `None`, normalization and activation layers is skipped in post-processing.
+        norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization.
+        cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280):
+            The dimension of the cross attention features.
+        transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1):
+            The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for
+            [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`],
+            [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`].
+        encoder_hid_dim (`int`, *optional*, defaults to None):
+            If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim`
+            dimension to `cross_attention_dim`.
+        encoder_hid_dim_type (`str`, *optional*, defaults to `None`):
+            If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text
+            embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`.
+        attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads.
+        num_attention_heads (`int`, *optional*):
+            The number of attention heads. If not defined, defaults to `attention_head_dim`
+        resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config
+            for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`.
+        class_embed_type (`str`, *optional*, defaults to `None`):
+            The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`,
+            `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`.
+        addition_embed_type (`str`, *optional*, defaults to `None`):
+            Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or
+            "text". "text" will use the `TextTimeEmbedding` layer.
+        addition_time_embed_dim: (`int`, *optional*, defaults to `None`):
+            Dimension for the timestep embeddings.
+        num_class_embeds (`int`, *optional*, defaults to `None`):
+            Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing
+            class conditioning with `class_embed_type` equal to `None`.
+        time_embedding_type (`str`, *optional*, defaults to `positional`):
+            The type of position embedding to use for timesteps. Choose from `positional` or `fourier`.
+        time_embedding_dim (`int`, *optional*, defaults to `None`):
+            An optional override for the dimension of the projected time embedding.
+        time_embedding_act_fn (`str`, *optional*, defaults to `None`):
+            Optional activation function to use only once on the time embeddings before they are passed to the rest of
+            the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`.
+        timestep_post_act (`str`, *optional*, defaults to `None`):
+            The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`.
+        time_cond_proj_dim (`int`, *optional*, defaults to `None`):
+            The dimension of `cond_proj` layer in the timestep embedding.
+        conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer.
+        conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer.
+        projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when
+            `class_embed_type="projection"`. Required when `class_embed_type="projection"`.
+        class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time
+            embeddings with the class embeddings.
+        mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`):
+            Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If
+            `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the
+            `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False`
+            otherwise.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        sample_size: Optional[int] = None,
+        in_channels: int = 4,
+        out_channels: int = 4,
+        center_input_sample: bool = False,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,
+        down_block_types: Tuple[str] = (
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "DownBlock2D",
+        ),
+        mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn",
+        up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"),
+        only_cross_attention: Union[bool, Tuple[bool]] = False,
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        layers_per_block: Union[int, Tuple[int]] = 2,
+        downsample_padding: int = 1,
+        mid_block_scale_factor: float = 1,
+        act_fn: str = "silu",
+        norm_num_groups: Optional[int] = 32,
+        norm_eps: float = 1e-5,
+        cross_attention_dim: Union[int, Tuple[int]] = 1280,
+        transformer_layers_per_block: Union[int, Tuple[int]] = 1,
+        encoder_hid_dim: Optional[int] = None,
+        encoder_hid_dim_type: Optional[str] = None,
+        attention_head_dim: Union[int, Tuple[int]] = 8,
+        num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
+        dual_cross_attention: bool = False,
+        use_linear_projection: bool = False,
+        class_embed_type: Optional[str] = None,
+        addition_embed_type: Optional[str] = None,
+        addition_time_embed_dim: Optional[int] = None,
+        num_class_embeds: Optional[int] = None,
+        upcast_attention: bool = False,
+        resnet_time_scale_shift: str = "default",
+        resnet_skip_time_act: bool = False,
+        resnet_out_scale_factor: int = 1.0,
+        time_embedding_type: str = "positional",
+        time_embedding_dim: Optional[int] = None,
+        time_embedding_act_fn: Optional[str] = None,
+        timestep_post_act: Optional[str] = None,
+        time_cond_proj_dim: Optional[int] = None,
+        conv_in_kernel: int = 3,
+        conv_out_kernel: int = 3,
+        projection_class_embeddings_input_dim: Optional[int] = None,
+        attention_type: str = "default",
+        class_embeddings_concat: bool = False,
+        mid_block_only_cross_attention: Optional[bool] = None,
+        cross_attention_norm: Optional[str] = None,
+        addition_embed_type_num_heads=64,
+    ):
+        super().__init__()
+
+        self.sample_size = sample_size
+
+        if num_attention_heads is not None:
+            raise ValueError(
+                "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."
+            )
+
+        # If `num_attention_heads` is not defined (which is the case for most models)
+        # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
+        # The reason for this behavior is to correct for incorrectly named variables that were introduced
+        # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
+        # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
+        # which is why we correct for the naming here.
+        num_attention_heads = num_attention_heads or attention_head_dim
+
+        # Check inputs
+        if len(down_block_types) != len(up_block_types):
+            raise ValueError(
+                f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
+            )
+
+        if len(block_out_channels) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
+            )
+
+        # input
+        conv_in_padding = (conv_in_kernel - 1) // 2
+        self.conv_in = nn.Conv2d(
+            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
+        )
+
+        # time
+        if time_embedding_type == "fourier":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
+            if time_embed_dim % 2 != 0:
+                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
+            self.time_proj = GaussianFourierProjection(
+                time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
+            )
+            timestep_input_dim = time_embed_dim
+        elif time_embedding_type == "positional":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
+
+            self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+            timestep_input_dim = block_out_channels[0]
+        else:
+            raise ValueError(
+                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
+            )
+
+        self.time_embedding = TimestepEmbedding(
+            timestep_input_dim,
+            time_embed_dim,
+            act_fn=act_fn,
+            post_act_fn=timestep_post_act,
+            cond_proj_dim=time_cond_proj_dim,
+        )
+
+        if encoder_hid_dim_type is None and encoder_hid_dim is not None:
+            encoder_hid_dim_type = "text_proj"
+            self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type)
+            logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.")
+
+        if encoder_hid_dim is None and encoder_hid_dim_type is not None:
+            raise ValueError(
+                f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}."
+            )
+
+        if encoder_hid_dim_type == "text_proj":
+            self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim)
+        elif encoder_hid_dim_type == "text_image_proj":
+            # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+            # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)`
+            self.encoder_hid_proj = TextImageProjection(
+                text_embed_dim=encoder_hid_dim,
+                image_embed_dim=cross_attention_dim,
+                cross_attention_dim=cross_attention_dim,
+            )
+        elif encoder_hid_dim_type == "image_proj":
+            # Kandinsky 2.2
+            self.encoder_hid_proj = ImageProjection(
+                image_embed_dim=encoder_hid_dim,
+                cross_attention_dim=cross_attention_dim,
+            )
+        elif encoder_hid_dim_type is not None:
+            raise ValueError(
+                f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'."
+            )
+        else:
+            self.encoder_hid_proj = None
+
+        # class embedding
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn)
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        elif class_embed_type == "projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
+            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
+            # 2. it projects from an arbitrary input dimension.
+            #
+            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
+            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
+            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
+            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+        elif class_embed_type == "simple_projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim)
+        else:
+            self.class_embedding = None
+
+        if addition_embed_type == "text":
+            if encoder_hid_dim is not None:
+                text_time_embedding_from_dim = encoder_hid_dim
+            else:
+                text_time_embedding_from_dim = cross_attention_dim
+
+            self.add_embedding = TextTimeEmbedding(
+                text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads
+            )
+        elif addition_embed_type == "text_image":
+            # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much
+            # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use
+            # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)`
+            self.add_embedding = TextImageTimeEmbedding(
+                text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim
+            )
+        elif addition_embed_type == "text_time":
+            self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift)
+            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+        elif addition_embed_type == "image":
+            # Kandinsky 2.2
+            self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
+        elif addition_embed_type == "image_hint":
+            # Kandinsky 2.2 ControlNet
+            self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim)
+        elif addition_embed_type is not None:
+            raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.")
+
+        if time_embedding_act_fn is None:
+            self.time_embed_act = None
+        else:
+            self.time_embed_act = get_activation(time_embedding_act_fn)
+
+        self.down_blocks = nn.ModuleList([])
+        self.up_blocks = nn.ModuleList([])
+
+        if isinstance(only_cross_attention, bool):
+            if mid_block_only_cross_attention is None:
+                mid_block_only_cross_attention = only_cross_attention
+
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+        if mid_block_only_cross_attention is None:
+            mid_block_only_cross_attention = False
+
+        if isinstance(num_attention_heads, int):
+            num_attention_heads = (num_attention_heads,) * len(down_block_types)
+
+        if isinstance(attention_head_dim, int):
+            attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+        if isinstance(cross_attention_dim, int):
+            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
+
+        if isinstance(layers_per_block, int):
+            layers_per_block = [layers_per_block] * len(down_block_types)
+
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
+
+        if class_embeddings_concat:
+            # The time embeddings are concatenated with the class embeddings. The dimension of the
+            # time embeddings passed to the down, middle, and up blocks is twice the dimension of the
+            # regular time embeddings
+            blocks_time_embed_dim = time_embed_dim * 2
+        else:
+            blocks_time_embed_dim = time_embed_dim
+
+        # down
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block[i],
+                transformer_layers_per_block=transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim[i],
+                num_attention_heads=num_attention_heads[i],
+                downsample_padding=downsample_padding,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                attention_type=attention_type,
+                resnet_skip_time_act=resnet_skip_time_act,
+                resnet_out_scale_factor=resnet_out_scale_factor,
+                cross_attention_norm=cross_attention_norm,
+                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        if mid_block_type == "UNetMidBlock2DCrossAttn":
+            self.mid_block = UNetMidBlock2DCrossAttn(
+                transformer_layers_per_block=transformer_layers_per_block[-1],
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim[-1],
+                num_attention_heads=num_attention_heads[-1],
+                resnet_groups=norm_num_groups,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+                attention_type=attention_type,
+            )
+        elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn":
+            self.mid_block = UNetMidBlock2DSimpleCrossAttn(
+                in_channels=block_out_channels[-1],
+                temb_channels=blocks_time_embed_dim,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                cross_attention_dim=cross_attention_dim[-1],
+                attention_head_dim=attention_head_dim[-1],
+                resnet_groups=norm_num_groups,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                skip_time_act=resnet_skip_time_act,
+                only_cross_attention=mid_block_only_cross_attention,
+                cross_attention_norm=cross_attention_norm,
+            )
+        elif mid_block_type is None:
+            self.mid_block = None
+        else:
+            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
+
+        # count how many layers upsample the images
+        self.num_upsamplers = 0
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        reversed_num_attention_heads = list(reversed(num_attention_heads))
+        reversed_layers_per_block = list(reversed(layers_per_block))
+        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
+        reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
+        only_cross_attention = list(reversed(only_cross_attention))
+
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            is_final_block = i == len(block_out_channels) - 1
+
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
+
+            # add upsample block for all BUT final layer
+            if not is_final_block:
+                add_upsample = True
+                self.num_upsamplers += 1
+            else:
+                add_upsample = False
+
+            up_block = get_up_block(
+                up_block_type,
+                num_layers=reversed_layers_per_block[i] + 1,
+                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
+                in_channels=input_channel,
+                out_channels=output_channel,
+                prev_output_channel=prev_output_channel,
+                temb_channels=blocks_time_embed_dim,
+                add_upsample=add_upsample,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=reversed_cross_attention_dim[i],
+                num_attention_heads=reversed_num_attention_heads[i],
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                attention_type=attention_type,
+                resnet_skip_time_act=resnet_skip_time_act,
+                resnet_out_scale_factor=resnet_out_scale_factor,
+                cross_attention_norm=cross_attention_norm,
+                attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel,
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+        self.up_blocks[3].attentions[2].transformer_blocks[0].attn1.to_q = _LoRACompatibleLinear()
+        self.up_blocks[3].attentions[2].transformer_blocks[0].attn1.to_k = _LoRACompatibleLinear()
+        self.up_blocks[3].attentions[2].transformer_blocks[0].attn1.to_v = _LoRACompatibleLinear()
+        self.up_blocks[3].attentions[2].transformer_blocks[0].attn1.to_out = nn.ModuleList([Identity(), Identity()])
+        self.up_blocks[3].attentions[2].transformer_blocks[0].norm2 = Identity()
+        self.up_blocks[3].attentions[2].transformer_blocks[0].attn2 = None
+        self.up_blocks[3].attentions[2].transformer_blocks[0].norm3 = Identity()
+        self.up_blocks[3].attentions[2].transformer_blocks[0].ff = Identity()
+        self.up_blocks[3].attentions[2].proj_out = Identity()
+
+        if attention_type in ["gated", "gated-text-image"]:
+            positive_len = 768
+            if isinstance(cross_attention_dim, int):
+                positive_len = cross_attention_dim
+            elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list):
+                positive_len = cross_attention_dim[0]
+
+            feature_type = "text-only" if attention_type == "gated" else "text-image"
+            self.position_net = PositionNet(
+                positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type
+            )
+
+    @property
+    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(return_deprecated_lora=True)
+
+            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
+
+    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.
+        """
+        if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnAddedKVProcessor()
+        elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnProcessor()
+        else:
+            raise ValueError(
+                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
+            )
+
+        self.set_attn_processor(processor)
+
+    def set_attention_slice(self, slice_size):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module splits the input tensor in slices to compute attention in
+        several steps. This is useful for saving some memory in exchange for a small decrease in speed.
+
+        Args:
+            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+                When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If
+                `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is
+                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+                must be a multiple of `slice_size`.
+        """
+        sliceable_head_dims = []
+
+        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                sliceable_head_dims.append(module.sliceable_head_dim)
+
+            for child in module.children():
+                fn_recursive_retrieve_sliceable_dims(child)
+
+        # retrieve number of attention layers
+        for module in self.children():
+            fn_recursive_retrieve_sliceable_dims(module)
+
+        num_sliceable_layers = len(sliceable_head_dims)
+
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = [dim // 2 for dim in sliceable_head_dims]
+        elif slice_size == "max":
+            # make smallest slice possible
+            slice_size = num_sliceable_layers * [1]
+
+        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
+
+        if len(slice_size) != len(sliceable_head_dims):
+            raise ValueError(
+                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+            )
+
+        for i in range(len(slice_size)):
+            size = slice_size[i]
+            dim = sliceable_head_dims[i]
+            if size is not None and size > dim:
+                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+        # Recursively walk through all the children.
+        # Any children which exposes the set_attention_slice method
+        # gets the message
+        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
+            if hasattr(module, "set_attention_slice"):
+                module.set_attention_slice(slice_size.pop())
+
+            for child in module.children():
+                fn_recursive_set_attention_slice(child, slice_size)
+
+        reversed_slice_size = list(reversed(slice_size))
+        for module in self.children():
+            fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        class_labels: Optional[torch.Tensor] = None,
+        timestep_cond: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+        mid_block_additional_residual: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ) -> Union[UNet2DConditionOutput, Tuple]:
+        r"""
+        The [`UNet2DConditionModel`] forward method.
+
+        Args:
+            sample (`torch.FloatTensor`):
+                The noisy input tensor with the following shape `(batch, channel, height, width)`.
+            timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input.
+            encoder_hidden_states (`torch.FloatTensor`):
+                The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
+            encoder_attention_mask (`torch.Tensor`):
+                A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If
+                `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias,
+                which adds large negative values to the attention scores corresponding to "discard" tokens.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
+                tuple.
+            cross_attention_kwargs (`dict`, *optional*):
+                A kwargs dictionary that if specified is passed along to the [`AttnProcessor`].
+            added_cond_kwargs: (`dict`, *optional*):
+                A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that
+                are passed along to the UNet blocks.
+
+        Returns:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+                If `return_dict` is True, an [`~models.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise
+                a `tuple` is returned where the first element is the sample tensor.
+        """
+        # By default samples have to be AT least a multiple of the overall upsampling factor.
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layers).
+        # However, the upsampling interpolation output size can be forced to fit any upsampling size
+        # on the fly if necessary.
+        default_overall_up_factor = 2**self.num_upsamplers
+
+        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+        forward_upsample_size = False
+        upsample_size = None
+
+        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
+            logger.info("Forward upsample size to force interpolation output size.")
+            forward_upsample_size = True
+
+        if attention_mask is not None:
+            attention_mask = (1 - attention_mask.to(sample.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:
+            encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0
+            encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
+
+        # 0. center input if necessary
+        if self.config.center_input_sample:
+            sample = 2 * sample - 1.0
+
+        # 1. time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # `Timesteps` does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=sample.dtype)
+
+        emb = self.time_embedding(t_emb, timestep_cond)
+        aug_emb = None
+
+        if self.class_embedding is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+                # `Timesteps` does not contain any weights and will always return f32 tensors
+                # there might be better ways to encapsulate this.
+                class_labels = class_labels.to(dtype=sample.dtype)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype)
+
+            if self.config.class_embeddings_concat:
+                emb = torch.cat([emb, class_emb], dim=-1)
+            else:
+                emb = emb + class_emb
+
+        if self.config.addition_embed_type == "text":
+            aug_emb = self.add_embedding(encoder_hidden_states)
+        elif self.config.addition_embed_type == "text_image":
+            # Kandinsky 2.1 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
+                )
+
+            image_embs = added_cond_kwargs.get("image_embeds")
+            text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states)
+            aug_emb = self.add_embedding(text_embs, image_embs)
+        elif self.config.addition_embed_type == "text_time":
+            # SDXL - style
+            if "text_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
+                )
+            text_embeds = added_cond_kwargs.get("text_embeds")
+            if "time_ids" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
+                )
+            time_ids = added_cond_kwargs.get("time_ids")
+            time_embeds = self.add_time_proj(time_ids.flatten())
+            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
+
+            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
+            add_embeds = add_embeds.to(emb.dtype)
+            aug_emb = self.add_embedding(add_embeds)
+        elif self.config.addition_embed_type == "image":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`"
+                )
+            image_embs = added_cond_kwargs.get("image_embeds")
+            aug_emb = self.add_embedding(image_embs)
+        elif self.config.addition_embed_type == "image_hint":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`"
+                )
+            image_embs = added_cond_kwargs.get("image_embeds")
+            hint = added_cond_kwargs.get("hint")
+            aug_emb, hint = self.add_embedding(image_embs, hint)
+            sample = torch.cat([sample, hint], dim=1)
+
+        emb = emb + aug_emb if aug_emb is not None else emb
+
+        if self.time_embed_act is not None:
+            emb = self.time_embed_act(emb)
+
+        if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj":
+            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj":
+            # Kadinsky 2.1 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds)
+        elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj":
+            # Kandinsky 2.2 - style
+            if "image_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in  `added_conditions`"
+                )
+            image_embeds = added_cond_kwargs.get("image_embeds")
+            encoder_hidden_states = self.encoder_hid_proj(image_embeds)
+        # 2. pre-process
+        sample = self.conv_in(sample)
+
+        # 2.5 GLIGEN position net
+        if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None:
+            cross_attention_kwargs = cross_attention_kwargs.copy()
+            gligen_args = cross_attention_kwargs.pop("gligen")
+            cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)}
+
+        # 3. down
+
+        is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
+        is_adapter = mid_block_additional_residual is None and down_block_additional_residuals is not None
+
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                # For t2i-adapter CrossAttnDownBlock2D
+                additional_residuals = {}
+                if is_adapter and len(down_block_additional_residuals) > 0:
+                    additional_residuals["additional_residuals"] = down_block_additional_residuals.pop(0)
+
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    encoder_attention_mask=encoder_attention_mask,
+                    **additional_residuals,
+                )
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+
+                if is_adapter and len(down_block_additional_residuals) > 0:
+                    sample += down_block_additional_residuals.pop(0)
+
+            down_block_res_samples += res_samples
+
+        if is_controlnet:
+            new_down_block_res_samples = ()
+
+            for down_block_res_sample, down_block_additional_residual in zip(
+                down_block_res_samples, down_block_additional_residuals
+            ):
+                down_block_res_sample = down_block_res_sample + down_block_additional_residual
+                new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
+
+            down_block_res_samples = new_down_block_res_samples
+
+        # 4. mid
+        if self.mid_block is not None:
+            sample = self.mid_block(
+                sample,
+                emb,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=attention_mask,
+                cross_attention_kwargs=cross_attention_kwargs,
+                encoder_attention_mask=encoder_attention_mask,
+            )
+            # To support T2I-Adapter-XL
+            if (
+                is_adapter
+                and len(down_block_additional_residuals) > 0
+                and sample.shape == down_block_additional_residuals[0].shape
+            ):
+                sample += down_block_additional_residuals.pop(0)
+
+        if is_controlnet:
+            sample = sample + mid_block_additional_residual
+
+        # 5. up
+        for i, upsample_block in enumerate(self.up_blocks):
+            is_final_block = i == len(self.up_blocks) - 1
+
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+            # if we have not reached the final block and need to forward the
+            # upsample size, we do it here
+            if not is_final_block and forward_upsample_size:
+                upsample_size = down_block_res_samples[-1].shape[2:]
+
+            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    upsample_size=upsample_size,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                )
+            else:
+                sample = upsample_block(
+                    hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size
+                )
+
+        if not return_dict:
+            return (sample,)
+
+        return UNet2DConditionOutput(sample=sample)
\ No newline at end of file

magicanimate/models/attention.py

+# *************************************************************************
+# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
+# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
+# ytedance Inc..  
+# *************************************************************************
+
+# Copyright 2023 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 dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils import BaseOutput
+from diffusers.utils.import_utils import is_xformers_available
+from diffusers.models.attention import FeedForward, AdaLayerNorm
+from diffusers.models.attention import Attention as CrossAttention
+
+from einops import rearrange, repeat
+
+@dataclass
+class Transformer3DModelOutput(BaseOutput):
+    sample: torch.FloatTensor
+
+
+if is_xformers_available():
+    import xformers
+    import xformers.ops
+else:
+    xformers = None
+
+
+class Transformer3DModel(ModelMixin, ConfigMixin):
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int = 16,
+        attention_head_dim: int = 88,
+        in_channels: Optional[int] = None,
+        num_layers: int = 1,
+        dropout: float = 0.0,
+        norm_num_groups: int = 32,
+        cross_attention_dim: Optional[int] = None,
+        attention_bias: bool = False,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        use_linear_projection: bool = False,
+        only_cross_attention: bool = False,
+        upcast_attention: bool = False,
+
+        unet_use_cross_frame_attention=None,
+        unet_use_temporal_attention=None,
+    ):
+        super().__init__()
+        self.use_linear_projection = use_linear_projection
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_dim = attention_head_dim
+        inner_dim = num_attention_heads * attention_head_dim
+
+        # Define input layers
+        self.in_channels = in_channels
+
+        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+        if use_linear_projection:
+            self.proj_in = nn.Linear(in_channels, inner_dim)
+        else:
+            self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
+
+        # Define transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    dropout=dropout,
+                    cross_attention_dim=cross_attention_dim,
+                    activation_fn=activation_fn,
+                    num_embeds_ada_norm=num_embeds_ada_norm,
+                    attention_bias=attention_bias,
+                    only_cross_attention=only_cross_attention,
+                    upcast_attention=upcast_attention,
+
+                    unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                    unet_use_temporal_attention=unet_use_temporal_attention,
+                )
+                for d in range(num_layers)
+            ]
+        )
+
+        # 4. Define output layers
+        if use_linear_projection:
+            self.proj_out = nn.Linear(in_channels, inner_dim)
+        else:
+            self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True):
+        # Input
+        assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
+        video_length = hidden_states.shape[2]
+        hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
+        # JH: need not repeat when a list of prompts are given 
+        if encoder_hidden_states.shape[0] != hidden_states.shape[0]:
+            encoder_hidden_states = repeat(encoder_hidden_states, 'b n c -> (b f) n c', f=video_length)
+
+        batch, channel, height, weight = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        if not self.use_linear_projection:
+            hidden_states = self.proj_in(hidden_states)
+            inner_dim = hidden_states.shape[1]
+            hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
+        else:
+            inner_dim = hidden_states.shape[1]
+            hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
+            hidden_states = self.proj_in(hidden_states)
+
+        # Blocks
+        for block in self.transformer_blocks:
+            hidden_states = block(
+                hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                timestep=timestep,
+                video_length=video_length
+            )
+
+        # Output
+        if not self.use_linear_projection:
+            hidden_states = (
+                hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
+            )
+            hidden_states = self.proj_out(hidden_states)
+        else:
+            hidden_states = self.proj_out(hidden_states)
+            hidden_states = (
+                hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
+            )
+
+        output = hidden_states + residual
+
+        output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
+        if not return_dict:
+            return (output,)
+
+        return Transformer3DModelOutput(sample=output)
+
+
+class BasicTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        dropout=0.0,
+        cross_attention_dim: Optional[int] = None,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        attention_bias: bool = False,
+        only_cross_attention: bool = False,
+        upcast_attention: bool = False,
+
+        unet_use_cross_frame_attention = None,
+        unet_use_temporal_attention = None,
+    ):
+        super().__init__()
+        self.only_cross_attention = only_cross_attention
+        self.use_ada_layer_norm = num_embeds_ada_norm is not None
+        self.unet_use_cross_frame_attention = unet_use_cross_frame_attention
+        self.unet_use_temporal_attention = unet_use_temporal_attention
+
+        # SC-Attn
+        assert unet_use_cross_frame_attention is not None
+        if unet_use_cross_frame_attention:
+            self.attn1 = SparseCausalAttention2D(
+                query_dim=dim,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+                upcast_attention=upcast_attention,
+            )
+        else:
+            self.attn1 = CrossAttention(
+                query_dim=dim,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                upcast_attention=upcast_attention,
+            )
+        self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
+
+        # Cross-Attn
+        if cross_attention_dim is not None:
+            self.attn2 = CrossAttention(
+                query_dim=dim,
+                cross_attention_dim=cross_attention_dim,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                upcast_attention=upcast_attention,
+            )
+        else:
+            self.attn2 = None
+
+        if cross_attention_dim is not None:
+            self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
+        else:
+            self.norm2 = None
+
+        # Feed-forward
+        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
+        self.norm3 = nn.LayerNorm(dim)
+        self.use_ada_layer_norm_zero = False
+        
+        # Temp-Attn
+        assert unet_use_temporal_attention is not None
+        if unet_use_temporal_attention:
+            self.attn_temp = CrossAttention(
+                query_dim=dim,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                upcast_attention=upcast_attention,
+            )
+            nn.init.zeros_(self.attn_temp.to_out[0].weight.data)
+            self.norm_temp = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
+
+    def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool, *args, **kwargs):
+        if not is_xformers_available():
+            print("Here is how to install it")
+            raise ModuleNotFoundError(
+                "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
+                " xformers",
+                name="xformers",
+            )
+        elif not torch.cuda.is_available():
+            raise ValueError(
+                "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only"
+                " available for GPU "
+            )
+        else:
+            try:
+                # Make sure we can run the memory efficient attention
+                _ = xformers.ops.memory_efficient_attention(
+                    torch.randn((1, 2, 40), device="cuda"),
+                    torch.randn((1, 2, 40), device="cuda"),
+                    torch.randn((1, 2, 40), device="cuda"),
+                )
+            except Exception as e:
+                raise e
+            self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
+            if self.attn2 is not None:
+                self.attn2._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
+            # self.attn_temp._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
+
+    def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None):
+        # SparseCausal-Attention
+        norm_hidden_states = (
+            self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states)
+        )
+
+        # if self.only_cross_attention:
+        #     hidden_states = (
+        #         self.attn1(norm_hidden_states, encoder_hidden_states, attention_mask=attention_mask) + hidden_states
+        #     )
+        # else:
+        #     hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + hidden_states
+
+        # pdb.set_trace()
+        if self.unet_use_cross_frame_attention:
+            hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + hidden_states
+        else:
+            hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask) + hidden_states
+
+        if self.attn2 is not None:
+            # Cross-Attention
+            norm_hidden_states = (
+                self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
+            )
+            hidden_states = (
+                self.attn2(
+                    norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
+                )
+                + hidden_states
+            )
+
+        # Feed-forward
+        hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
+
+        # Temporal-Attention
+        if self.unet_use_temporal_attention:
+            d = hidden_states.shape[1]
+            hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
+            norm_hidden_states = (
+                self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)
+            )
+            hidden_states = self.attn_temp(norm_hidden_states) + hidden_states
+            hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
+
+        return hidden_states

magicanimate/models/controlnet.py

+# *************************************************************************
+# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
+# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
+# ytedance Inc..  
+# *************************************************************************
+
+# Copyright 2023 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 dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.utils import BaseOutput, logging
+from .embeddings import TimestepEmbedding, Timesteps
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.unet_2d_blocks import (
+    CrossAttnDownBlock2D,
+    DownBlock2D,
+    UNetMidBlock2DCrossAttn,
+    get_down_block,
+)
+from diffusers.models.unet_2d_condition import UNet2DConditionModel
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class ControlNetOutput(BaseOutput):
+    down_block_res_samples: Tuple[torch.Tensor]
+    mid_block_res_sample: torch.Tensor
+
+
+class ControlNetConditioningEmbedding(nn.Module):
+    """
+    Quoting from https://arxiv.org/abs/2302.05543: "Stable Diffusion uses a pre-processing method similar to VQ-GAN
+    [11] to convert the entire dataset of 512 × 512 images into smaller 64 × 64 “latent images” for stabilized
+    training. This requires ControlNets to convert image-based conditions to 64 × 64 feature space to match the
+    convolution size. We use a tiny network E(·) of four convolution layers with 4 × 4 kernels and 2 × 2 strides
+    (activated by ReLU, channels are 16, 32, 64, 128, initialized with Gaussian weights, trained jointly with the full
+    model) to encode image-space conditions ... into feature maps ..."
+    """
+
+    def __init__(
+        self,
+        conditioning_embedding_channels: int,
+        conditioning_channels: int = 3,
+        block_out_channels: Tuple[int] = (16, 32, 96, 256),
+    ):
+        super().__init__()
+
+        self.conv_in = nn.Conv2d(conditioning_channels, block_out_channels[0], kernel_size=3, padding=1)
+
+        self.blocks = nn.ModuleList([])
+
+        for i in range(len(block_out_channels) - 1):
+            channel_in = block_out_channels[i]
+            channel_out = block_out_channels[i + 1]
+            self.blocks.append(nn.Conv2d(channel_in, channel_in, kernel_size=3, padding=1))
+            self.blocks.append(nn.Conv2d(channel_in, channel_out, kernel_size=3, padding=1, stride=2))
+
+        self.conv_out = zero_module(
+            nn.Conv2d(block_out_channels[-1], conditioning_embedding_channels, kernel_size=3, padding=1)
+        )
+
+    def forward(self, conditioning):
+        embedding = self.conv_in(conditioning)
+        embedding = F.silu(embedding)
+
+        for block in self.blocks:
+            embedding = block(embedding)
+            embedding = F.silu(embedding)
+
+        embedding = self.conv_out(embedding)
+
+        return embedding
+
+
+class ControlNetModel(ModelMixin, ConfigMixin):
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        in_channels: int = 4,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,
+        down_block_types: Tuple[str] = (
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "CrossAttnDownBlock2D",
+            "DownBlock2D",
+        ),
+        only_cross_attention: Union[bool, Tuple[bool]] = False,
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        layers_per_block: int = 2,
+        downsample_padding: int = 1,
+        mid_block_scale_factor: float = 1,
+        act_fn: str = "silu",
+        norm_num_groups: Optional[int] = 32,
+        norm_eps: float = 1e-5,
+        cross_attention_dim: int = 1280,
+        attention_head_dim: Union[int, Tuple[int]] = 8,
+        use_linear_projection: bool = False,
+        class_embed_type: Optional[str] = None,
+        num_class_embeds: Optional[int] = None,
+        upcast_attention: bool = False,
+        resnet_time_scale_shift: str = "default",
+        projection_class_embeddings_input_dim: Optional[int] = None,
+        controlnet_conditioning_channel_order: str = "rgb",
+        conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),
+    ):
+        super().__init__()
+
+        # Check inputs
+        if len(block_out_channels) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        # input
+        conv_in_kernel = 3
+        conv_in_padding = (conv_in_kernel - 1) // 2
+        self.conv_in = nn.Conv2d(
+            in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding
+        )
+
+        # time
+        time_embed_dim = block_out_channels[0] * 4
+
+        self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+        timestep_input_dim = block_out_channels[0]
+
+        self.time_embedding = TimestepEmbedding(
+            timestep_input_dim,
+            time_embed_dim,
+            act_fn=act_fn,
+        )
+
+        # class embedding
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        elif class_embed_type == "projection":
+            if projection_class_embeddings_input_dim is None:
+                raise ValueError(
+                    "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set"
+                )
+            # The projection `class_embed_type` is the same as the timestep `class_embed_type` except
+            # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings
+            # 2. it projects from an arbitrary input dimension.
+            #
+            # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations.
+            # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings.
+            # As a result, `TimestepEmbedding` can be passed arbitrary vectors.
+            self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+        else:
+            self.class_embedding = None
+
+        # control net conditioning embedding
+        self.controlnet_cond_embedding = ControlNetConditioningEmbedding(
+            conditioning_embedding_channels=block_out_channels[0],
+            block_out_channels=conditioning_embedding_out_channels,
+        )
+
+        self.down_blocks = nn.ModuleList([])
+        self.controlnet_down_blocks = nn.ModuleList([])
+
+        if isinstance(only_cross_attention, bool):
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+        if isinstance(attention_head_dim, int):
+            attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+        # down
+        output_channel = block_out_channels[0]
+
+        controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
+        controlnet_block = zero_module(controlnet_block)
+        self.controlnet_down_blocks.append(controlnet_block)
+
+        for i, down_block_type in enumerate(down_block_types):
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim,
+                num_attention_heads=attention_head_dim[i],
+                downsample_padding=downsample_padding,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+            )
+            self.down_blocks.append(down_block)
+
+            for _ in range(layers_per_block):
+                controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
+                controlnet_block = zero_module(controlnet_block)
+                self.controlnet_down_blocks.append(controlnet_block)
+
+            if not is_final_block:
+                controlnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1)
+                controlnet_block = zero_module(controlnet_block)
+                self.controlnet_down_blocks.append(controlnet_block)
+
+        # mid
+        mid_block_channel = block_out_channels[-1]
+
+        controlnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1)
+        controlnet_block = zero_module(controlnet_block)
+        self.controlnet_mid_block = controlnet_block
+
+        self.mid_block = UNetMidBlock2DCrossAttn(
+            in_channels=mid_block_channel,
+            temb_channels=time_embed_dim,
+            resnet_eps=norm_eps,
+            resnet_act_fn=act_fn,
+            output_scale_factor=mid_block_scale_factor,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+            cross_attention_dim=cross_attention_dim,
+            num_attention_heads=attention_head_dim[-1],
+            resnet_groups=norm_num_groups,
+            use_linear_projection=use_linear_projection,
+            upcast_attention=upcast_attention,
+        )
+
+    @classmethod
+    def from_unet(
+        cls,
+        unet: UNet2DConditionModel,
+        controlnet_conditioning_channel_order: str = "rgb",
+        conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256),
+        load_weights_from_unet: bool = True,
+    ):
+        r"""
+        Instantiate Controlnet class from UNet2DConditionModel.
+
+        Parameters:
+            unet (`UNet2DConditionModel`):
+                UNet model which weights are copied to the ControlNet. Note that all configuration options are also
+                copied where applicable.
+        """
+        controlnet = cls(
+            in_channels=unet.config.in_channels,
+            flip_sin_to_cos=unet.config.flip_sin_to_cos,
+            freq_shift=unet.config.freq_shift,
+            down_block_types=unet.config.down_block_types,
+            only_cross_attention=unet.config.only_cross_attention,
+            block_out_channels=unet.config.block_out_channels,
+            layers_per_block=unet.config.layers_per_block,
+            downsample_padding=unet.config.downsample_padding,
+            mid_block_scale_factor=unet.config.mid_block_scale_factor,
+            act_fn=unet.config.act_fn,
+            norm_num_groups=unet.config.norm_num_groups,
+            norm_eps=unet.config.norm_eps,
+            cross_attention_dim=unet.config.cross_attention_dim,
+            attention_head_dim=unet.config.attention_head_dim,
+            use_linear_projection=unet.config.use_linear_projection,
+            class_embed_type=unet.config.class_embed_type,
+            num_class_embeds=unet.config.num_class_embeds,
+            upcast_attention=unet.config.upcast_attention,
+            resnet_time_scale_shift=unet.config.resnet_time_scale_shift,
+            projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim,
+            controlnet_conditioning_channel_order=controlnet_conditioning_channel_order,
+            conditioning_embedding_out_channels=conditioning_embedding_out_channels,
+        )
+
+        if load_weights_from_unet:
+            controlnet.conv_in.load_state_dict(unet.conv_in.state_dict())
+            controlnet.time_proj.load_state_dict(unet.time_proj.state_dict())
+            controlnet.time_embedding.load_state_dict(unet.time_embedding.state_dict())
+
+            if controlnet.class_embedding:
+                controlnet.class_embedding.load_state_dict(unet.class_embedding.state_dict())
+
+            controlnet.down_blocks.load_state_dict(unet.down_blocks.state_dict())
+            controlnet.mid_block.load_state_dict(unet.mid_block.state_dict())
+
+        return controlnet
+
+    # @property
+    # # Copied from diffusers.models.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, "set_processor"):
+    #             processors[f"{name}.processor"] = module.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.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    # def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
+    #     r"""
+    #     Parameters:
+    #         `processor (`dict` of `AttentionProcessor` or `AttentionProcessor`):
+    #             The instantiated processor class or a dictionary of processor classes that will be set as the processor
+    #             of **all** `Attention` layers.
+    #         In case `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.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
+    # def set_default_attn_processor(self):
+    #     """
+    #     Disables custom attention processors and sets the default attention implementation.
+    #     """
+    #     self.set_attn_processor(AttnProcessor())
+
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attention_slice
+    def set_attention_slice(self, slice_size):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                `"max"`, maximum amount of memory will be saved by running only one slice at a time. If a number is
+                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+                must be a multiple of `slice_size`.
+        """
+        sliceable_head_dims = []
+
+        def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                sliceable_head_dims.append(module.sliceable_head_dim)
+
+            for child in module.children():
+                fn_recursive_retrieve_sliceable_dims(child)
+
+        # retrieve number of attention layers
+        for module in self.children():
+            fn_recursive_retrieve_sliceable_dims(module)
+
+        num_sliceable_layers = len(sliceable_head_dims)
+
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = [dim // 2 for dim in sliceable_head_dims]
+        elif slice_size == "max":
+            # make smallest slice possible
+            slice_size = num_sliceable_layers * [1]
+
+        slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
+
+        if len(slice_size) != len(sliceable_head_dims):
+            raise ValueError(
+                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+            )
+
+        for i in range(len(slice_size)):
+            size = slice_size[i]
+            dim = sliceable_head_dims[i]
+            if size is not None and size > dim:
+                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+        # Recursively walk through all the children.
+        # Any children which exposes the set_attention_slice method
+        # gets the message
+        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
+            if hasattr(module, "set_attention_slice"):
+                module.set_attention_slice(slice_size.pop())
+
+            for child in module.children():
+                fn_recursive_set_attention_slice(child, slice_size)
+
+        reversed_slice_size = list(reversed(slice_size))
+        for module in self.children():
+            fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        controlnet_cond: torch.FloatTensor,
+        conditioning_scale: float = 1.0,
+        class_labels: Optional[torch.Tensor] = None,
+        timestep_cond: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        return_dict: bool = True,
+    ) -> Union[ControlNetOutput, Tuple]:
+        # check channel order
+        channel_order = self.config.controlnet_conditioning_channel_order
+
+        if channel_order == "rgb":
+            # in rgb order by default
+            ...
+        elif channel_order == "bgr":
+            controlnet_cond = torch.flip(controlnet_cond, dims=[1])
+        else:
+            raise ValueError(f"unknown `controlnet_conditioning_channel_order`: {channel_order}")
+
+        # prepare attention_mask
+        if attention_mask is not None:
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # 1. time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=self.dtype)
+
+        emb = self.time_embedding(t_emb, timestep_cond)
+
+        if self.class_embedding is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
+            emb = emb + class_emb
+
+        # 2. pre-process
+        sample = self.conv_in(sample)
+
+        controlnet_cond = self.controlnet_cond_embedding(controlnet_cond)
+
+        sample += controlnet_cond
+
+        # 3. down
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                    # cross_attention_kwargs=cross_attention_kwargs,
+                )
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb)
+
+            down_block_res_samples += res_samples
+
+        # 4. mid
+        if self.mid_block is not None:
+            sample = self.mid_block(
+                sample,
+                emb,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=attention_mask,
+                # cross_attention_kwargs=cross_attention_kwargs,
+            )
+
+        # 5. Control net blocks
+
+        controlnet_down_block_res_samples = ()
+
+        for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks):
+            down_block_res_sample = controlnet_block(down_block_res_sample)
+            controlnet_down_block_res_samples += (down_block_res_sample,)
+
+        down_block_res_samples = controlnet_down_block_res_samples
+
+        mid_block_res_sample = self.controlnet_mid_block(sample)
+
+        # 6. scaling
+        down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]
+        mid_block_res_sample *= conditioning_scale
+
+        if not return_dict:
+            return (down_block_res_samples, mid_block_res_sample)
+
+        return ControlNetOutput(
+            down_block_res_samples=down_block_res_samples, mid_block_res_sample=mid_block_res_sample
+        )
+
+
+def zero_module(module):
+    for p in module.parameters():
+        nn.init.zeros_(p)
+    return module
\ No newline at end of file

magicanimate/models/embeddings.py

+# *************************************************************************
+# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
+# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
+# ytedance Inc..  
+# *************************************************************************
+
+# Copyright 2023 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.
+import math
+from typing import Optional
+
+import numpy as np
+import torch
+from torch import nn
+
+
+def get_timestep_embedding(
+    timesteps: torch.Tensor,
+    embedding_dim: int,
+    flip_sin_to_cos: bool = False,
+    downscale_freq_shift: float = 1,
+    scale: float = 1,
+    max_period: int = 10000,
+):
+    """
+    This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
+
+    :param timesteps: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
+    embeddings. :return: an [N x dim] Tensor of positional embeddings.
+    """
+    assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
+
+    half_dim = embedding_dim // 2
+    exponent = -math.log(max_period) * torch.arange(
+        start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
+    )
+    exponent = exponent / (half_dim - downscale_freq_shift)
+
+    emb = torch.exp(exponent)
+    emb = timesteps[:, None].float() * emb[None, :]
+
+    # scale embeddings
+    emb = scale * emb
+
+    # concat sine and cosine embeddings
+    emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
+
+    # flip sine and cosine embeddings
+    if flip_sin_to_cos:
+        emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
+
+    # zero pad
+    if embedding_dim % 2 == 1:
+        emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
+    return emb
+
+
+def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
+    """
+    grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or
+    [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    grid_h = np.arange(grid_size, dtype=np.float32)
+    grid_w = np.arange(grid_size, dtype=np.float32)
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+
+    grid = grid.reshape([2, 1, grid_size, grid_size])
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token and extra_tokens > 0:
+        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
+    return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    if embed_dim % 2 != 0:
+        raise ValueError("embed_dim must be divisible by 2")
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
+
+    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)
+    """
+    if embed_dim % 2 != 0:
+        raise ValueError("embed_dim must be divisible by 2")
+
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.0
+    omega = 1.0 / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)  # (M, D/2)
+    emb_cos = np.cos(out)  # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+
+class PatchEmbed(nn.Module):
+    """2D Image to Patch Embedding"""
+
+    def __init__(
+        self,
+        height=224,
+        width=224,
+        patch_size=16,
+        in_channels=3,
+        embed_dim=768,
+        layer_norm=False,
+        flatten=True,
+        bias=True,
+    ):
+        super().__init__()
+
+        num_patches = (height // patch_size) * (width // patch_size)
+        self.flatten = flatten
+        self.layer_norm = layer_norm
+
+        self.proj = nn.Conv2d(
+            in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
+        )
+        if layer_norm:
+            self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6)
+        else:
+            self.norm = None
+
+        pos_embed = get_2d_sincos_pos_embed(embed_dim, int(num_patches**0.5))
+        self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=False)
+
+    def forward(self, latent):
+        latent = self.proj(latent)
+        if self.flatten:
+            latent = latent.flatten(2).transpose(1, 2)  # BCHW -> BNC
+        if self.layer_norm:
+            latent = self.norm(latent)
+        return latent + self.pos_embed
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        time_embed_dim: int,
+        act_fn: str = "silu",
+        out_dim: int = None,
+        post_act_fn: Optional[str] = None,
+        cond_proj_dim=None,
+    ):
+        super().__init__()
+
+        self.linear_1 = nn.Linear(in_channels, time_embed_dim)
+
+        if cond_proj_dim is not None:
+            self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False)
+        else:
+            self.cond_proj = None
+
+        if act_fn == "silu":
+            self.act = nn.SiLU()
+        elif act_fn == "mish":
+            self.act = nn.Mish()
+        elif act_fn == "gelu":
+            self.act = nn.GELU()
+        else:
+            raise ValueError(f"{act_fn} does not exist. Make sure to define one of 'silu', 'mish', or 'gelu'")
+
+        if out_dim is not None:
+            time_embed_dim_out = out_dim
+        else:
+            time_embed_dim_out = time_embed_dim
+        self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out)
+
+        if post_act_fn is None:
+            self.post_act = None
+        elif post_act_fn == "silu":
+            self.post_act = nn.SiLU()
+        elif post_act_fn == "mish":
+            self.post_act = nn.Mish()
+        elif post_act_fn == "gelu":
+            self.post_act = nn.GELU()
+        else:
+            raise ValueError(f"{post_act_fn} does not exist. Make sure to define one of 'silu', 'mish', or 'gelu'")
+
+    def forward(self, sample, condition=None):
+        if condition is not None:
+            sample = sample + self.cond_proj(condition)
+        sample = self.linear_1(sample)
+
+        if self.act is not None:
+            sample = self.act(sample)
+
+        sample = self.linear_2(sample)
+
+        if self.post_act is not None:
+            sample = self.post_act(sample)
+        return sample
+
+
+class Timesteps(nn.Module):
+    def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
+        super().__init__()
+        self.num_channels = num_channels
+        self.flip_sin_to_cos = flip_sin_to_cos
+        self.downscale_freq_shift = downscale_freq_shift
+
+    def forward(self, timesteps):
+        t_emb = get_timestep_embedding(
+            timesteps,
+            self.num_channels,
+            flip_sin_to_cos=self.flip_sin_to_cos,
+            downscale_freq_shift=self.downscale_freq_shift,
+        )
+        return t_emb
+
+
+class GaussianFourierProjection(nn.Module):
+    """Gaussian Fourier embeddings for noise levels."""
+
+    def __init__(
+        self, embedding_size: int = 256, scale: float = 1.0, set_W_to_weight=True, log=True, flip_sin_to_cos=False
+    ):
+        super().__init__()
+        self.weight = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
+        self.log = log
+        self.flip_sin_to_cos = flip_sin_to_cos
+
+        if set_W_to_weight:
+            # to delete later
+            self.W = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
+
+            self.weight = self.W
+
+    def forward(self, x):
+        if self.log:
+            x = torch.log(x)
+
+        x_proj = x[:, None] * self.weight[None, :] * 2 * np.pi
+
+        if self.flip_sin_to_cos:
+            out = torch.cat([torch.cos(x_proj), torch.sin(x_proj)], dim=-1)
+        else:
+            out = torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)
+        return out
+
+
+class ImagePositionalEmbeddings(nn.Module):
+    """
+    Converts latent image classes into vector embeddings. Sums the vector embeddings with positional embeddings for the
+    height and width of the latent space.
+
+    For more details, see figure 10 of the dall-e paper: https://arxiv.org/abs/2102.12092
+
+    For VQ-diffusion:
+
+    Output vector embeddings are used as input for the transformer.
+
+    Note that the vector embeddings for the transformer are different than the vector embeddings from the VQVAE.
+
+    Args:
+        num_embed (`int`):
+            Number of embeddings for the latent pixels embeddings.
+        height (`int`):
+            Height of the latent image i.e. the number of height embeddings.
+        width (`int`):
+            Width of the latent image i.e. the number of width embeddings.
+        embed_dim (`int`):
+            Dimension of the produced vector embeddings. Used for the latent pixel, height, and width embeddings.
+    """
+
+    def __init__(
+        self,
+        num_embed: int,
+        height: int,
+        width: int,
+        embed_dim: int,
+    ):
+        super().__init__()
+
+        self.height = height
+        self.width = width
+        self.num_embed = num_embed
+        self.embed_dim = embed_dim
+
+        self.emb = nn.Embedding(self.num_embed, embed_dim)
+        self.height_emb = nn.Embedding(self.height, embed_dim)
+        self.width_emb = nn.Embedding(self.width, embed_dim)
+
+    def forward(self, index):
+        emb = self.emb(index)
+
+        height_emb = self.height_emb(torch.arange(self.height, device=index.device).view(1, self.height))
+
+        # 1 x H x D -> 1 x H x 1 x D
+        height_emb = height_emb.unsqueeze(2)
+
+        width_emb = self.width_emb(torch.arange(self.width, device=index.device).view(1, self.width))
+
+        # 1 x W x D -> 1 x 1 x W x D
+        width_emb = width_emb.unsqueeze(1)
+
+        pos_emb = height_emb + width_emb
+
+        # 1 x H x W x D -> 1 x L xD
+        pos_emb = pos_emb.view(1, self.height * self.width, -1)
+
+        emb = emb + pos_emb[:, : emb.shape[1], :]
+
+        return emb
+
+
+class LabelEmbedding(nn.Module):
+    """
+    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
+
+    Args:
+        num_classes (`int`): The number of classes.
+        hidden_size (`int`): The size of the vector embeddings.
+        dropout_prob (`float`): The probability of dropping a label.
+    """
+
+    def __init__(self, num_classes, hidden_size, dropout_prob):
+        super().__init__()
+        use_cfg_embedding = dropout_prob > 0
+        self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
+        self.num_classes = num_classes
+        self.dropout_prob = dropout_prob
+
+    def token_drop(self, labels, force_drop_ids=None):
+        """
+        Drops labels to enable classifier-free guidance.
+        """
+        if force_drop_ids is None:
+            drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
+        else:
+            drop_ids = torch.tensor(force_drop_ids == 1)
+        labels = torch.where(drop_ids, self.num_classes, labels)
+        return labels
+
+    def forward(self, labels, force_drop_ids=None):
+        use_dropout = self.dropout_prob > 0
+        if (self.training and use_dropout) or (force_drop_ids is not None):
+            labels = self.token_drop(labels, force_drop_ids)
+        embeddings = self.embedding_table(labels)
+        return embeddings
+
+
+class CombinedTimestepLabelEmbeddings(nn.Module):
+    def __init__(self, num_classes, embedding_dim, class_dropout_prob=0.1):
+        super().__init__()
+
+        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=1)
+        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+        self.class_embedder = LabelEmbedding(num_classes, embedding_dim, class_dropout_prob)
+
+    def forward(self, timestep, class_labels, hidden_dtype=None):
+        timesteps_proj = self.time_proj(timestep)
+        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))  # (N, D)
+
+        class_labels = self.class_embedder(class_labels)  # (N, D)
+
+        conditioning = timesteps_emb + class_labels  # (N, D)
+
+        return conditioning
\ No newline at end of file

magicanimate/models/motion_module.py

+# *************************************************************************
+# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
+# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
+# ytedance Inc..  
+# *************************************************************************
+
+# Adapted from https://github.com/guoyww/AnimateDiff
+from dataclasses import dataclass
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from diffusers.utils import BaseOutput
+from diffusers.utils.import_utils import is_xformers_available
+from diffusers.models.attention import FeedForward
+from magicanimate.models.orig_attention import CrossAttention
+
+from einops import rearrange, repeat
+import math
+
+
+def zero_module(module):
+    # Zero out the parameters of a module and return it.
+    for p in module.parameters():
+        p.detach().zero_()
+    return module
+
+
+@dataclass
+class TemporalTransformer3DModelOutput(BaseOutput):
+    sample: torch.FloatTensor
+
+
+if is_xformers_available():
+    import xformers
+    import xformers.ops
+else:
+    xformers = None
+
+
+def get_motion_module(
+    in_channels,
+    motion_module_type: str, 
+    motion_module_kwargs: dict
+):
+    if motion_module_type == "Vanilla":
+        return VanillaTemporalModule(in_channels=in_channels, **motion_module_kwargs,)    
+    else:
+        raise ValueError
+
+
+class VanillaTemporalModule(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        num_attention_heads                = 8,
+        num_transformer_block              = 2,
+        attention_block_types              =( "Temporal_Self", "Temporal_Self" ),
+        cross_frame_attention_mode         = None,
+        temporal_position_encoding         = False,
+        temporal_position_encoding_max_len = 24,
+        temporal_attention_dim_div         = 1,
+        zero_initialize                    = True,
+    ):
+        super().__init__()
+        
+        self.temporal_transformer = TemporalTransformer3DModel(
+            in_channels=in_channels,
+            num_attention_heads=num_attention_heads,
+            attention_head_dim=in_channels // num_attention_heads // temporal_attention_dim_div,
+            num_layers=num_transformer_block,
+            attention_block_types=attention_block_types,
+            cross_frame_attention_mode=cross_frame_attention_mode,
+            temporal_position_encoding=temporal_position_encoding,
+            temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+        )
+        
+        if zero_initialize:
+            self.temporal_transformer.proj_out = zero_module(self.temporal_transformer.proj_out)
+
+    def forward(self, input_tensor, temb, encoder_hidden_states, attention_mask=None, anchor_frame_idx=None):
+        hidden_states = input_tensor
+        hidden_states = self.temporal_transformer(hidden_states, encoder_hidden_states, attention_mask)
+
+        output = hidden_states
+        return output
+
+
+class TemporalTransformer3DModel(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        num_attention_heads,
+        attention_head_dim,
+
+        num_layers,
+        attention_block_types              = ( "Temporal_Self", "Temporal_Self", ),        
+        dropout                            = 0.0,
+        norm_num_groups                    = 32,
+        cross_attention_dim                = 768,
+        activation_fn                      = "geglu",
+        attention_bias                     = False,
+        upcast_attention                   = False,
+        
+        cross_frame_attention_mode         = None,
+        temporal_position_encoding         = False,
+        temporal_position_encoding_max_len = 24,
+    ):
+        super().__init__()
+
+        inner_dim = num_attention_heads * attention_head_dim
+
+        self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+        self.proj_in = nn.Linear(in_channels, inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                TemporalTransformerBlock(
+                    dim=inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                    attention_block_types=attention_block_types,
+                    dropout=dropout,
+                    norm_num_groups=norm_num_groups,
+                    cross_attention_dim=cross_attention_dim,
+                    activation_fn=activation_fn,
+                    attention_bias=attention_bias,
+                    upcast_attention=upcast_attention,
+                    cross_frame_attention_mode=cross_frame_attention_mode,
+                    temporal_position_encoding=temporal_position_encoding,
+                    temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+                )
+                for d in range(num_layers)
+            ]
+        )
+        self.proj_out = nn.Linear(inner_dim, in_channels)    
+    
+    def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None):
+        assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
+        video_length = hidden_states.shape[2]
+        hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
+
+        batch, channel, height, weight = hidden_states.shape
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
+        hidden_states = self.proj_in(hidden_states)
+
+        # Transformer Blocks
+        for block in self.transformer_blocks:
+            hidden_states = block(hidden_states, encoder_hidden_states=encoder_hidden_states, video_length=video_length)
+        
+        # output
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
+
+        output = hidden_states + residual
+        output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
+        
+        return output
+
+
+class TemporalTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim,
+        num_attention_heads,
+        attention_head_dim,
+        attention_block_types              = ( "Temporal_Self", "Temporal_Self", ),
+        dropout                            = 0.0,
+        norm_num_groups                    = 32,
+        cross_attention_dim                = 768,
+        activation_fn                      = "geglu",
+        attention_bias                     = False,
+        upcast_attention                   = False,
+        cross_frame_attention_mode         = None,
+        temporal_position_encoding         = False,
+        temporal_position_encoding_max_len = 24,
+    ):
+        super().__init__()
+
+        attention_blocks = []
+        norms = []
+        
+        for block_name in attention_block_types:
+            attention_blocks.append(
+                VersatileAttention(
+                    attention_mode=block_name.split("_")[0],
+                    cross_attention_dim=cross_attention_dim if block_name.endswith("_Cross") else None,
+                    
+                    query_dim=dim,
+                    heads=num_attention_heads,
+                    dim_head=attention_head_dim,
+                    dropout=dropout,
+                    bias=attention_bias,
+                    upcast_attention=upcast_attention,
+        
+                    cross_frame_attention_mode=cross_frame_attention_mode,
+                    temporal_position_encoding=temporal_position_encoding,
+                    temporal_position_encoding_max_len=temporal_position_encoding_max_len,
+                )
+            )
+            norms.append(nn.LayerNorm(dim))
+            
+        self.attention_blocks = nn.ModuleList(attention_blocks)
+        self.norms = nn.ModuleList(norms)
+
+        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
+        self.ff_norm = nn.LayerNorm(dim)
+
+
+    def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None):
+        for attention_block, norm in zip(self.attention_blocks, self.norms):
+            norm_hidden_states = norm(hidden_states)
+            hidden_states = attention_block(
+                norm_hidden_states,
+                encoder_hidden_states=encoder_hidden_states if attention_block.is_cross_attention else None,
+                video_length=video_length,
+            ) + hidden_states
+            
+        hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states
+        
+        output = hidden_states  
+        return output
+
+
+class PositionalEncoding(nn.Module):
+    def __init__(
+        self, 
+        d_model, 
+        dropout = 0., 
+        max_len = 24
+    ):
+        super().__init__()
+        self.dropout = nn.Dropout(p=dropout)
+        position = torch.arange(max_len).unsqueeze(1)
+        div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
+        pe = torch.zeros(1, max_len, d_model)
+        pe[0, :, 0::2] = torch.sin(position * div_term)
+        pe[0, :, 1::2] = torch.cos(position * div_term)
+        self.register_buffer('pe', pe)
+
+    def forward(self, x):
+        x = x + self.pe[:, :x.size(1)]
+        return self.dropout(x)
+
+
+class VersatileAttention(CrossAttention):
+    def __init__(
+            self,
+            attention_mode                     = None,
+            cross_frame_attention_mode         = None,
+            temporal_position_encoding         = False,
+            temporal_position_encoding_max_len = 24,            
+            *args, **kwargs
+        ):
+        super().__init__(*args, **kwargs)
+        assert attention_mode == "Temporal"
+
+        self.attention_mode = attention_mode
+        self.is_cross_attention = kwargs["cross_attention_dim"] is not None
+        
+        self.pos_encoder = PositionalEncoding(
+            kwargs["query_dim"],
+            dropout=0., 
+            max_len=temporal_position_encoding_max_len
+        ) if (temporal_position_encoding and attention_mode == "Temporal") else None
+
+    def extra_repr(self):
+        return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}"
+
+    def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None):
+        batch_size, sequence_length, _ = hidden_states.shape
+
+        if self.attention_mode == "Temporal":
+            d = hidden_states.shape[1]
+            hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
+            
+            if self.pos_encoder is not None:
+                hidden_states = self.pos_encoder(hidden_states)
+            
+            encoder_hidden_states = repeat(encoder_hidden_states, "b n c -> (b d) n c", d=d) if encoder_hidden_states is not None else encoder_hidden_states
+        else:
+            raise NotImplementedError
+
+        encoder_hidden_states = encoder_hidden_states
+
+        if self.group_norm is not None:
+            hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = self.to_q(hidden_states)
+        dim = query.shape[-1]
+        query = self.reshape_heads_to_batch_dim(query)
+
+        if self.added_kv_proj_dim is not None:
+            raise NotImplementedError
+
+        encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
+        key = self.to_k(encoder_hidden_states)
+        value = self.to_v(encoder_hidden_states)
+
+        key = self.reshape_heads_to_batch_dim(key)
+        value = self.reshape_heads_to_batch_dim(value)
+
+        if attention_mask is not None:
+            if attention_mask.shape[-1] != query.shape[1]:
+                target_length = query.shape[1]
+                attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
+                attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
+
+        # attention, what we cannot get enough of
+        if self._use_memory_efficient_attention_xformers:
+            hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
+            # Some versions of xformers return output in fp32, cast it back to the dtype of the input
+            hidden_states = hidden_states.to(query.dtype)
+        else:
+            if self._slice_size is None or query.shape[0] // self._slice_size == 1:
+                hidden_states = self._attention(query, key, value, attention_mask)
+            else:
+                hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
+
+        # linear proj
+        hidden_states = self.to_out[0](hidden_states)
+
+        # dropout
+        hidden_states = self.to_out[1](hidden_states)
+
+        if self.attention_mode == "Temporal":
+            hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
+
+        return hidden_states

magicanimate/models/mutual_self_attention.py

+# Copyright 2023 ByteDance and/or its affiliates.
+#
+# Copyright (2023) MagicAnimate Authors
+#
+# ByteDance, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from ByteDance or
+# its affiliates is strictly prohibited.
+
+import torch
+import torch.nn.functional as F
+
+from einops import rearrange
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+from diffusers.models.attention import BasicTransformerBlock
+from magicanimate.models.attention import BasicTransformerBlock as _BasicTransformerBlock
+from diffusers.models.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
+from .stable_diffusion_controlnet_reference import torch_dfs
+
+
+class AttentionBase:
+    def __init__(self):
+        self.cur_step = 0
+        self.num_att_layers = -1
+        self.cur_att_layer = 0
+
+    def after_step(self):
+        pass
+
+    def __call__(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
+        out = self.forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
+        self.cur_att_layer += 1
+        if self.cur_att_layer == self.num_att_layers:
+            self.cur_att_layer = 0
+            self.cur_step += 1
+            # after step
+            self.after_step()
+        return out
+
+    def forward(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
+        out = torch.einsum('b i j, b j d -> b i d', attn, v)
+        out = rearrange(out, '(b h) n d -> b n (h d)', h=num_heads)
+        return out
+
+    def reset(self):
+        self.cur_step = 0
+        self.cur_att_layer = 0
+
+
+class MutualSelfAttentionControl(AttentionBase):
+
+    def __init__(self, total_steps=50, hijack_init_state=True, with_negative_guidance=False, appearance_control_alpha=0.5, mode='enqueue'):
+        """
+        Mutual self-attention control for Stable-Diffusion MODEl
+        Args:
+            total_steps: the total number of steps
+        """
+        super().__init__()
+        self.total_steps = total_steps
+        self.hijack = hijack_init_state
+        self.with_negative_guidance = with_negative_guidance
+        
+        # alpha: mutual self attention intensity
+        # TODO: make alpha learnable
+        self.alpha = appearance_control_alpha
+        self.GLOBAL_ATTN_QUEUE = []
+        assert mode in ['enqueue', 'dequeue']
+        MODE = mode
+    
+    def attn_batch(self, q, k, v, num_heads, **kwargs):
+        """
+        Performing attention for a batch of queries, keys, and values
+        """
+        b = q.shape[0] // num_heads
+        q = rearrange(q, "(b h) n d -> h (b n) d", h=num_heads)
+        k = rearrange(k, "(b h) n d -> h (b n) d", h=num_heads)
+        v = rearrange(v, "(b h) n d -> h (b n) d", h=num_heads)
+
+        sim = torch.einsum("h i d, h j d -> h i j", q, k) * kwargs.get("scale")
+        attn = sim.softmax(-1)
+        out = torch.einsum("h i j, h j d -> h i d", attn, v)
+        out = rearrange(out, "h (b n) d -> b n (h d)", b=b)
+        return out
+
+    def mutual_self_attn(self, q, k, v, num_heads, **kwargs):
+        q_tgt, q_src = q.chunk(2)
+        k_tgt, k_src = k.chunk(2)
+        v_tgt, v_src = v.chunk(2)
+        
+        # out_tgt = self.attn_batch(q_tgt, k_src, v_src, num_heads, **kwargs) * self.alpha + \
+        #           self.attn_batch(q_tgt, k_tgt, v_tgt, num_heads, **kwargs) * (1 - self.alpha)
+        out_tgt = self.attn_batch(q_tgt, torch.cat([k_tgt, k_src], dim=1), torch.cat([v_tgt, v_src], dim=1), num_heads, **kwargs)
+        out_src = self.attn_batch(q_src, k_src, v_src, num_heads, **kwargs)
+        out = torch.cat([out_tgt, out_src], dim=0)
+        return out
+    
+    def mutual_self_attn_wq(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
+        if self.MODE == 'dequeue' and len(self.kv_queue) > 0:
+            k_src, v_src = self.kv_queue.pop(0)
+            out = self.attn_batch(q, torch.cat([k, k_src], dim=1), torch.cat([v, v_src], dim=1), num_heads, **kwargs)
+            return out
+        else:
+            self.kv_queue.append([k.clone(), v.clone()])
+            return super().forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
+    
+    def get_queue(self):
+        return self.GLOBAL_ATTN_QUEUE
+    
+    def set_queue(self, attn_queue):
+        self.GLOBAL_ATTN_QUEUE = attn_queue
+    
+    def clear_queue(self):
+        self.GLOBAL_ATTN_QUEUE = []
+    
+    def to(self, dtype):
+        self.GLOBAL_ATTN_QUEUE = [p.to(dtype) for p in self.GLOBAL_ATTN_QUEUE]
+
+    def forward(self, q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs):
+        """
+        Attention forward function
+        """
+        return super().forward(q, k, v, sim, attn, is_cross, place_in_unet, num_heads, **kwargs)
+
+
+class ReferenceAttentionControl():
+    
+    def __init__(self, 
+                 unet,
+                 mode="write",
+                 do_classifier_free_guidance=False,
+                 attention_auto_machine_weight = float('inf'),
+                 gn_auto_machine_weight = 1.0,
+                 style_fidelity = 1.0,
+                 reference_attn=True,
+                 reference_adain=False,
+                 fusion_blocks="midup",
+                 batch_size=1, 
+                 ) -> None:
+        # 10. Modify self attention and group norm
+        self.unet = unet
+        assert mode in ["read", "write"]
+        assert fusion_blocks in ["midup", "full"]
+        self.reference_attn = reference_attn
+        self.reference_adain = reference_adain
+        self.fusion_blocks = fusion_blocks
+        self.register_reference_hooks(
+            mode, 
+            do_classifier_free_guidance,
+            attention_auto_machine_weight,
+            gn_auto_machine_weight,
+            style_fidelity,
+            reference_attn,
+            reference_adain,
+            fusion_blocks,
+            batch_size=batch_size, 
+        )
+
+    def register_reference_hooks(
+            self, 
+            mode, 
+            do_classifier_free_guidance,
+            attention_auto_machine_weight,
+            gn_auto_machine_weight,
+            style_fidelity,
+            reference_attn,
+            reference_adain,
+            dtype=torch.float16,
+            batch_size=1, 
+            num_images_per_prompt=1, 
+            device=torch.device("cpu"), 
+            fusion_blocks='midup',
+        ):
+        MODE = mode
+        do_classifier_free_guidance = do_classifier_free_guidance
+        attention_auto_machine_weight = attention_auto_machine_weight
+        gn_auto_machine_weight = gn_auto_machine_weight
+        style_fidelity = style_fidelity
+        reference_attn = reference_attn
+        reference_adain = reference_adain
+        fusion_blocks = fusion_blocks
+        num_images_per_prompt = num_images_per_prompt
+        dtype=dtype
+        if do_classifier_free_guidance:
+            uc_mask = (
+                torch.Tensor([1] * batch_size * num_images_per_prompt * 16 + [0] * batch_size * num_images_per_prompt * 16)
+                .to(device)
+                .bool()
+            )
+        else:
+            uc_mask = (
+                torch.Tensor([0] * batch_size * num_images_per_prompt * 2)
+                .to(device)
+                .bool()
+            )
+        
+        def hacked_basic_transformer_inner_forward(
+            self,
+            hidden_states: torch.FloatTensor,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,
+            encoder_attention_mask: Optional[torch.FloatTensor] = None,
+            timestep: Optional[torch.LongTensor] = None,
+            cross_attention_kwargs: Dict[str, Any] = None,
+            class_labels: Optional[torch.LongTensor] = None,
+            video_length=None,
+        ):
+            if self.use_ada_layer_norm:
+                norm_hidden_states = self.norm1(hidden_states, timestep)
+            elif self.use_ada_layer_norm_zero:
+                norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+                    hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+                )
+            else:
+                norm_hidden_states = self.norm1(hidden_states)
+
+            # 1. Self-Attention
+            cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
+            if self.only_cross_attention:
+                attn_output = self.attn1(
+                    norm_hidden_states,
+                    encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+                    attention_mask=attention_mask,
+                    **cross_attention_kwargs,
+                )
+            else:
+                if MODE == "write":
+                    self.bank.append(norm_hidden_states.clone())
+                    attn_output = self.attn1(
+                        norm_hidden_states,
+                        encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+                        attention_mask=attention_mask,
+                        **cross_attention_kwargs,
+                    )
+                if MODE == "read":
+                    self.bank = [rearrange(d.unsqueeze(1).repeat(1, video_length, 1, 1), "b t l c -> (b t) l c")[:hidden_states.shape[0]] for d in self.bank]
+                    hidden_states_uc = self.attn1(norm_hidden_states, 
+                                                encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),
+                                                attention_mask=attention_mask) + hidden_states
+                    hidden_states_c = hidden_states_uc.clone()
+                    _uc_mask = uc_mask.clone()
+                    if do_classifier_free_guidance:
+                        if hidden_states.shape[0] != _uc_mask.shape[0]:
+                            _uc_mask = (
+                                torch.Tensor([1] * (hidden_states.shape[0]//2) + [0] * (hidden_states.shape[0]//2))
+                                .to(device)
+                                .bool()
+                            )
+                        hidden_states_c[_uc_mask] = self.attn1(
+                            norm_hidden_states[_uc_mask],
+                            encoder_hidden_states=norm_hidden_states[_uc_mask],
+                            attention_mask=attention_mask,
+                        ) + hidden_states[_uc_mask]
+                    hidden_states = hidden_states_c.clone()
+                        
+                    self.bank.clear()
+                    if self.attn2 is not None:
+                        # Cross-Attention
+                        norm_hidden_states = (
+                            self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
+                        )
+                        hidden_states = (
+                            self.attn2(
+                                norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
+                            )
+                            + hidden_states
+                        )
+
+                    # Feed-forward
+                    hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
+
+                    # Temporal-Attention
+                    if self.unet_use_temporal_attention:
+                        d = hidden_states.shape[1]
+                        hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
+                        norm_hidden_states = (
+                            self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states)
+                        )
+                        hidden_states = self.attn_temp(norm_hidden_states) + hidden_states
+                        hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
+
+                    return hidden_states
+                
+            if self.use_ada_layer_norm_zero:
+                attn_output = gate_msa.unsqueeze(1) * attn_output
+            hidden_states = attn_output + hidden_states
+
+            if self.attn2 is not None:
+                norm_hidden_states = (
+                    self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
+                )
+
+                # 2. Cross-Attention
+                attn_output = self.attn2(
+                    norm_hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=encoder_attention_mask,
+                    **cross_attention_kwargs,
+                )
+                hidden_states = attn_output + hidden_states
+
+            # 3. Feed-forward
+            norm_hidden_states = self.norm3(hidden_states)
+
+            if self.use_ada_layer_norm_zero:
+                norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+            ff_output = self.ff(norm_hidden_states)
+
+            if self.use_ada_layer_norm_zero:
+                ff_output = gate_mlp.unsqueeze(1) * ff_output
+
+            hidden_states = ff_output + hidden_states
+
+            return hidden_states
+
+        def hacked_mid_forward(self, *args, **kwargs):
+            eps = 1e-6
+            x = self.original_forward(*args, **kwargs)
+            if MODE == "write":
+                if gn_auto_machine_weight >= self.gn_weight:
+                    var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
+                    self.mean_bank.append(mean)
+                    self.var_bank.append(var)
+            if MODE == "read":
+                if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                    var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
+                    std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                    mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))
+                    var_acc = sum(self.var_bank) / float(len(self.var_bank))
+                    std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                    x_uc = (((x - mean) / std) * std_acc) + mean_acc
+                    x_c = x_uc.clone()
+                    if do_classifier_free_guidance and style_fidelity > 0:
+                        x_c[uc_mask] = x[uc_mask]
+                    x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc
+                self.mean_bank = []
+                self.var_bank = []
+            return x
+
+        def hack_CrossAttnDownBlock2D_forward(
+            self,
+            hidden_states: torch.FloatTensor,
+            temb: Optional[torch.FloatTensor] = None,
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+            encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        ):
+            eps = 1e-6
+
+            # TODO(Patrick, William) - attention mask is not used
+            output_states = ()
+
+            for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    return_dict=False,
+                )[0]
+                if MODE == "write":
+                    if gn_auto_machine_weight >= self.gn_weight:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        self.mean_bank.append([mean])
+                        self.var_bank.append([var])
+                if MODE == "read":
+                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
+                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
+                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
+                        hidden_states_c = hidden_states_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)
+                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
+
+                output_states = output_states + (hidden_states,)
+
+            if MODE == "read":
+                self.mean_bank = []
+                self.var_bank = []
+
+            if self.downsamplers is not None:
+                for downsampler in self.downsamplers:
+                    hidden_states = downsampler(hidden_states)
+
+                output_states = output_states + (hidden_states,)
+
+            return hidden_states, output_states
+
+        def hacked_DownBlock2D_forward(self, hidden_states, temb=None):
+            eps = 1e-6
+
+            output_states = ()
+
+            for i, resnet in enumerate(self.resnets):
+                hidden_states = resnet(hidden_states, temb)
+
+                if MODE == "write":
+                    if gn_auto_machine_weight >= self.gn_weight:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        self.mean_bank.append([mean])
+                        self.var_bank.append([var])
+                if MODE == "read":
+                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
+                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
+                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
+                        hidden_states_c = hidden_states_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)
+                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
+
+                output_states = output_states + (hidden_states,)
+
+            if MODE == "read":
+                self.mean_bank = []
+                self.var_bank = []
+
+            if self.downsamplers is not None:
+                for downsampler in self.downsamplers:
+                    hidden_states = downsampler(hidden_states)
+
+                output_states = output_states + (hidden_states,)
+
+            return hidden_states, output_states
+
+        def hacked_CrossAttnUpBlock2D_forward(
+            self,
+            hidden_states: torch.FloatTensor,
+            res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
+            temb: Optional[torch.FloatTensor] = None,
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,
+            cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+            upsample_size: Optional[int] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        ):
+            eps = 1e-6
+            # TODO(Patrick, William) - attention mask is not used
+            for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
+                # pop res hidden states
+                res_hidden_states = res_hidden_states_tuple[-1]
+                res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+                hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    return_dict=False,
+                )[0]
+
+                if MODE == "write":
+                    if gn_auto_machine_weight >= self.gn_weight:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        self.mean_bank.append([mean])
+                        self.var_bank.append([var])
+                if MODE == "read":
+                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
+                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
+                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
+                        hidden_states_c = hidden_states_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)
+                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
+
+            if MODE == "read":
+                self.mean_bank = []
+                self.var_bank = []
+
+            if self.upsamplers is not None:
+                for upsampler in self.upsamplers:
+                    hidden_states = upsampler(hidden_states, upsample_size)
+
+            return hidden_states
+
+        def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
+            eps = 1e-6
+            for i, resnet in enumerate(self.resnets):
+                # pop res hidden states
+                res_hidden_states = res_hidden_states_tuple[-1]
+                res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+                hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+                hidden_states = resnet(hidden_states, temb)
+
+                if MODE == "write":
+                    if gn_auto_machine_weight >= self.gn_weight:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        self.mean_bank.append([mean])
+                        self.var_bank.append([var])
+                if MODE == "read":
+                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
+                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
+                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
+                        hidden_states_c = hidden_states_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            hidden_states_c[uc_mask] = hidden_states[uc_mask].to(hidden_states_c.dtype)
+                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
+
+            if MODE == "read":
+                self.mean_bank = []
+                self.var_bank = []
+
+            if self.upsamplers is not None:
+                for upsampler in self.upsamplers:
+                    hidden_states = upsampler(hidden_states, upsample_size)
+
+            return hidden_states
+
+        if self.reference_attn:
+            if self.fusion_blocks == "midup":
+                attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]
+            elif self.fusion_blocks == "full":
+                attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]            
+            attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
+
+            for i, module in enumerate(attn_modules):
+                module._original_inner_forward = module.forward
+                module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)
+                module.bank = []
+                module.attn_weight = float(i) / float(len(attn_modules))
+
+        if self.reference_adain:
+            gn_modules = [self.unet.mid_block]
+            self.unet.mid_block.gn_weight = 0
+
+            down_blocks = self.unet.down_blocks
+            for w, module in enumerate(down_blocks):
+                module.gn_weight = 1.0 - float(w) / float(len(down_blocks))
+                gn_modules.append(module)
+
+            up_blocks = self.unet.up_blocks
+            for w, module in enumerate(up_blocks):
+                module.gn_weight = float(w) / float(len(up_blocks))
+                gn_modules.append(module)
+
+            for i, module in enumerate(gn_modules):
+                if getattr(module, "original_forward", None) is None:
+                    module.original_forward = module.forward
+                if i == 0:
+                    # mid_block
+                    module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)
+                elif isinstance(module, CrossAttnDownBlock2D):
+                    module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)
+                elif isinstance(module, DownBlock2D):
+                    module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)
+                elif isinstance(module, CrossAttnUpBlock2D):
+                    module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)
+                elif isinstance(module, UpBlock2D):
+                    module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)
+                module.mean_bank = []
+                module.var_bank = []
+                module.gn_weight *= 2
+    
+    def update(self, writer, dtype=torch.float16):
+        if self.reference_attn:
+            if self.fusion_blocks == "midup":
+                reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, _BasicTransformerBlock)]
+                writer_attn_modules = [module for module in (torch_dfs(writer.unet.mid_block)+torch_dfs(writer.unet.up_blocks)) if isinstance(module, BasicTransformerBlock)]
+            elif self.fusion_blocks == "full":
+                reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, _BasicTransformerBlock)]
+                writer_attn_modules = [module for module in torch_dfs(writer.unet) if isinstance(module, BasicTransformerBlock)]
+            reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])    
+            writer_attn_modules = sorted(writer_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
+            for r, w in zip(reader_attn_modules, writer_attn_modules):
+                r.bank = [v.clone().to(dtype) for v in w.bank]
+                # w.bank.clear()
+        if self.reference_adain:
+            reader_gn_modules = [self.unet.mid_block]
+            
+            down_blocks = self.unet.down_blocks
+            for w, module in enumerate(down_blocks):
+                reader_gn_modules.append(module)
+
+            up_blocks = self.unet.up_blocks
+            for w, module in enumerate(up_blocks):
+                reader_gn_modules.append(module)
+                
+            writer_gn_modules = [writer.unet.mid_block]
+            
+            down_blocks = writer.unet.down_blocks
+            for w, module in enumerate(down_blocks):
+                writer_gn_modules.append(module)
+
+            up_blocks = writer.unet.up_blocks
+            for w, module in enumerate(up_blocks):
+                writer_gn_modules.append(module)
+            
+            for r, w in zip(reader_gn_modules, writer_gn_modules):
+                if len(w.mean_bank) > 0 and isinstance(w.mean_bank[0], list):
+                    r.mean_bank = [[v.clone().to(dtype) for v in vl] for vl in w.mean_bank]
+                    r.var_bank = [[v.clone().to(dtype) for v in vl] for vl in w.var_bank]
+                else:
+                    r.mean_bank = [v.clone().to(dtype) for v in w.mean_bank]
+                    r.var_bank = [v.clone().to(dtype) for v in w.var_bank]
+    
+    def clear(self):
+        if self.reference_attn:
+            if self.fusion_blocks == "midup":
+                reader_attn_modules = [module for module in (torch_dfs(self.unet.mid_block)+torch_dfs(self.unet.up_blocks)) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]
+            elif self.fusion_blocks == "full":
+                reader_attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock) or isinstance(module, _BasicTransformerBlock)]
+            reader_attn_modules = sorted(reader_attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
+            for r in reader_attn_modules:
+                r.bank.clear()
+        if self.reference_adain:
+            reader_gn_modules = [self.unet.mid_block]
+            
+            down_blocks = self.unet.down_blocks
+            for w, module in enumerate(down_blocks):
+                reader_gn_modules.append(module)
+
+            up_blocks = self.unet.up_blocks
+            for w, module in enumerate(up_blocks):
+                reader_gn_modules.append(module)
+            
+            for r in reader_gn_modules:
+                r.mean_bank.clear()
+                r.var_bank.clear()
+            
\ No newline at end of file

magicanimate/models/orig_attention.py

+# *************************************************************************
+# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
+# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
+# ytedance Inc..  
+# *************************************************************************
+
+# Copyright 2022 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.
+import math
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.embeddings import ImagePositionalEmbeddings
+from diffusers.utils import BaseOutput
+from diffusers.utils.import_utils import is_xformers_available
+
+
+@dataclass
+class Transformer2DModelOutput(BaseOutput):
+    """
+    Args:
+        sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
+            Hidden states conditioned on `encoder_hidden_states` input. If discrete, returns probability distributions
+            for the unnoised latent pixels.
+    """
+
+    sample: torch.FloatTensor
+
+
+if is_xformers_available():
+    import xformers
+    import xformers.ops
+else:
+    xformers = None
+
+
+class Transformer2DModel(ModelMixin, ConfigMixin):
+    """
+    Transformer model for image-like data. Takes either discrete (classes of vector embeddings) or continuous (actual
+    embeddings) inputs.
+
+    When input is continuous: First, project the input (aka embedding) and reshape to b, t, d. Then apply standard
+    transformer action. Finally, reshape to image.
+
+    When input is discrete: First, input (classes of latent pixels) is converted to embeddings and has positional
+    embeddings applied, see `ImagePositionalEmbeddings`. Then apply standard transformer action. Finally, predict
+    classes of unnoised image.
+
+    Note that it is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised
+    image do not contain a prediction for the masked pixel as the unnoised image cannot be masked.
+
+    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 88): The number of channels in each head.
+        in_channels (`int`, *optional*):
+            Pass if the input is continuous. The number of channels in the input and output.
+        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        cross_attention_dim (`int`, *optional*): The number of encoder_hidden_states dimensions to use.
+        sample_size (`int`, *optional*): Pass if the input is discrete. The width of the latent images.
+            Note that this is fixed at training time as it is used for learning a number of position embeddings. See
+            `ImagePositionalEmbeddings`.
+        num_vector_embeds (`int`, *optional*):
+            Pass if the input is discrete. The number of classes of the vector embeddings of the latent pixels.
+            Includes the class for the masked latent pixel.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        num_embeds_ada_norm ( `int`, *optional*): Pass if at least one of the norm_layers is `AdaLayerNorm`.
+            The number of diffusion steps used during training. Note that this is fixed at training time as it is used
+            to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for
+            up to but not more than steps than `num_embeds_ada_norm`.
+        attention_bias (`bool`, *optional*):
+            Configure if the TransformerBlocks' attention should contain a bias parameter.
+    """
+
+    @register_to_config
+    def __init__(
+        self,
+        num_attention_heads: int = 16,
+        attention_head_dim: int = 88,
+        in_channels: Optional[int] = None,
+        num_layers: int = 1,
+        dropout: float = 0.0,
+        norm_num_groups: int = 32,
+        cross_attention_dim: Optional[int] = None,
+        attention_bias: bool = False,
+        sample_size: Optional[int] = None,
+        num_vector_embeds: Optional[int] = None,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        use_linear_projection: bool = False,
+        only_cross_attention: bool = False,
+        upcast_attention: bool = False,
+    ):
+        super().__init__()
+        self.use_linear_projection = use_linear_projection
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_dim = attention_head_dim
+        inner_dim = num_attention_heads * attention_head_dim
+
+        # 1. Transformer2DModel can process both standard continous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
+        # Define whether input is continuous or discrete depending on configuration
+        self.is_input_continuous = in_channels is not None
+        self.is_input_vectorized = num_vector_embeds is not None
+
+        if self.is_input_continuous and self.is_input_vectorized:
+            raise ValueError(
+                f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
+                " sure that either `in_channels` or `num_vector_embeds` is None."
+            )
+        elif not self.is_input_continuous and not self.is_input_vectorized:
+            raise ValueError(
+                f"Has to define either `in_channels`: {in_channels} or `num_vector_embeds`: {num_vector_embeds}. Make"
+                " sure that either `in_channels` or `num_vector_embeds` is not None."
+            )
+
+        # 2. Define input layers
+        if self.is_input_continuous:
+            self.in_channels = in_channels
+
+            self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+            if use_linear_projection:
+                self.proj_in = nn.Linear(in_channels, inner_dim)
+            else:
+                self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
+        elif self.is_input_vectorized:
+            assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size"
+            assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed"
+
+            self.height = sample_size
+            self.width = sample_size
+            self.num_vector_embeds = num_vector_embeds
+            self.num_latent_pixels = self.height * self.width
+
+            self.latent_image_embedding = ImagePositionalEmbeddings(
+                num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width
+            )
+
+        # 3. Define transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    dropout=dropout,
+                    cross_attention_dim=cross_attention_dim,
+                    activation_fn=activation_fn,
+                    num_embeds_ada_norm=num_embeds_ada_norm,
+                    attention_bias=attention_bias,
+                    only_cross_attention=only_cross_attention,
+                    upcast_attention=upcast_attention,
+                )
+                for d in range(num_layers)
+            ]
+        )
+
+        # 4. Define output layers
+        if self.is_input_continuous:
+            if use_linear_projection:
+                self.proj_out = nn.Linear(in_channels, inner_dim)
+            else:
+                self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0)
+        elif self.is_input_vectorized:
+            self.norm_out = nn.LayerNorm(inner_dim)
+            self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1)
+
+    def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True):
+        """
+        Args:
+            hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`.
+                When continous, `torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input
+                hidden_states
+            encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
+                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
+                self-attention.
+            timestep ( `torch.long`, *optional*):
+                Optional timestep to be applied as an embedding in AdaLayerNorm's. Used to indicate denoising step.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.attention.Transformer2DModelOutput`] or `tuple`: [`~models.attention.Transformer2DModelOutput`]
+            if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample
+            tensor.
+        """
+        # 1. Input
+        if self.is_input_continuous:
+            batch, channel, height, weight = hidden_states.shape
+            residual = hidden_states
+
+            hidden_states = self.norm(hidden_states)
+            if not self.use_linear_projection:
+                hidden_states = self.proj_in(hidden_states)
+                inner_dim = hidden_states.shape[1]
+                hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
+            else:
+                inner_dim = hidden_states.shape[1]
+                hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
+                hidden_states = self.proj_in(hidden_states)
+        elif self.is_input_vectorized:
+            hidden_states = self.latent_image_embedding(hidden_states)
+
+        # 2. Blocks
+        for block in self.transformer_blocks:
+            hidden_states = block(hidden_states, encoder_hidden_states=encoder_hidden_states, timestep=timestep)
+
+        # 3. Output
+        if self.is_input_continuous:
+            if not self.use_linear_projection:
+                hidden_states = (
+                    hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
+                )
+                hidden_states = self.proj_out(hidden_states)
+            else:
+                hidden_states = self.proj_out(hidden_states)
+                hidden_states = (
+                    hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
+                )
+
+            output = hidden_states + residual
+        elif self.is_input_vectorized:
+            hidden_states = self.norm_out(hidden_states)
+            logits = self.out(hidden_states)
+            # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
+            logits = logits.permute(0, 2, 1)
+
+            # log(p(x_0))
+            output = F.log_softmax(logits.double(), dim=1).float()
+
+        if not return_dict:
+            return (output,)
+
+        return Transformer2DModelOutput(sample=output)
+
+
+class AttentionBlock(nn.Module):
+    """
+    An attention block that allows spatial positions to attend to each other. Originally ported from here, but adapted
+    to the N-d case.
+    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+    Uses three q, k, v linear layers to compute attention.
+
+    Parameters:
+        channels (`int`): The number of channels in the input and output.
+        num_head_channels (`int`, *optional*):
+            The number of channels in each head. If None, then `num_heads` = 1.
+        norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for group norm.
+        rescale_output_factor (`float`, *optional*, defaults to 1.0): The factor to rescale the output by.
+        eps (`float`, *optional*, defaults to 1e-5): The epsilon value to use for group norm.
+    """
+
+    # IMPORTANT;TODO(Patrick, William) - this class will be deprecated soon. Do not use it anymore
+
+    def __init__(
+        self,
+        channels: int,
+        num_head_channels: Optional[int] = None,
+        norm_num_groups: int = 32,
+        rescale_output_factor: float = 1.0,
+        eps: float = 1e-5,
+    ):
+        super().__init__()
+        self.channels = channels
+
+        self.num_heads = channels // num_head_channels if num_head_channels is not None else 1
+        self.num_head_size = num_head_channels
+        self.group_norm = nn.GroupNorm(num_channels=channels, num_groups=norm_num_groups, eps=eps, affine=True)
+
+        # define q,k,v as linear layers
+        self.query = nn.Linear(channels, channels)
+        self.key = nn.Linear(channels, channels)
+        self.value = nn.Linear(channels, channels)
+
+        self.rescale_output_factor = rescale_output_factor
+        self.proj_attn = nn.Linear(channels, channels, 1)
+
+        self._use_memory_efficient_attention_xformers = False
+
+    def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool, *args, **kwargs):
+        if not is_xformers_available():
+            raise ModuleNotFoundError(
+                "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
+                " xformers",
+                name="xformers",
+            )
+        elif not torch.cuda.is_available():
+            raise ValueError(
+                "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only"
+                " available for GPU "
+            )
+        else:
+            try:
+                # Make sure we can run the memory efficient attention
+                _ = xformers.ops.memory_efficient_attention(
+                    torch.randn((1, 2, 40), device="cuda"),
+                    torch.randn((1, 2, 40), device="cuda"),
+                    torch.randn((1, 2, 40), device="cuda"),
+                )
+            except Exception as e:
+                raise e
+            self._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
+
+    def reshape_heads_to_batch_dim(self, tensor):
+        batch_size, seq_len, dim = tensor.shape
+        head_size = self.num_heads
+        tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
+        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)
+        return tensor
+
+    def reshape_batch_dim_to_heads(self, tensor):
+        batch_size, seq_len, dim = tensor.shape
+        head_size = self.num_heads
+        tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
+        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
+        return tensor
+
+    def forward(self, hidden_states):
+        residual = hidden_states
+        batch, channel, height, width = hidden_states.shape
+
+        # norm
+        hidden_states = self.group_norm(hidden_states)
+
+        hidden_states = hidden_states.view(batch, channel, height * width).transpose(1, 2)
+
+        # proj to q, k, v
+        query_proj = self.query(hidden_states)
+        key_proj = self.key(hidden_states)
+        value_proj = self.value(hidden_states)
+
+        scale = 1 / math.sqrt(self.channels / self.num_heads)
+
+        query_proj = self.reshape_heads_to_batch_dim(query_proj)
+        key_proj = self.reshape_heads_to_batch_dim(key_proj)
+        value_proj = self.reshape_heads_to_batch_dim(value_proj)
+
+        if self._use_memory_efficient_attention_xformers:
+            # Memory efficient attention
+            hidden_states = xformers.ops.memory_efficient_attention(query_proj, key_proj, value_proj, attn_bias=None)
+            hidden_states = hidden_states.to(query_proj.dtype)
+        else:
+            attention_scores = torch.baddbmm(
+                torch.empty(
+                    query_proj.shape[0],
+                    query_proj.shape[1],
+                    key_proj.shape[1],
+                    dtype=query_proj.dtype,
+                    device=query_proj.device,
+                ),
+                query_proj,
+                key_proj.transpose(-1, -2),
+                beta=0,
+                alpha=scale,
+            )
+            attention_probs = torch.softmax(attention_scores.float(), dim=-1).type(attention_scores.dtype)
+            hidden_states = torch.bmm(attention_probs, value_proj)
+
+        # reshape hidden_states
+        hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
+
+        # compute next hidden_states
+        hidden_states = self.proj_attn(hidden_states)
+
+        hidden_states = hidden_states.transpose(-1, -2).reshape(batch, channel, height, width)
+
+        # res connect and rescale
+        hidden_states = (hidden_states + residual) / self.rescale_output_factor
+        return hidden_states
+
+
+class BasicTransformerBlock(nn.Module):
+    r"""
+    A basic Transformer block.
+
+    Parameters:
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        num_embeds_ada_norm (:
+            obj: `int`, *optional*): The number of diffusion steps used during training. See `Transformer2DModel`.
+        attention_bias (:
+            obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        dropout=0.0,
+        cross_attention_dim: Optional[int] = None,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+        attention_bias: bool = False,
+        only_cross_attention: bool = False,
+        upcast_attention: bool = False,
+    ):
+        super().__init__()
+        self.only_cross_attention = only_cross_attention
+        self.use_ada_layer_norm = num_embeds_ada_norm is not None
+
+        # 1. Self-Attn
+        self.attn1 = CrossAttention(
+            query_dim=dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            dropout=dropout,
+            bias=attention_bias,
+            cross_attention_dim=cross_attention_dim if only_cross_attention else None,
+            upcast_attention=upcast_attention,
+        )  # is a self-attention
+        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
+
+        # 2. Cross-Attn
+        if cross_attention_dim is not None:
+            self.attn2 = CrossAttention(
+                query_dim=dim,
+                cross_attention_dim=cross_attention_dim,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+                dropout=dropout,
+                bias=attention_bias,
+                upcast_attention=upcast_attention,
+            )  # is self-attn if encoder_hidden_states is none
+        else:
+            self.attn2 = None
+
+        self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
+
+        if cross_attention_dim is not None:
+            self.norm2 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim)
+        else:
+            self.norm2 = None
+
+        # 3. Feed-forward
+        self.norm3 = nn.LayerNorm(dim)
+
+    def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool, *args, **kwargs):
+        if not is_xformers_available():
+            print("Here is how to install it")
+            raise ModuleNotFoundError(
+                "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
+                " xformers",
+                name="xformers",
+            )
+        elif not torch.cuda.is_available():
+            raise ValueError(
+                "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only"
+                " available for GPU "
+            )
+        else:
+            try:
+                # Make sure we can run the memory efficient attention
+                _ = xformers.ops.memory_efficient_attention(
+                    torch.randn((1, 2, 40), device="cuda"),
+                    torch.randn((1, 2, 40), device="cuda"),
+                    torch.randn((1, 2, 40), device="cuda"),
+                )
+            except Exception as e:
+                raise e
+            self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
+            if self.attn2 is not None:
+                self.attn2._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers
+
+    def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None):
+        # 1. Self-Attention
+        norm_hidden_states = (
+            self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states)
+        )
+
+        if self.only_cross_attention:
+            hidden_states = (
+                self.attn1(norm_hidden_states, encoder_hidden_states, attention_mask=attention_mask) + hidden_states
+            )
+        else:
+            hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask) + hidden_states
+
+        if self.attn2 is not None:
+            # 2. Cross-Attention
+            norm_hidden_states = (
+                self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
+            )
+            hidden_states = (
+                self.attn2(
+                    norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
+                )
+                + hidden_states
+            )
+
+        # 3. Feed-forward
+        hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states
+
+        return hidden_states
+
+
+class CrossAttention(nn.Module):
+    r"""
+    A cross attention layer.
+
+    Parameters:
+        query_dim (`int`): The number of channels in the query.
+        cross_attention_dim (`int`, *optional*):
+            The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
+        heads (`int`,  *optional*, defaults to 8): The number of heads to use for multi-head attention.
+        dim_head (`int`,  *optional*, defaults to 64): The number of channels in each head.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        bias (`bool`, *optional*, defaults to False):
+            Set to `True` for the query, key, and value linear layers to contain a bias parameter.
+    """
+
+    def __init__(
+        self,
+        query_dim: int,
+        cross_attention_dim: Optional[int] = None,
+        heads: int = 8,
+        dim_head: int = 64,
+        dropout: float = 0.0,
+        bias=False,
+        upcast_attention: bool = False,
+        upcast_softmax: bool = False,
+        added_kv_proj_dim: Optional[int] = None,
+        norm_num_groups: Optional[int] = None,
+    ):
+        super().__init__()
+        inner_dim = dim_head * heads
+        cross_attention_dim = cross_attention_dim if cross_attention_dim is not None else query_dim
+        self.upcast_attention = upcast_attention
+        self.upcast_softmax = upcast_softmax
+
+        self.scale = dim_head**-0.5
+
+        self.heads = heads
+        # for slice_size > 0 the attention score computation
+        # is split across the batch axis to save memory
+        # You can set slice_size with `set_attention_slice`
+        self.sliceable_head_dim = heads
+        self._slice_size = None
+        self._use_memory_efficient_attention_xformers = False
+        self.added_kv_proj_dim = added_kv_proj_dim
+
+        if norm_num_groups is not None:
+            self.group_norm = nn.GroupNorm(num_channels=inner_dim, num_groups=norm_num_groups, eps=1e-5, affine=True)
+        else:
+            self.group_norm = None
+
+        self.to_q = nn.Linear(query_dim, inner_dim, bias=bias)
+        self.to_k = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
+        self.to_v = nn.Linear(cross_attention_dim, inner_dim, bias=bias)
+
+        if self.added_kv_proj_dim is not None:
+            self.add_k_proj = nn.Linear(added_kv_proj_dim, cross_attention_dim)
+            self.add_v_proj = nn.Linear(added_kv_proj_dim, cross_attention_dim)
+
+        self.to_out = nn.ModuleList([])
+        self.to_out.append(nn.Linear(inner_dim, query_dim))
+        self.to_out.append(nn.Dropout(dropout))
+
+    def reshape_heads_to_batch_dim(self, tensor):
+        batch_size, seq_len, dim = tensor.shape
+        head_size = self.heads
+        tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
+        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size * head_size, seq_len, dim // head_size)
+        return tensor
+
+    def reshape_batch_dim_to_heads(self, tensor):
+        batch_size, seq_len, dim = tensor.shape
+        head_size = self.heads
+        tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
+        tensor = tensor.permute(0, 2, 1, 3).reshape(batch_size // head_size, seq_len, dim * head_size)
+        return tensor
+
+    def set_attention_slice(self, slice_size):
+        if slice_size is not None and slice_size > self.sliceable_head_dim:
+            raise ValueError(f"slice_size {slice_size} has to be smaller or equal to {self.sliceable_head_dim}.")
+
+        self._slice_size = slice_size
+
+    def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None):
+        batch_size, sequence_length, _ = hidden_states.shape
+
+        encoder_hidden_states = encoder_hidden_states
+
+        if self.group_norm is not None:
+            hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
+
+        query = self.to_q(hidden_states)
+        dim = query.shape[-1]
+        query = self.reshape_heads_to_batch_dim(query)
+
+        if self.added_kv_proj_dim is not None:
+            key = self.to_k(hidden_states)
+            value = self.to_v(hidden_states)
+            encoder_hidden_states_key_proj = self.add_k_proj(encoder_hidden_states)
+            encoder_hidden_states_value_proj = self.add_v_proj(encoder_hidden_states)
+
+            key = self.reshape_heads_to_batch_dim(key)
+            value = self.reshape_heads_to_batch_dim(value)
+            encoder_hidden_states_key_proj = self.reshape_heads_to_batch_dim(encoder_hidden_states_key_proj)
+            encoder_hidden_states_value_proj = self.reshape_heads_to_batch_dim(encoder_hidden_states_value_proj)
+
+            key = torch.concat([encoder_hidden_states_key_proj, key], dim=1)
+            value = torch.concat([encoder_hidden_states_value_proj, value], dim=1)
+        else:
+            encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
+            key = self.to_k(encoder_hidden_states)
+            value = self.to_v(encoder_hidden_states)
+
+            key = self.reshape_heads_to_batch_dim(key)
+            value = self.reshape_heads_to_batch_dim(value)
+
+        if attention_mask is not None:
+            if attention_mask.shape[-1] != query.shape[1]:
+                target_length = query.shape[1]
+                attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
+                attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
+
+        # attention, what we cannot get enough of
+        if self._use_memory_efficient_attention_xformers:
+            hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
+            # Some versions of xformers return output in fp32, cast it back to the dtype of the input
+            hidden_states = hidden_states.to(query.dtype)
+        else:
+            if self._slice_size is None or query.shape[0] // self._slice_size == 1:
+                hidden_states = self._attention(query, key, value, attention_mask)
+            else:
+                hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
+
+        # linear proj
+        hidden_states = self.to_out[0](hidden_states)
+
+        # dropout
+        hidden_states = self.to_out[1](hidden_states)
+        return hidden_states
+
+    def _attention(self, query, key, value, attention_mask=None):
+        if self.upcast_attention:
+            query = query.float()
+            key = key.float()
+
+        attention_scores = torch.baddbmm(
+            torch.empty(query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device),
+            query,
+            key.transpose(-1, -2),
+            beta=0,
+            alpha=self.scale,
+        )
+
+        if attention_mask is not None:
+            attention_scores = attention_scores + attention_mask
+
+        if self.upcast_softmax:
+            attention_scores = attention_scores.float()
+
+        attention_probs = attention_scores.softmax(dim=-1)
+
+        # cast back to the original dtype
+        attention_probs = attention_probs.to(value.dtype)
+
+        # compute attention output
+        hidden_states = torch.bmm(attention_probs, value)
+
+        # reshape hidden_states
+        hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
+        return hidden_states
+
+    def _sliced_attention(self, query, key, value, sequence_length, dim, attention_mask):
+        batch_size_attention = query.shape[0]
+        hidden_states = torch.zeros(
+            (batch_size_attention, sequence_length, dim // self.heads), device=query.device, dtype=query.dtype
+        )
+        slice_size = self._slice_size if self._slice_size is not None else hidden_states.shape[0]
+        for i in range(hidden_states.shape[0] // slice_size):
+            start_idx = i * slice_size
+            end_idx = (i + 1) * slice_size
+
+            query_slice = query[start_idx:end_idx]
+            key_slice = key[start_idx:end_idx]
+
+            if self.upcast_attention:
+                query_slice = query_slice.float()
+                key_slice = key_slice.float()
+
+            attn_slice = torch.baddbmm(
+                torch.empty(slice_size, query.shape[1], key.shape[1], dtype=query_slice.dtype, device=query.device),
+                query_slice,
+                key_slice.transpose(-1, -2),
+                beta=0,
+                alpha=self.scale,
+            )
+
+            if attention_mask is not None:
+                attn_slice = attn_slice + attention_mask[start_idx:end_idx]
+
+            if self.upcast_softmax:
+                attn_slice = attn_slice.float()
+
+            attn_slice = attn_slice.softmax(dim=-1)
+
+            # cast back to the original dtype
+            attn_slice = attn_slice.to(value.dtype)
+            attn_slice = torch.bmm(attn_slice, value[start_idx:end_idx])
+
+            hidden_states[start_idx:end_idx] = attn_slice
+
+        # reshape hidden_states
+        hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
+        return hidden_states
+
+    def _memory_efficient_attention_xformers(self, query, key, value, attention_mask):
+        # TODO attention_mask
+        query = query.contiguous()
+        key = key.contiguous()
+        value = value.contiguous()
+        hidden_states = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=attention_mask)
+        hidden_states = self.reshape_batch_dim_to_heads(hidden_states)
+        return hidden_states
+
+
+class FeedForward(nn.Module):
+    r"""
+    A feed-forward layer.
+
+    Parameters:
+        dim (`int`): The number of channels in the input.
+        dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
+        mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
+        dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        dim_out: Optional[int] = None,
+        mult: int = 4,
+        dropout: float = 0.0,
+        activation_fn: str = "geglu",
+    ):
+        super().__init__()
+        inner_dim = int(dim * mult)
+        dim_out = dim_out if dim_out is not None else dim
+
+        if activation_fn == "gelu":
+            act_fn = GELU(dim, inner_dim)
+        elif activation_fn == "geglu":
+            act_fn = GEGLU(dim, inner_dim)
+        elif activation_fn == "geglu-approximate":
+            act_fn = ApproximateGELU(dim, inner_dim)
+
+        self.net = nn.ModuleList([])
+        # project in
+        self.net.append(act_fn)
+        # project dropout
+        self.net.append(nn.Dropout(dropout))
+        # project out
+        self.net.append(nn.Linear(inner_dim, dim_out))
+
+    def forward(self, hidden_states):
+        for module in self.net:
+            hidden_states = module(hidden_states)
+        return hidden_states
+
+
+class GELU(nn.Module):
+    r"""
+    GELU activation function
+    """
+
+    def __init__(self, dim_in: int, dim_out: int):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out)
+
+    def gelu(self, gate):
+        if gate.device.type != "mps":
+            return F.gelu(gate)
+        # mps: gelu is not implemented for float16
+        return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype)
+
+    def forward(self, hidden_states):
+        hidden_states = self.proj(hidden_states)
+        hidden_states = self.gelu(hidden_states)
+        return hidden_states
+
+
+# feedforward
+class GEGLU(nn.Module):
+    r"""
+    A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202.
+
+    Parameters:
+        dim_in (`int`): The number of channels in the input.
+        dim_out (`int`): The number of channels in the output.
+    """
+
+    def __init__(self, dim_in: int, dim_out: int):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out * 2)
+
+    def gelu(self, gate):
+        if gate.device.type != "mps":
+            return F.gelu(gate)
+        # mps: gelu is not implemented for float16
+        return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype)
+
+    def forward(self, hidden_states):
+        hidden_states, gate = self.proj(hidden_states).chunk(2, dim=-1)
+        return hidden_states * self.gelu(gate)
+
+
+class ApproximateGELU(nn.Module):
+    """
+    The approximate form of Gaussian Error Linear Unit (GELU)
+
+    For more details, see section 2: https://arxiv.org/abs/1606.08415
+    """
+
+    def __init__(self, dim_in: int, dim_out: int):
+        super().__init__()
+        self.proj = nn.Linear(dim_in, dim_out)
+
+    def forward(self, x):
+        x = self.proj(x)
+        return x * torch.sigmoid(1.702 * x)
+
+
+class AdaLayerNorm(nn.Module):
+    """
+    Norm layer modified to incorporate timestep embeddings.
+    """
+
+    def __init__(self, embedding_dim, num_embeddings):
+        super().__init__()
+        self.emb = nn.Embedding(num_embeddings, embedding_dim)
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(embedding_dim, embedding_dim * 2)
+        self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False)
+
+    def forward(self, x, timestep):
+        emb = self.linear(self.silu(self.emb(timestep)))
+        scale, shift = torch.chunk(emb, 2)
+        x = self.norm(x) * (1 + scale) + shift
+        return x
+
+
+class DualTransformer2DModel(nn.Module):
+    """
+    Dual transformer wrapper that combines two `Transformer2DModel`s for mixed inference.
+
+    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 88): The number of channels in each head.
+        in_channels (`int`, *optional*):
+            Pass if the input is continuous. The number of channels in the input and output.
+        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
+        dropout (`float`, *optional*, defaults to 0.1): The dropout probability to use.
+        cross_attention_dim (`int`, *optional*): The number of encoder_hidden_states dimensions to use.
+        sample_size (`int`, *optional*): Pass if the input is discrete. The width of the latent images.
+            Note that this is fixed at training time as it is used for learning a number of position embeddings. See
+            `ImagePositionalEmbeddings`.
+        num_vector_embeds (`int`, *optional*):
+            Pass if the input is discrete. The number of classes of the vector embeddings of the latent pixels.
+            Includes the class for the masked latent pixel.
+        activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
+        num_embeds_ada_norm ( `int`, *optional*): Pass if at least one of the norm_layers is `AdaLayerNorm`.
+            The number of diffusion steps used during training. Note that this is fixed at training time as it is used
+            to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for
+            up to but not more than steps than `num_embeds_ada_norm`.
+        attention_bias (`bool`, *optional*):
+            Configure if the TransformerBlocks' attention should contain a bias parameter.
+    """
+
+    def __init__(
+        self,
+        num_attention_heads: int = 16,
+        attention_head_dim: int = 88,
+        in_channels: Optional[int] = None,
+        num_layers: int = 1,
+        dropout: float = 0.0,
+        norm_num_groups: int = 32,
+        cross_attention_dim: Optional[int] = None,
+        attention_bias: bool = False,
+        sample_size: Optional[int] = None,
+        num_vector_embeds: Optional[int] = None,
+        activation_fn: str = "geglu",
+        num_embeds_ada_norm: Optional[int] = None,
+    ):
+        super().__init__()
+        self.transformers = nn.ModuleList(
+            [
+                Transformer2DModel(
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                    in_channels=in_channels,
+                    num_layers=num_layers,
+                    dropout=dropout,
+                    norm_num_groups=norm_num_groups,
+                    cross_attention_dim=cross_attention_dim,
+                    attention_bias=attention_bias,
+                    sample_size=sample_size,
+                    num_vector_embeds=num_vector_embeds,
+                    activation_fn=activation_fn,
+                    num_embeds_ada_norm=num_embeds_ada_norm,
+                )
+                for _ in range(2)
+            ]
+        )
+
+        # Variables that can be set by a pipeline:
+
+        # The ratio of transformer1 to transformer2's output states to be combined during inference
+        self.mix_ratio = 0.5
+
+        # The shape of `encoder_hidden_states` is expected to be
+        # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
+        self.condition_lengths = [77, 257]
+
+        # Which transformer to use to encode which condition.
+        # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
+        self.transformer_index_for_condition = [1, 0]
+
+    def forward(
+        self, hidden_states, encoder_hidden_states, timestep=None, attention_mask=None, return_dict: bool = True
+    ):
+        """
+        Args:
+            hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`.
+                When continuous, `torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input
+                hidden_states
+            encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
+                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
+                self-attention.
+            timestep ( `torch.long`, *optional*):
+                Optional timestep to be applied as an embedding in AdaLayerNorm's. Used to indicate denoising step.
+            attention_mask (`torch.FloatTensor`, *optional*):
+                Optional attention mask to be applied in CrossAttention
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.attention.Transformer2DModelOutput`] or `tuple`: [`~models.attention.Transformer2DModelOutput`]
+            if `return_dict` is True, otherwise a `tuple`. When returning a tuple, the first element is the sample
+            tensor.
+        """
+        input_states = hidden_states
+
+        encoded_states = []
+        tokens_start = 0
+        # attention_mask is not used yet
+        for i in range(2):
+            # for each of the two transformers, pass the corresponding condition tokens
+            condition_state = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
+            transformer_index = self.transformer_index_for_condition[i]
+            encoded_state = self.transformers[transformer_index](
+                input_states,
+                encoder_hidden_states=condition_state,
+                timestep=timestep,
+                return_dict=False,
+            )[0]
+            encoded_states.append(encoded_state - input_states)
+            tokens_start += self.condition_lengths[i]
+
+        output_states = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
+        output_states = output_states + input_states
+
+        if not return_dict:
+            return (output_states,)
+
+        return Transformer2DModelOutput(sample=output_states)
\ No newline at end of file

magicanimate/models/resnet.py

+# *************************************************************************
+# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
+# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
+# ytedance Inc..  
+# *************************************************************************
+
+# Adapted from https://github.com/guoyww/AnimateDiff
+
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+# `TemporalConvLayer` Copyright 2023 Alibaba DAMO-VILAB, The ModelScope Team and 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.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from einops import rearrange
+
+
+class InflatedConv3d(nn.Conv2d):
+    def forward(self, x):
+        video_length = x.shape[2]
+
+        x = rearrange(x, "b c f h w -> (b f) c h w")
+        x = super().forward(x)
+        x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length)
+
+        return x
+
+
+class Upsample3D(nn.Module):
+    def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.use_conv_transpose = use_conv_transpose
+        self.name = name
+
+        conv = None
+        if use_conv_transpose:
+            raise NotImplementedError
+        elif use_conv:
+            self.conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1)
+
+    def forward(self, hidden_states, output_size=None):
+        assert hidden_states.shape[1] == self.channels
+
+        if self.use_conv_transpose:
+            raise NotImplementedError
+
+        # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16
+        dtype = hidden_states.dtype
+        if dtype == torch.bfloat16:
+            hidden_states = hidden_states.to(torch.float32)
+
+        # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984
+        if hidden_states.shape[0] >= 64:
+            hidden_states = hidden_states.contiguous()
+
+        # if `output_size` is passed we force the interpolation output
+        # size and do not make use of `scale_factor=2`
+        if output_size is None:
+            hidden_states = F.interpolate(hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest")
+        else:
+            hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest")
+
+        # If the input is bfloat16, we cast back to bfloat16
+        if dtype == torch.bfloat16:
+            hidden_states = hidden_states.to(dtype)
+
+        hidden_states = self.conv(hidden_states)
+
+        return hidden_states
+
+
+class Downsample3D(nn.Module):
+    def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"):
+        super().__init__()
+        self.channels = channels
+        self.out_channels = out_channels or channels
+        self.use_conv = use_conv
+        self.padding = padding
+        stride = 2
+        self.name = name
+
+        if use_conv:
+            self.conv = InflatedConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding)
+        else:
+            raise NotImplementedError
+
+    def forward(self, hidden_states):
+        assert hidden_states.shape[1] == self.channels
+        if self.use_conv and self.padding == 0:
+            raise NotImplementedError
+
+        assert hidden_states.shape[1] == self.channels
+        hidden_states = self.conv(hidden_states)
+
+        return hidden_states
+
+
+class ResnetBlock3D(nn.Module):
+    def __init__(
+        self,
+        *,
+        in_channels,
+        out_channels=None,
+        conv_shortcut=False,
+        dropout=0.0,
+        temb_channels=512,
+        groups=32,
+        groups_out=None,
+        pre_norm=True,
+        eps=1e-6,
+        non_linearity="swish",
+        time_embedding_norm="default",
+        output_scale_factor=1.0,
+        use_in_shortcut=None,
+    ):
+        super().__init__()
+        self.pre_norm = pre_norm
+        self.pre_norm = True
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+        self.use_conv_shortcut = conv_shortcut
+        self.time_embedding_norm = time_embedding_norm
+        self.output_scale_factor = output_scale_factor
+
+        if groups_out is None:
+            groups_out = groups
+
+        self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True)
+
+        self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+        if temb_channels is not None:
+            if self.time_embedding_norm == "default":
+                time_emb_proj_out_channels = out_channels
+            elif self.time_embedding_norm == "scale_shift":
+                time_emb_proj_out_channels = out_channels * 2
+            else:
+                raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ")
+
+            self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels)
+        else:
+            self.time_emb_proj = None
+
+        self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True)
+        self.dropout = torch.nn.Dropout(dropout)
+        self.conv2 = InflatedConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+        if non_linearity == "swish":
+            self.nonlinearity = lambda x: F.silu(x)
+        elif non_linearity == "mish":
+            self.nonlinearity = Mish()
+        elif non_linearity == "silu":
+            self.nonlinearity = nn.SiLU()
+
+        self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut
+
+        self.conv_shortcut = None
+        if self.use_in_shortcut:
+            self.conv_shortcut = InflatedConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, input_tensor, temb):
+        hidden_states = input_tensor
+
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+
+        hidden_states = self.conv1(hidden_states)
+
+        if temb is not None:
+            temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None, None]
+
+        if temb is not None and self.time_embedding_norm == "default":
+            hidden_states = hidden_states + temb
+
+        hidden_states = self.norm2(hidden_states)
+
+        if temb is not None and self.time_embedding_norm == "scale_shift":
+            scale, shift = torch.chunk(temb, 2, dim=1)
+            hidden_states = hidden_states * (1 + scale) + shift
+
+        hidden_states = self.nonlinearity(hidden_states)
+
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+
+        if self.conv_shortcut is not None:
+            input_tensor = self.conv_shortcut(input_tensor)
+
+        output_tensor = (input_tensor + hidden_states) / self.output_scale_factor
+
+        return output_tensor
+
+
+class Mish(torch.nn.Module):
+    def forward(self, hidden_states):
+        return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states))
\ No newline at end of file

magicanimate/models/stable_diffusion_controlnet_reference.py

+# *************************************************************************
+# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
+# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
+# ytedance Inc..  
+# *************************************************************************
+
+# Inspired by: https://github.com/Mikubill/sd-webui-controlnet/discussions/1236 and https://github.com/Mikubill/sd-webui-controlnet/discussions/1280
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import PIL.Image
+import torch
+
+from diffusers import StableDiffusionControlNetPipeline
+from diffusers.models import ControlNetModel
+from diffusers.models.attention import BasicTransformerBlock
+from diffusers.models.unet_2d_blocks import CrossAttnDownBlock2D, CrossAttnUpBlock2D, DownBlock2D, UpBlock2D
+from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.utils import logging
+from diffusers.utils.torch_utils import is_compiled_module, randn_tensor
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import cv2
+        >>> import torch
+        >>> import numpy as np
+        >>> from PIL import Image
+        >>> from diffusers import UniPCMultistepScheduler
+        >>> from diffusers.utils import load_image
+
+        >>> input_image = load_image("https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png")
+
+        >>> # get canny image
+        >>> image = cv2.Canny(np.array(input_image), 100, 200)
+        >>> image = image[:, :, None]
+        >>> image = np.concatenate([image, image, image], axis=2)
+        >>> canny_image = Image.fromarray(image)
+
+        >>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
+        >>> pipe = StableDiffusionControlNetReferencePipeline.from_pretrained(
+                "runwayml/stable-diffusion-v1-5",
+                controlnet=controlnet,
+                safety_checker=None,
+                torch_dtype=torch.float16
+                ).to('cuda:0')
+
+        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe_controlnet.scheduler.config)
+
+        >>> result_img = pipe(ref_image=input_image,
+                        prompt="1girl",
+                        image=canny_image,
+                        num_inference_steps=20,
+                        reference_attn=True,
+                        reference_adain=True).images[0]
+
+        >>> result_img.show()
+        ```
+"""
+
+
+def torch_dfs(model: torch.nn.Module):
+    result = [model]
+    for child in model.children():
+        result += torch_dfs(child)
+    return result
+
+
+class StableDiffusionControlNetReferencePipeline(StableDiffusionControlNetPipeline):
+    def prepare_ref_latents(self, refimage, batch_size, dtype, device, generator, do_classifier_free_guidance):
+        refimage = refimage.to(device=device, dtype=dtype)
+
+        # encode the mask image into latents space so we can concatenate it to the latents
+        if isinstance(generator, list):
+            ref_image_latents = [
+                self.vae.encode(refimage[i : i + 1]).latent_dist.sample(generator=generator[i])
+                for i in range(batch_size)
+            ]
+            ref_image_latents = torch.cat(ref_image_latents, dim=0)
+        else:
+            ref_image_latents = self.vae.encode(refimage).latent_dist.sample(generator=generator)
+        ref_image_latents = self.vae.config.scaling_factor * ref_image_latents
+
+        # duplicate mask and ref_image_latents for each generation per prompt, using mps friendly method
+        if ref_image_latents.shape[0] < batch_size:
+            if not batch_size % ref_image_latents.shape[0] == 0:
+                raise ValueError(
+                    "The passed images and the required batch size don't match. Images are supposed to be duplicated"
+                    f" to a total batch size of {batch_size}, but {ref_image_latents.shape[0]} images were passed."
+                    " Make sure the number of images that you pass is divisible by the total requested batch size."
+                )
+            ref_image_latents = ref_image_latents.repeat(batch_size // ref_image_latents.shape[0], 1, 1, 1)
+
+        ref_image_latents = torch.cat([ref_image_latents] * 2) if do_classifier_free_guidance else ref_image_latents
+
+        # aligning device to prevent device errors when concating it with the latent model input
+        ref_image_latents = ref_image_latents.to(device=device, dtype=dtype)
+        return ref_image_latents
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        image: Union[
+            torch.FloatTensor,
+            PIL.Image.Image,
+            np.ndarray,
+            List[torch.FloatTensor],
+            List[PIL.Image.Image],
+            List[np.ndarray],
+        ] = None,
+        ref_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_images_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: int = 1,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        guess_mode: bool = False,
+        attention_auto_machine_weight: float = 1.0,
+        gn_auto_machine_weight: float = 1.0,
+        style_fidelity: float = 0.5,
+        reference_attn: bool = True,
+        reference_adain: bool = True,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
+                instead.
+            image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,:
+                    `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`):
+                The ControlNet input condition. ControlNet uses this input condition to generate guidance to Unet. If
+                the type is specified as `Torch.FloatTensor`, it is passed to ControlNet as is. `PIL.Image.Image` can
+                also be accepted as an image. The dimensions of the output image defaults to `image`'s dimensions. If
+                height and/or width are passed, `image` is resized according to them. If multiple ControlNets are
+                specified in init, images must be passed as a list such that each element of the list can be correctly
+                batched for input to a single controlnet.
+            ref_image (`torch.FloatTensor`, `PIL.Image.Image`):
+                The Reference Control input condition. Reference Control uses this input condition to generate guidance to Unet. If
+                the type is specified as `Torch.FloatTensor`, it is passed to Reference Control as is. `PIL.Image.Image` can
+                also be accepted as an image.
+            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts not to guide the image generation. If not defined, one has to pass
+                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
+                less than `1`).
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            eta (`float`, *optional*, defaults to 0.0):
+                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
+                [`schedulers.DDIMScheduler`], will be ignored for others.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
+                to make generation deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
+                provided, text embeddings will be generated from `prompt` input argument.
+            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
+                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
+                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
+                argument.
+            output_type (`str`, *optional*, defaults to `"pil"`):
+                The output format of the generate image. Choose between
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
+                plain tuple.
+            callback (`Callable`, *optional*):
+                A function that will be called every `callback_steps` steps during inference. The function will be
+                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+            callback_steps (`int`, *optional*, defaults to 1):
+                The frequency at which the `callback` function will be called. If not specified, the callback will be
+                called at every step.
+            cross_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).
+            controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
+                The outputs of the controlnet are multiplied by `controlnet_conditioning_scale` before they are added
+                to the residual in the original unet. If multiple ControlNets are specified in init, you can set the
+                corresponding scale as a list.
+            guess_mode (`bool`, *optional*, defaults to `False`):
+                In this mode, the ControlNet encoder will try best to recognize the content of the input image even if
+                you remove all prompts. The `guidance_scale` between 3.0 and 5.0 is recommended.
+            attention_auto_machine_weight (`float`):
+                Weight of using reference query for self attention's context.
+                If attention_auto_machine_weight=1.0, use reference query for all self attention's context.
+            gn_auto_machine_weight (`float`):
+                Weight of using reference adain. If gn_auto_machine_weight=2.0, use all reference adain plugins.
+            style_fidelity (`float`):
+                style fidelity of ref_uncond_xt. If style_fidelity=1.0, control more important,
+                elif style_fidelity=0.0, prompt more important, else balanced.
+            reference_attn (`bool`):
+                Whether to use reference query for self attention's context.
+            reference_adain (`bool`):
+                Whether to use reference adain.
+
+        Examples:
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        assert reference_attn or reference_adain, "`reference_attn` or `reference_adain` must be True."
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            image,
+            callback_steps,
+            negative_prompt,
+            prompt_embeds,
+            negative_prompt_embeds,
+            controlnet_conditioning_scale,
+        )
+
+        # 2. Define call parameters
+        if prompt is not None and isinstance(prompt, str):
+            batch_size = 1
+        elif prompt is not None and isinstance(prompt, list):
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
+
+        if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
+            controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
+
+        global_pool_conditions = (
+            controlnet.config.global_pool_conditions
+            if isinstance(controlnet, ControlNetModel)
+            else controlnet.nets[0].config.global_pool_conditions
+        )
+        guess_mode = guess_mode or global_pool_conditions
+
+        # 3. Encode input prompt
+        text_encoder_lora_scale = (
+            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
+        )
+        prompt_embeds = self._encode_prompt(
+            prompt,
+            device,
+            num_images_per_prompt,
+            do_classifier_free_guidance,
+            negative_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            lora_scale=text_encoder_lora_scale,
+        )
+
+        # 4. Prepare image
+        if isinstance(controlnet, ControlNetModel):
+            image = self.prepare_image(
+                image=image,
+                width=width,
+                height=height,
+                batch_size=batch_size * num_images_per_prompt,
+                num_images_per_prompt=num_images_per_prompt,
+                device=device,
+                dtype=controlnet.dtype,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+                guess_mode=guess_mode,
+            )
+            height, width = image.shape[-2:]
+        elif isinstance(controlnet, MultiControlNetModel):
+            images = []
+
+            for image_ in image:
+                image_ = self.prepare_image(
+                    image=image_,
+                    width=width,
+                    height=height,
+                    batch_size=batch_size * num_images_per_prompt,
+                    num_images_per_prompt=num_images_per_prompt,
+                    device=device,
+                    dtype=controlnet.dtype,
+                    do_classifier_free_guidance=do_classifier_free_guidance,
+                    guess_mode=guess_mode,
+                )
+
+                images.append(image_)
+
+            image = images
+            height, width = image[0].shape[-2:]
+        else:
+            assert False
+
+        # 5. Preprocess reference image
+        ref_image = self.prepare_image(
+            image=ref_image,
+            width=width,
+            height=height,
+            batch_size=batch_size * num_images_per_prompt,
+            num_images_per_prompt=num_images_per_prompt,
+            device=device,
+            dtype=prompt_embeds.dtype,
+        )
+
+        # 6. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 7. Prepare latent variables
+        num_channels_latents = self.unet.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 8. Prepare reference latent variables
+        ref_image_latents = self.prepare_ref_latents(
+            ref_image,
+            batch_size * num_images_per_prompt,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            do_classifier_free_guidance,
+        )
+
+        # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # 10. Modify self attention and group norm
+        MODE = "write"
+        uc_mask = (
+            torch.Tensor([1] * batch_size * num_images_per_prompt + [0] * batch_size * num_images_per_prompt)
+            .type_as(ref_image_latents)
+            .bool()
+        )
+
+        def hacked_basic_transformer_inner_forward(
+            self,
+            hidden_states: torch.FloatTensor,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,
+            encoder_attention_mask: Optional[torch.FloatTensor] = None,
+            timestep: Optional[torch.LongTensor] = None,
+            cross_attention_kwargs: Dict[str, Any] = None,
+            class_labels: Optional[torch.LongTensor] = None,
+        ):
+            if self.use_ada_layer_norm:
+                norm_hidden_states = self.norm1(hidden_states, timestep)
+            elif self.use_ada_layer_norm_zero:
+                norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+                    hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype
+                )
+            else:
+                norm_hidden_states = self.norm1(hidden_states)
+
+            # 1. Self-Attention
+            cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {}
+            if self.only_cross_attention:
+                attn_output = self.attn1(
+                    norm_hidden_states,
+                    encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+                    attention_mask=attention_mask,
+                    **cross_attention_kwargs,
+                )
+            else:
+                if MODE == "write":
+                    self.bank.append(norm_hidden_states.detach().clone())
+                    attn_output = self.attn1(
+                        norm_hidden_states,
+                        encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+                        attention_mask=attention_mask,
+                        **cross_attention_kwargs,
+                    )
+                if MODE == "read":
+                    if attention_auto_machine_weight > self.attn_weight:
+                        attn_output_uc = self.attn1(
+                            norm_hidden_states,
+                            encoder_hidden_states=torch.cat([norm_hidden_states] + self.bank, dim=1),
+                            # attention_mask=attention_mask,
+                            **cross_attention_kwargs,
+                        )
+                        attn_output_c = attn_output_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            attn_output_c[uc_mask] = self.attn1(
+                                norm_hidden_states[uc_mask],
+                                encoder_hidden_states=norm_hidden_states[uc_mask],
+                                **cross_attention_kwargs,
+                            )
+                        attn_output = style_fidelity * attn_output_c + (1.0 - style_fidelity) * attn_output_uc
+                        self.bank.clear()
+                    else:
+                        attn_output = self.attn1(
+                            norm_hidden_states,
+                            encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None,
+                            attention_mask=attention_mask,
+                            **cross_attention_kwargs,
+                        )
+            if self.use_ada_layer_norm_zero:
+                attn_output = gate_msa.unsqueeze(1) * attn_output
+            hidden_states = attn_output + hidden_states
+
+            if self.attn2 is not None:
+                norm_hidden_states = (
+                    self.norm2(hidden_states, timestep) if self.use_ada_layer_norm else self.norm2(hidden_states)
+                )
+
+                # 2. Cross-Attention
+                attn_output = self.attn2(
+                    norm_hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=encoder_attention_mask,
+                    **cross_attention_kwargs,
+                )
+                hidden_states = attn_output + hidden_states
+
+            # 3. Feed-forward
+            norm_hidden_states = self.norm3(hidden_states)
+
+            if self.use_ada_layer_norm_zero:
+                norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+
+            ff_output = self.ff(norm_hidden_states)
+
+            if self.use_ada_layer_norm_zero:
+                ff_output = gate_mlp.unsqueeze(1) * ff_output
+
+            hidden_states = ff_output + hidden_states
+
+            return hidden_states
+
+        def hacked_mid_forward(self, *args, **kwargs):
+            eps = 1e-6
+            x = self.original_forward(*args, **kwargs)
+            if MODE == "write":
+                if gn_auto_machine_weight >= self.gn_weight:
+                    var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
+                    self.mean_bank.append(mean)
+                    self.var_bank.append(var)
+            if MODE == "read":
+                if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                    var, mean = torch.var_mean(x, dim=(2, 3), keepdim=True, correction=0)
+                    std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                    mean_acc = sum(self.mean_bank) / float(len(self.mean_bank))
+                    var_acc = sum(self.var_bank) / float(len(self.var_bank))
+                    std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                    x_uc = (((x - mean) / std) * std_acc) + mean_acc
+                    x_c = x_uc.clone()
+                    if do_classifier_free_guidance and style_fidelity > 0:
+                        x_c[uc_mask] = x[uc_mask]
+                    x = style_fidelity * x_c + (1.0 - style_fidelity) * x_uc
+                self.mean_bank = []
+                self.var_bank = []
+            return x
+
+        def hack_CrossAttnDownBlock2D_forward(
+            self,
+            hidden_states: torch.FloatTensor,
+            temb: Optional[torch.FloatTensor] = None,
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+            encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        ):
+            eps = 1e-6
+
+            # TODO(Patrick, William) - attention mask is not used
+            output_states = ()
+
+            for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    return_dict=False,
+                )[0]
+                if MODE == "write":
+                    if gn_auto_machine_weight >= self.gn_weight:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        self.mean_bank.append([mean])
+                        self.var_bank.append([var])
+                if MODE == "read":
+                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
+                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
+                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
+                        hidden_states_c = hidden_states_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            hidden_states_c[uc_mask] = hidden_states[uc_mask]
+                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
+
+                output_states = output_states + (hidden_states,)
+
+            if MODE == "read":
+                self.mean_bank = []
+                self.var_bank = []
+
+            if self.downsamplers is not None:
+                for downsampler in self.downsamplers:
+                    hidden_states = downsampler(hidden_states)
+
+                output_states = output_states + (hidden_states,)
+
+            return hidden_states, output_states
+
+        def hacked_DownBlock2D_forward(self, hidden_states, temb=None):
+            eps = 1e-6
+
+            output_states = ()
+
+            for i, resnet in enumerate(self.resnets):
+                hidden_states = resnet(hidden_states, temb)
+
+                if MODE == "write":
+                    if gn_auto_machine_weight >= self.gn_weight:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        self.mean_bank.append([mean])
+                        self.var_bank.append([var])
+                if MODE == "read":
+                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
+                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
+                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
+                        hidden_states_c = hidden_states_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            hidden_states_c[uc_mask] = hidden_states[uc_mask]
+                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
+
+                output_states = output_states + (hidden_states,)
+
+            if MODE == "read":
+                self.mean_bank = []
+                self.var_bank = []
+
+            if self.downsamplers is not None:
+                for downsampler in self.downsamplers:
+                    hidden_states = downsampler(hidden_states)
+
+                output_states = output_states + (hidden_states,)
+
+            return hidden_states, output_states
+
+        def hacked_CrossAttnUpBlock2D_forward(
+            self,
+            hidden_states: torch.FloatTensor,
+            res_hidden_states_tuple: Tuple[torch.FloatTensor, ...],
+            temb: Optional[torch.FloatTensor] = None,
+            encoder_hidden_states: Optional[torch.FloatTensor] = None,
+            cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+            upsample_size: Optional[int] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        ):
+            eps = 1e-6
+            # TODO(Patrick, William) - attention mask is not used
+            for i, (resnet, attn) in enumerate(zip(self.resnets, self.attentions)):
+                # pop res hidden states
+                res_hidden_states = res_hidden_states_tuple[-1]
+                res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+                hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    attention_mask=attention_mask,
+                    encoder_attention_mask=encoder_attention_mask,
+                    return_dict=False,
+                )[0]
+
+                if MODE == "write":
+                    if gn_auto_machine_weight >= self.gn_weight:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        self.mean_bank.append([mean])
+                        self.var_bank.append([var])
+                if MODE == "read":
+                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
+                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
+                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
+                        hidden_states_c = hidden_states_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            hidden_states_c[uc_mask] = hidden_states[uc_mask]
+                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
+
+            if MODE == "read":
+                self.mean_bank = []
+                self.var_bank = []
+
+            if self.upsamplers is not None:
+                for upsampler in self.upsamplers:
+                    hidden_states = upsampler(hidden_states, upsample_size)
+
+            return hidden_states
+
+        def hacked_UpBlock2D_forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None):
+            eps = 1e-6
+            for i, resnet in enumerate(self.resnets):
+                # pop res hidden states
+                res_hidden_states = res_hidden_states_tuple[-1]
+                res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+                hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+                hidden_states = resnet(hidden_states, temb)
+
+                if MODE == "write":
+                    if gn_auto_machine_weight >= self.gn_weight:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        self.mean_bank.append([mean])
+                        self.var_bank.append([var])
+                if MODE == "read":
+                    if len(self.mean_bank) > 0 and len(self.var_bank) > 0:
+                        var, mean = torch.var_mean(hidden_states, dim=(2, 3), keepdim=True, correction=0)
+                        std = torch.maximum(var, torch.zeros_like(var) + eps) ** 0.5
+                        mean_acc = sum(self.mean_bank[i]) / float(len(self.mean_bank[i]))
+                        var_acc = sum(self.var_bank[i]) / float(len(self.var_bank[i]))
+                        std_acc = torch.maximum(var_acc, torch.zeros_like(var_acc) + eps) ** 0.5
+                        hidden_states_uc = (((hidden_states - mean) / std) * std_acc) + mean_acc
+                        hidden_states_c = hidden_states_uc.clone()
+                        if do_classifier_free_guidance and style_fidelity > 0:
+                            hidden_states_c[uc_mask] = hidden_states[uc_mask]
+                        hidden_states = style_fidelity * hidden_states_c + (1.0 - style_fidelity) * hidden_states_uc
+
+            if MODE == "read":
+                self.mean_bank = []
+                self.var_bank = []
+
+            if self.upsamplers is not None:
+                for upsampler in self.upsamplers:
+                    hidden_states = upsampler(hidden_states, upsample_size)
+
+            return hidden_states
+
+        if reference_attn:
+            attn_modules = [module for module in torch_dfs(self.unet) if isinstance(module, BasicTransformerBlock)]
+            attn_modules = sorted(attn_modules, key=lambda x: -x.norm1.normalized_shape[0])
+
+            for i, module in enumerate(attn_modules):
+                module._original_inner_forward = module.forward
+                module.forward = hacked_basic_transformer_inner_forward.__get__(module, BasicTransformerBlock)
+                module.bank = []
+                module.attn_weight = float(i) / float(len(attn_modules))
+
+        if reference_adain:
+            gn_modules = [self.unet.mid_block]
+            self.unet.mid_block.gn_weight = 0
+
+            down_blocks = self.unet.down_blocks
+            for w, module in enumerate(down_blocks):
+                module.gn_weight = 1.0 - float(w) / float(len(down_blocks))
+                gn_modules.append(module)
+
+            up_blocks = self.unet.up_blocks
+            for w, module in enumerate(up_blocks):
+                module.gn_weight = float(w) / float(len(up_blocks))
+                gn_modules.append(module)
+
+            for i, module in enumerate(gn_modules):
+                if getattr(module, "original_forward", None) is None:
+                    module.original_forward = module.forward
+                if i == 0:
+                    # mid_block
+                    module.forward = hacked_mid_forward.__get__(module, torch.nn.Module)
+                elif isinstance(module, CrossAttnDownBlock2D):
+                    module.forward = hack_CrossAttnDownBlock2D_forward.__get__(module, CrossAttnDownBlock2D)
+                elif isinstance(module, DownBlock2D):
+                    module.forward = hacked_DownBlock2D_forward.__get__(module, DownBlock2D)
+                elif isinstance(module, CrossAttnUpBlock2D):
+                    module.forward = hacked_CrossAttnUpBlock2D_forward.__get__(module, CrossAttnUpBlock2D)
+                elif isinstance(module, UpBlock2D):
+                    module.forward = hacked_UpBlock2D_forward.__get__(module, UpBlock2D)
+                module.mean_bank = []
+                module.var_bank = []
+                module.gn_weight *= 2
+
+        # 11. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                # controlnet(s) inference
+                if guess_mode and do_classifier_free_guidance:
+                    # Infer ControlNet only for the conditional batch.
+                    control_model_input = latents
+                    control_model_input = self.scheduler.scale_model_input(control_model_input, t)
+                    controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
+                else:
+                    control_model_input = latent_model_input
+                    controlnet_prompt_embeds = prompt_embeds
+
+                down_block_res_samples, mid_block_res_sample = self.controlnet(
+                    control_model_input,
+                    t,
+                    encoder_hidden_states=controlnet_prompt_embeds,
+                    controlnet_cond=image,
+                    conditioning_scale=controlnet_conditioning_scale,
+                    guess_mode=guess_mode,
+                    return_dict=False,
+                )
+
+                if guess_mode and do_classifier_free_guidance:
+                    # Infered ControlNet only for the conditional batch.
+                    # To apply the output of ControlNet to both the unconditional and conditional batches,
+                    # add 0 to the unconditional batch to keep it unchanged.
+                    down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
+                    mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
+
+                # ref only part
+                noise = randn_tensor(
+                    ref_image_latents.shape, generator=generator, device=device, dtype=ref_image_latents.dtype
+                )
+                ref_xt = self.scheduler.add_noise(
+                    ref_image_latents,
+                    noise,
+                    t.reshape(
+                        1,
+                    ),
+                )
+                ref_xt = self.scheduler.scale_model_input(ref_xt, t)
+
+                MODE = "write"
+                self.unet(
+                    ref_xt,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    return_dict=False,
+                )
+
+                # predict the noise residual
+                MODE = "read"
+                noise_pred = self.unet(
+                    latent_model_input,
+                    t,
+                    encoder_hidden_states=prompt_embeds,
+                    cross_attention_kwargs=cross_attention_kwargs,
+                    down_block_additional_residuals=down_block_res_samples,
+                    mid_block_additional_residual=mid_block_res_sample,
+                    return_dict=False,
+                )[0]
+
+                # perform guidance
+                if do_classifier_free_guidance:
+                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
+
+                # call the callback, if provided
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+                    if callback is not None and i % callback_steps == 0:
+                        callback(i, t, latents)
+
+        # If we do sequential model offloading, let's offload unet and controlnet
+        # manually for max memory savings
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.unet.to("cpu")
+            self.controlnet.to("cpu")
+            torch.cuda.empty_cache()
+
+        if not output_type == "latent":
+            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
+            image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype)
+        else:
+            image = latents
+            has_nsfw_concept = None
+
+        if has_nsfw_concept is None:
+            do_denormalize = [True] * image.shape[0]
+        else:
+            do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept]
+
+        image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize)
+
+        # Offload last model to CPU
+        if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None:
+            self.final_offload_hook.offload()
+
+        if not return_dict:
+            return (image, has_nsfw_concept)
+
+        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)

magicanimate/models/unet.py

+# *************************************************************************
+# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
+# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
+# ytedance Inc..  
+# *************************************************************************
+
+# Adapted from https://github.com/guoyww/AnimateDiff
+
+# Copyright 2023 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 dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import os
+import json
+import pdb
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils import BaseOutput, logging
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+from .unet_3d_blocks import (
+    CrossAttnDownBlock3D,
+    CrossAttnUpBlock3D,
+    DownBlock3D,
+    UNetMidBlock3DCrossAttn,
+    UpBlock3D,
+    get_down_block,
+    get_up_block,
+)
+from .resnet import InflatedConv3d
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class UNet3DConditionOutput(BaseOutput):
+    sample: torch.FloatTensor
+
+
+class UNet3DConditionModel(ModelMixin, ConfigMixin):
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        sample_size: Optional[int] = None,
+        in_channels: int = 4,
+        out_channels: int = 4,
+        center_input_sample: bool = False,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,      
+        down_block_types: Tuple[str] = (
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "DownBlock3D",
+        ),
+        mid_block_type: str = "UNetMidBlock3DCrossAttn",
+        up_block_types: Tuple[str] = (
+            "UpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D"
+        ),
+        only_cross_attention: Union[bool, Tuple[bool]] = False,
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        layers_per_block: int = 2,
+        downsample_padding: int = 1,
+        mid_block_scale_factor: float = 1,
+        act_fn: str = "silu",
+        norm_num_groups: int = 32,
+        norm_eps: float = 1e-5,
+        cross_attention_dim: int = 1280,
+        attention_head_dim: Union[int, Tuple[int]] = 8,
+        dual_cross_attention: bool = False,
+        use_linear_projection: bool = False,
+        class_embed_type: Optional[str] = None,
+        num_class_embeds: Optional[int] = None,
+        upcast_attention: bool = False,
+        resnet_time_scale_shift: str = "default",
+        
+        # Additional
+        use_motion_module              = False,
+        motion_module_resolutions      = ( 1,2,4,8 ),
+        motion_module_mid_block        = False,
+        motion_module_decoder_only     = False,
+        motion_module_type             = None,
+        motion_module_kwargs           = {},
+        unet_use_cross_frame_attention = None,
+        unet_use_temporal_attention    = None,
+    ):
+        super().__init__()
+
+        self.sample_size = sample_size
+        time_embed_dim = block_out_channels[0] * 4
+
+        # input
+        self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
+
+        # time
+        self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+        timestep_input_dim = block_out_channels[0]
+
+        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+
+        # class embedding
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        else:
+            self.class_embedding = None
+
+        self.down_blocks = nn.ModuleList([])
+        self.mid_block = None
+        self.up_blocks = nn.ModuleList([])
+
+        if isinstance(only_cross_attention, bool):
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+        if isinstance(attention_head_dim, int):
+            attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+        # down
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            res = 2 ** i
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=attention_head_dim[i],
+                downsample_padding=downsample_padding,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+
+                unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                unet_use_temporal_attention=unet_use_temporal_attention,
+                
+                use_motion_module=use_motion_module and (res in motion_module_resolutions) and (not motion_module_decoder_only),
+                motion_module_type=motion_module_type,
+                motion_module_kwargs=motion_module_kwargs,
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        if mid_block_type == "UNetMidBlock3DCrossAttn":
+            self.mid_block = UNetMidBlock3DCrossAttn(
+                in_channels=block_out_channels[-1],
+                temb_channels=time_embed_dim,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=attention_head_dim[-1],
+                resnet_groups=norm_num_groups,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+
+                unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                unet_use_temporal_attention=unet_use_temporal_attention,
+                
+                use_motion_module=use_motion_module and motion_module_mid_block,
+                motion_module_type=motion_module_type,
+                motion_module_kwargs=motion_module_kwargs,
+            )
+        else:
+            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
+        
+        # count how many layers upsample the videos
+        self.num_upsamplers = 0
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        reversed_attention_head_dim = list(reversed(attention_head_dim))
+        only_cross_attention = list(reversed(only_cross_attention))
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            res = 2 ** (3 - i)
+            is_final_block = i == len(block_out_channels) - 1
+
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
+
+            # add upsample block for all BUT final layer
+            if not is_final_block:
+                add_upsample = True
+                self.num_upsamplers += 1
+            else:
+                add_upsample = False
+
+            up_block = get_up_block(
+                up_block_type,
+                num_layers=layers_per_block + 1,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                prev_output_channel=prev_output_channel,
+                temb_channels=time_embed_dim,
+                add_upsample=add_upsample,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=reversed_attention_head_dim[i],
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+
+                unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                unet_use_temporal_attention=unet_use_temporal_attention,
+
+                use_motion_module=use_motion_module and (res in motion_module_resolutions),
+                motion_module_type=motion_module_type,
+                motion_module_kwargs=motion_module_kwargs,
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
+        self.conv_act = nn.SiLU()
+        self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
+
+    def set_attention_slice(self, slice_size):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
+                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+                must be a multiple of `slice_size`.
+        """
+        sliceable_head_dims = []
+
+        def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                sliceable_head_dims.append(module.sliceable_head_dim)
+
+            for child in module.children():
+                fn_recursive_retrieve_slicable_dims(child)
+
+        # retrieve number of attention layers
+        for module in self.children():
+            fn_recursive_retrieve_slicable_dims(module)
+
+        num_slicable_layers = len(sliceable_head_dims)
+
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = [dim // 2 for dim in sliceable_head_dims]
+        elif slice_size == "max":
+            # make smallest slice possible
+            slice_size = num_slicable_layers * [1]
+
+        slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
+
+        if len(slice_size) != len(sliceable_head_dims):
+            raise ValueError(
+                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+            )
+
+        for i in range(len(slice_size)):
+            size = slice_size[i]
+            dim = sliceable_head_dims[i]
+            if size is not None and size > dim:
+                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+        # Recursively walk through all the children.
+        # Any children which exposes the set_attention_slice method
+        # gets the message
+        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
+            if hasattr(module, "set_attention_slice"):
+                module.set_attention_slice(slice_size.pop())
+
+            for child in module.children():
+                fn_recursive_set_attention_slice(child, slice_size)
+
+        reversed_slice_size = list(reversed(slice_size))
+        for module in self.children():
+            fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        class_labels: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ) -> Union[UNet3DConditionOutput, Tuple]:
+        r"""
+        Args:
+            sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
+            timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
+            encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is the sample tensor.
+        """
+        # By default samples have to be AT least a multiple of the overall upsampling factor.
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
+        # However, the upsampling interpolation output size can be forced to fit any upsampling size
+        # on the fly if necessary.
+        default_overall_up_factor = 2**self.num_upsamplers
+
+        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+        forward_upsample_size = False
+        upsample_size = None
+
+        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
+            logger.info("Forward upsample size to force interpolation output size.")
+            forward_upsample_size = True
+
+        # prepare attention_mask
+        if attention_mask is not None:
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # center input if necessary
+        if self.config.center_input_sample:
+            sample = 2 * sample - 1.0
+
+        # time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=self.dtype)
+        emb = self.time_embedding(t_emb)
+
+        if self.class_embedding is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
+            emb = emb + class_emb
+
+        # pre-process
+        sample = self.conv_in(sample)
+
+        # down
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                )
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states)
+
+            down_block_res_samples += res_samples
+
+        # mid
+        sample = self.mid_block(
+            sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
+        )
+
+        # up
+        for i, upsample_block in enumerate(self.up_blocks):
+            is_final_block = i == len(self.up_blocks) - 1
+
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+            # if we have not reached the final block and need to forward the
+            # upsample size, we do it here
+            if not is_final_block and forward_upsample_size:
+                upsample_size = down_block_res_samples[-1].shape[2:]
+
+            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states,
+                    upsample_size=upsample_size,
+                    attention_mask=attention_mask,
+                )
+            else:
+                sample = upsample_block(
+                    hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, encoder_hidden_states=encoder_hidden_states,
+                )
+
+        # post-process
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        if not return_dict:
+            return (sample,)
+
+        return UNet3DConditionOutput(sample=sample)
+
+    @classmethod
+    def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, unet_additional_kwargs=None):
+        if subfolder is not None:
+            pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
+        print(f"loaded temporal unet's pretrained weights from {pretrained_model_path} ...")
+
+        config_file = os.path.join(pretrained_model_path, 'config.json')
+        if not os.path.isfile(config_file):
+            raise RuntimeError(f"{config_file} does not exist")
+        with open(config_file, "r") as f:
+            config = json.load(f)
+        config["_class_name"] = cls.__name__
+        config["down_block_types"] = [
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "DownBlock3D"
+        ]
+        config["up_block_types"] = [
+            "UpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D"
+        ]
+
+        from diffusers.utils import WEIGHTS_NAME
+        model = cls.from_config(config, **unet_additional_kwargs)
+        model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
+        if not os.path.isfile(model_file):
+            raise RuntimeError(f"{model_file} does not exist")
+        state_dict = torch.load(model_file, map_location="cpu")
+
+        m, u = model.load_state_dict(state_dict, strict=False)
+        print(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
+        # print(f"### missing keys:\n{m}\n### unexpected keys:\n{u}\n")
+        
+        params = [p.numel() if "temporal" in n else 0 for n, p in model.named_parameters()]
+        print(f"### Temporal Module Parameters: {sum(params) / 1e6} M")
+        
+        return model

magicanimate/models/unet_3d_blocks.py

+# *************************************************************************
+# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
+# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
+# ytedance Inc..  
+# *************************************************************************
+
+# Adapted from https://github.com/guoyww/AnimateDiff
+
+# Copyright 2023 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.
+import torch
+from torch import nn
+
+from .attention import Transformer3DModel
+from .resnet import Downsample3D, ResnetBlock3D, Upsample3D
+from .motion_module import get_motion_module
+
+
+def get_down_block(
+    down_block_type,
+    num_layers,
+    in_channels,
+    out_channels,
+    temb_channels,
+    add_downsample,
+    resnet_eps,
+    resnet_act_fn,
+    attn_num_head_channels,
+    resnet_groups=None,
+    cross_attention_dim=None,
+    downsample_padding=None,
+    dual_cross_attention=False,
+    use_linear_projection=False,
+    only_cross_attention=False,
+    upcast_attention=False,
+    resnet_time_scale_shift="default",
+    
+    unet_use_cross_frame_attention=None,
+    unet_use_temporal_attention=None,
+    
+    use_motion_module=None,
+    
+    motion_module_type=None,
+    motion_module_kwargs=None,
+):
+    down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type
+    if down_block_type == "DownBlock3D":
+        return DownBlock3D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+
+            use_motion_module=use_motion_module,
+            motion_module_type=motion_module_type,
+            motion_module_kwargs=motion_module_kwargs,
+        )
+    elif down_block_type == "CrossAttnDownBlock3D":
+        if cross_attention_dim is None:
+            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D")
+        return CrossAttnDownBlock3D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            add_downsample=add_downsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            downsample_padding=downsample_padding,
+            cross_attention_dim=cross_attention_dim,
+            attn_num_head_channels=attn_num_head_channels,
+            dual_cross_attention=dual_cross_attention,
+            use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
+            upcast_attention=upcast_attention,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+
+            unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+            unet_use_temporal_attention=unet_use_temporal_attention,
+            
+            use_motion_module=use_motion_module,
+            motion_module_type=motion_module_type,
+            motion_module_kwargs=motion_module_kwargs,
+        )
+    raise ValueError(f"{down_block_type} does not exist.")
+
+
+def get_up_block(
+    up_block_type,
+    num_layers,
+    in_channels,
+    out_channels,
+    prev_output_channel,
+    temb_channels,
+    add_upsample,
+    resnet_eps,
+    resnet_act_fn,
+    attn_num_head_channels,
+    resnet_groups=None,
+    cross_attention_dim=None,
+    dual_cross_attention=False,
+    use_linear_projection=False,
+    only_cross_attention=False,
+    upcast_attention=False,
+    resnet_time_scale_shift="default",
+
+    unet_use_cross_frame_attention=None,
+    unet_use_temporal_attention=None,
+    
+    use_motion_module=None,
+    motion_module_type=None,
+    motion_module_kwargs=None,
+):
+    up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type
+    if up_block_type == "UpBlock3D":
+        return UpBlock3D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+
+            use_motion_module=use_motion_module,
+            motion_module_type=motion_module_type,
+            motion_module_kwargs=motion_module_kwargs,
+        )
+    elif up_block_type == "CrossAttnUpBlock3D":
+        if cross_attention_dim is None:
+            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D")
+        return CrossAttnUpBlock3D(
+            num_layers=num_layers,
+            in_channels=in_channels,
+            out_channels=out_channels,
+            prev_output_channel=prev_output_channel,
+            temb_channels=temb_channels,
+            add_upsample=add_upsample,
+            resnet_eps=resnet_eps,
+            resnet_act_fn=resnet_act_fn,
+            resnet_groups=resnet_groups,
+            cross_attention_dim=cross_attention_dim,
+            attn_num_head_channels=attn_num_head_channels,
+            dual_cross_attention=dual_cross_attention,
+            use_linear_projection=use_linear_projection,
+            only_cross_attention=only_cross_attention,
+            upcast_attention=upcast_attention,
+            resnet_time_scale_shift=resnet_time_scale_shift,
+
+            unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+            unet_use_temporal_attention=unet_use_temporal_attention,
+
+            use_motion_module=use_motion_module,
+            motion_module_type=motion_module_type,
+            motion_module_kwargs=motion_module_kwargs,
+        )
+    raise ValueError(f"{up_block_type} does not exist.")
+
+
+class UNetMidBlock3DCrossAttn(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attn_num_head_channels=1,
+        output_scale_factor=1.0,
+        cross_attention_dim=1280,
+        dual_cross_attention=False,
+        use_linear_projection=False,
+        upcast_attention=False,
+
+        unet_use_cross_frame_attention=None,
+        unet_use_temporal_attention=None,
+
+        use_motion_module=None,
+        
+        motion_module_type=None,
+        motion_module_kwargs=None,
+    ):
+        super().__init__()
+
+        self.has_cross_attention = True
+        self.attn_num_head_channels = attn_num_head_channels
+        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
+
+        # there is always at least one resnet
+        resnets = [
+            ResnetBlock3D(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                temb_channels=temb_channels,
+                eps=resnet_eps,
+                groups=resnet_groups,
+                dropout=dropout,
+                time_embedding_norm=resnet_time_scale_shift,
+                non_linearity=resnet_act_fn,
+                output_scale_factor=output_scale_factor,
+                pre_norm=resnet_pre_norm,
+            )
+        ]
+        attentions = []
+        motion_modules = []
+
+        for _ in range(num_layers):
+            if dual_cross_attention:
+                raise NotImplementedError
+            attentions.append(
+                Transformer3DModel(
+                    attn_num_head_channels,
+                    in_channels // attn_num_head_channels,
+                    in_channels=in_channels,
+                    num_layers=1,
+                    cross_attention_dim=cross_attention_dim,
+                    norm_num_groups=resnet_groups,
+                    use_linear_projection=use_linear_projection,
+                    upcast_attention=upcast_attention,
+
+                    unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                    unet_use_temporal_attention=unet_use_temporal_attention,
+                )
+            )
+            motion_modules.append(
+                get_motion_module(
+                    in_channels=in_channels,
+                    motion_module_type=motion_module_type, 
+                    motion_module_kwargs=motion_module_kwargs,
+                ) if use_motion_module else None
+            )
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+        self.motion_modules = nn.ModuleList(motion_modules)
+
+    def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None):
+        hidden_states = self.resnets[0](hidden_states, temb)
+        for attn, resnet, motion_module in zip(self.attentions, self.resnets[1:], self.motion_modules):
+            hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
+            hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) if motion_module is not None else hidden_states
+            hidden_states = resnet(hidden_states, temb)
+
+        return hidden_states
+
+
+class CrossAttnDownBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attn_num_head_channels=1,
+        cross_attention_dim=1280,
+        output_scale_factor=1.0,
+        downsample_padding=1,
+        add_downsample=True,
+        dual_cross_attention=False,
+        use_linear_projection=False,
+        only_cross_attention=False,
+        upcast_attention=False,
+
+        unet_use_cross_frame_attention=None,
+        unet_use_temporal_attention=None,
+        
+        use_motion_module=None,
+
+        motion_module_type=None,
+        motion_module_kwargs=None,
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+        motion_modules = []
+
+        self.has_cross_attention = True
+        self.attn_num_head_channels = attn_num_head_channels
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            if dual_cross_attention:
+                raise NotImplementedError
+            attentions.append(
+                Transformer3DModel(
+                    attn_num_head_channels,
+                    out_channels // attn_num_head_channels,
+                    in_channels=out_channels,
+                    num_layers=1,
+                    cross_attention_dim=cross_attention_dim,
+                    norm_num_groups=resnet_groups,
+                    use_linear_projection=use_linear_projection,
+                    only_cross_attention=only_cross_attention,
+                    upcast_attention=upcast_attention,
+
+                    unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                    unet_use_temporal_attention=unet_use_temporal_attention,
+                )
+            )
+            motion_modules.append(
+                get_motion_module(
+                    in_channels=out_channels,
+                    motion_module_type=motion_module_type, 
+                    motion_module_kwargs=motion_module_kwargs,
+                ) if use_motion_module else None
+            )
+            
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+        self.motion_modules = nn.ModuleList(motion_modules)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [
+                    Downsample3D(
+                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None):
+        output_states = ()
+
+        for resnet, attn, motion_module in zip(self.resnets, self.attentions, self.motion_modules):
+            if self.training 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
+
+                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(attn, return_dict=False),
+                    hidden_states,
+                    encoder_hidden_states,
+                )[0]
+                if motion_module is not None:
+                    hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, encoder_hidden_states)
+                
+            else:
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
+                
+                # add motion module
+                hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) if motion_module is not None else hidden_states
+
+            output_states += (hidden_states,)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+            output_states += (hidden_states,)
+
+        return hidden_states, output_states
+
+
+class DownBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        output_scale_factor=1.0,
+        add_downsample=True,
+        downsample_padding=1,
+        
+        use_motion_module=None,
+        motion_module_type=None,
+        motion_module_kwargs=None,
+    ):
+        super().__init__()
+        resnets = []
+        motion_modules = []
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            motion_modules.append(
+                get_motion_module(
+                    in_channels=out_channels,
+                    motion_module_type=motion_module_type, 
+                    motion_module_kwargs=motion_module_kwargs,
+                ) if use_motion_module else None
+            )
+            
+        self.resnets = nn.ModuleList(resnets)
+        self.motion_modules = nn.ModuleList(motion_modules)
+
+        if add_downsample:
+            self.downsamplers = nn.ModuleList(
+                [
+                    Downsample3D(
+                        out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states, temb=None, encoder_hidden_states=None):
+        output_states = ()
+
+        for resnet, motion_module in zip(self.resnets, self.motion_modules):
+            if self.training and self.gradient_checkpointing:
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs)
+
+                    return custom_forward
+
+                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
+                if motion_module is not None:
+                    hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, encoder_hidden_states)
+            else:
+                hidden_states = resnet(hidden_states, temb)
+
+                # add motion module
+                hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) if motion_module is not None else hidden_states
+
+            output_states += (hidden_states,)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+            output_states += (hidden_states,)
+
+        return hidden_states, output_states
+
+
+class CrossAttnUpBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        prev_output_channel: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        attn_num_head_channels=1,
+        cross_attention_dim=1280,
+        output_scale_factor=1.0,
+        add_upsample=True,
+        dual_cross_attention=False,
+        use_linear_projection=False,
+        only_cross_attention=False,
+        upcast_attention=False,
+
+        unet_use_cross_frame_attention=None,
+        unet_use_temporal_attention=None,
+        
+        use_motion_module=None,
+
+        motion_module_type=None,
+        motion_module_kwargs=None,
+    ):
+        super().__init__()
+        resnets = []
+        attentions = []
+        motion_modules = []
+
+        self.has_cross_attention = True
+        self.attn_num_head_channels = attn_num_head_channels
+
+        for i in range(num_layers):
+            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+            resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=resnet_in_channels + res_skip_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            if dual_cross_attention:
+                raise NotImplementedError
+            attentions.append(
+                Transformer3DModel(
+                    attn_num_head_channels,
+                    out_channels // attn_num_head_channels,
+                    in_channels=out_channels,
+                    num_layers=1,
+                    cross_attention_dim=cross_attention_dim,
+                    norm_num_groups=resnet_groups,
+                    use_linear_projection=use_linear_projection,
+                    only_cross_attention=only_cross_attention,
+                    upcast_attention=upcast_attention,
+
+                    unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                    unet_use_temporal_attention=unet_use_temporal_attention,
+                )
+            )
+            motion_modules.append(
+                get_motion_module(
+                    in_channels=out_channels,
+                    motion_module_type=motion_module_type, 
+                    motion_module_kwargs=motion_module_kwargs,
+                ) if use_motion_module else None
+            )
+            
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+        self.motion_modules = nn.ModuleList(motion_modules)
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)])
+        else:
+            self.upsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states,
+        res_hidden_states_tuple,
+        temb=None,
+        encoder_hidden_states=None,
+        upsample_size=None,
+        attention_mask=None,
+    ):
+        for resnet, attn, motion_module in zip(self.resnets, self.attentions, self.motion_modules):
+            # pop res hidden states
+            res_hidden_states = res_hidden_states_tuple[-1]
+            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+            if self.training 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
+
+                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(attn, return_dict=False),
+                    hidden_states,
+                    encoder_hidden_states,
+                )[0]
+                if motion_module is not None:
+                    hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, encoder_hidden_states)
+            
+            else:
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample
+                
+                # add motion module
+                hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) if motion_module is not None else hidden_states
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states, upsample_size)
+
+        return hidden_states
+
+
+class UpBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        prev_output_channel: int,
+        out_channels: int,
+        temb_channels: int,
+        dropout: float = 0.0,
+        num_layers: int = 1,
+        resnet_eps: float = 1e-6,
+        resnet_time_scale_shift: str = "default",
+        resnet_act_fn: str = "swish",
+        resnet_groups: int = 32,
+        resnet_pre_norm: bool = True,
+        output_scale_factor=1.0,
+        add_upsample=True,
+
+        use_motion_module=None,
+        motion_module_type=None,
+        motion_module_kwargs=None,
+    ):
+        super().__init__()
+        resnets = []
+        motion_modules = []
+
+        for i in range(num_layers):
+            res_skip_channels = in_channels if (i == num_layers - 1) else out_channels
+            resnet_in_channels = prev_output_channel if i == 0 else out_channels
+
+            resnets.append(
+                ResnetBlock3D(
+                    in_channels=resnet_in_channels + res_skip_channels,
+                    out_channels=out_channels,
+                    temb_channels=temb_channels,
+                    eps=resnet_eps,
+                    groups=resnet_groups,
+                    dropout=dropout,
+                    time_embedding_norm=resnet_time_scale_shift,
+                    non_linearity=resnet_act_fn,
+                    output_scale_factor=output_scale_factor,
+                    pre_norm=resnet_pre_norm,
+                )
+            )
+            motion_modules.append(
+                get_motion_module(
+                    in_channels=out_channels,
+                    motion_module_type=motion_module_type, 
+                    motion_module_kwargs=motion_module_kwargs,
+                ) if use_motion_module else None
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+        self.motion_modules = nn.ModuleList(motion_modules)
+
+        if add_upsample:
+            self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)])
+        else:
+            self.upsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None, encoder_hidden_states=None,):
+        for resnet, motion_module in zip(self.resnets, self.motion_modules):
+            # pop res hidden states
+            res_hidden_states = res_hidden_states_tuple[-1]
+            res_hidden_states_tuple = res_hidden_states_tuple[:-1]
+            hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)
+
+            if self.training and self.gradient_checkpointing:
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs)
+
+                    return custom_forward
+
+                hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb)
+                if motion_module is not None:
+                    hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, encoder_hidden_states)
+            else:
+                hidden_states = resnet(hidden_states, temb)
+                hidden_states = motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) if motion_module is not None else hidden_states
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states, upsample_size)
+
+        return hidden_states
\ No newline at end of file

magicanimate/models/unet_controlnet.py

+# *************************************************************************
+# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
+# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
+# ytedance Inc..  
+# *************************************************************************
+
+# Copyright 2023 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 dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+
+import os
+import json
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils import BaseOutput, logging
+from diffusers.models.embeddings import TimestepEmbedding, Timesteps
+from magicanimate.models.unet_3d_blocks import (
+    CrossAttnDownBlock3D,
+    CrossAttnUpBlock3D,
+    DownBlock3D,
+    UNetMidBlock3DCrossAttn,
+    UpBlock3D,
+    get_down_block,
+    get_up_block,
+)
+from .resnet import InflatedConv3d
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class UNet3DConditionOutput(BaseOutput):
+    sample: torch.FloatTensor
+
+
+class UNet3DConditionModel(ModelMixin, ConfigMixin):
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        sample_size: Optional[int] = None,
+        in_channels: int = 4,
+        out_channels: int = 4,
+        center_input_sample: bool = False,
+        flip_sin_to_cos: bool = True,
+        freq_shift: int = 0,      
+        down_block_types: Tuple[str] = (
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "DownBlock3D",
+        ),
+        mid_block_type: str = "UNetMidBlock3DCrossAttn",
+        up_block_types: Tuple[str] = (
+            "UpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D"
+        ),
+        only_cross_attention: Union[bool, Tuple[bool]] = False,
+        block_out_channels: Tuple[int] = (320, 640, 1280, 1280),
+        layers_per_block: int = 2,
+        downsample_padding: int = 1,
+        mid_block_scale_factor: float = 1,
+        act_fn: str = "silu",
+        norm_num_groups: int = 32,
+        norm_eps: float = 1e-5,
+        cross_attention_dim: int = 1280,
+        attention_head_dim: Union[int, Tuple[int]] = 8,
+        dual_cross_attention: bool = False,
+        use_linear_projection: bool = False,
+        class_embed_type: Optional[str] = None,
+        num_class_embeds: Optional[int] = None,
+        upcast_attention: bool = False,
+        resnet_time_scale_shift: str = "default",
+        
+        # Additional
+        use_motion_module              = False,
+        motion_module_resolutions      = ( 1,2,4,8 ),
+        motion_module_mid_block        = False,
+        motion_module_decoder_only     = False,
+        motion_module_type             = None,
+        motion_module_kwargs           = {},
+        unet_use_cross_frame_attention = None,
+        unet_use_temporal_attention    = None,
+    ):
+        super().__init__()
+
+        self.sample_size = sample_size
+        time_embed_dim = block_out_channels[0] * 4
+
+        # input
+        self.conv_in = InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))
+
+        # time
+        self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift)
+        timestep_input_dim = block_out_channels[0]
+
+        self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+
+        # class embedding
+        if class_embed_type is None and num_class_embeds is not None:
+            self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim)
+        elif class_embed_type == "timestep":
+            self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim)
+        elif class_embed_type == "identity":
+            self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim)
+        else:
+            self.class_embedding = None
+
+        self.down_blocks = nn.ModuleList([])
+        self.mid_block = None
+        self.up_blocks = nn.ModuleList([])
+
+        if isinstance(only_cross_attention, bool):
+            only_cross_attention = [only_cross_attention] * len(down_block_types)
+
+        if isinstance(attention_head_dim, int):
+            attention_head_dim = (attention_head_dim,) * len(down_block_types)
+
+        # down
+        output_channel = block_out_channels[0]
+        for i, down_block_type in enumerate(down_block_types):
+            res = 2 ** i
+            input_channel = output_channel
+            output_channel = block_out_channels[i]
+            is_final_block = i == len(block_out_channels) - 1
+
+            down_block = get_down_block(
+                down_block_type,
+                num_layers=layers_per_block,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                temb_channels=time_embed_dim,
+                add_downsample=not is_final_block,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=attention_head_dim[i],
+                downsample_padding=downsample_padding,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+
+                unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                unet_use_temporal_attention=unet_use_temporal_attention,
+                
+                use_motion_module=use_motion_module and (res in motion_module_resolutions) and (not motion_module_decoder_only),
+                motion_module_type=motion_module_type,
+                motion_module_kwargs=motion_module_kwargs,
+            )
+            self.down_blocks.append(down_block)
+
+        # mid
+        if mid_block_type == "UNetMidBlock3DCrossAttn":
+            self.mid_block = UNetMidBlock3DCrossAttn(
+                in_channels=block_out_channels[-1],
+                temb_channels=time_embed_dim,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                output_scale_factor=mid_block_scale_factor,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=attention_head_dim[-1],
+                resnet_groups=norm_num_groups,
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                upcast_attention=upcast_attention,
+
+                unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                unet_use_temporal_attention=unet_use_temporal_attention,
+                
+                use_motion_module=use_motion_module and motion_module_mid_block,
+                motion_module_type=motion_module_type,
+                motion_module_kwargs=motion_module_kwargs,
+            )
+        else:
+            raise ValueError(f"unknown mid_block_type : {mid_block_type}")
+        
+        # count how many layers upsample the videos
+        self.num_upsamplers = 0
+
+        # up
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        reversed_attention_head_dim = list(reversed(attention_head_dim))
+        only_cross_attention = list(reversed(only_cross_attention))
+        output_channel = reversed_block_out_channels[0]
+        for i, up_block_type in enumerate(up_block_types):
+            res = 2 ** (3 - i)
+            is_final_block = i == len(block_out_channels) - 1
+
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+            input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
+
+            # add upsample block for all BUT final layer
+            if not is_final_block:
+                add_upsample = True
+                self.num_upsamplers += 1
+            else:
+                add_upsample = False
+
+            up_block = get_up_block(
+                up_block_type,
+                num_layers=layers_per_block + 1,
+                in_channels=input_channel,
+                out_channels=output_channel,
+                prev_output_channel=prev_output_channel,
+                temb_channels=time_embed_dim,
+                add_upsample=add_upsample,
+                resnet_eps=norm_eps,
+                resnet_act_fn=act_fn,
+                resnet_groups=norm_num_groups,
+                cross_attention_dim=cross_attention_dim,
+                attn_num_head_channels=reversed_attention_head_dim[i],
+                dual_cross_attention=dual_cross_attention,
+                use_linear_projection=use_linear_projection,
+                only_cross_attention=only_cross_attention[i],
+                upcast_attention=upcast_attention,
+                resnet_time_scale_shift=resnet_time_scale_shift,
+
+                unet_use_cross_frame_attention=unet_use_cross_frame_attention,
+                unet_use_temporal_attention=unet_use_temporal_attention,
+
+                use_motion_module=use_motion_module and (res in motion_module_resolutions),
+                motion_module_type=motion_module_type,
+                motion_module_kwargs=motion_module_kwargs,
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        # out
+        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps)
+        self.conv_act = nn.SiLU()
+        self.conv_out = InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
+
+    def set_attention_slice(self, slice_size):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is
+                provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim`
+                must be a multiple of `slice_size`.
+        """
+        sliceable_head_dims = []
+
+        def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module):
+            if hasattr(module, "set_attention_slice"):
+                sliceable_head_dims.append(module.sliceable_head_dim)
+
+            for child in module.children():
+                fn_recursive_retrieve_slicable_dims(child)
+
+        # retrieve number of attention layers
+        for module in self.children():
+            fn_recursive_retrieve_slicable_dims(module)
+
+        num_slicable_layers = len(sliceable_head_dims)
+
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = [dim // 2 for dim in sliceable_head_dims]
+        elif slice_size == "max":
+            # make smallest slice possible
+            slice_size = num_slicable_layers * [1]
+
+        slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size
+
+        if len(slice_size) != len(sliceable_head_dims):
+            raise ValueError(
+                f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different"
+                f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}."
+            )
+
+        for i in range(len(slice_size)):
+            size = slice_size[i]
+            dim = sliceable_head_dims[i]
+            if size is not None and size > dim:
+                raise ValueError(f"size {size} has to be smaller or equal to {dim}.")
+
+        # Recursively walk through all the children.
+        # Any children which exposes the set_attention_slice method
+        # gets the message
+        def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]):
+            if hasattr(module, "set_attention_slice"):
+                module.set_attention_slice(slice_size.pop())
+
+            for child in module.children():
+                fn_recursive_set_attention_slice(child, slice_size)
+
+        reversed_slice_size = list(reversed(slice_size))
+        for module in self.children():
+            fn_recursive_set_attention_slice(module, reversed_slice_size)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):
+            module.gradient_checkpointing = value
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        class_labels: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        # for controlnet
+        down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
+        mid_block_additional_residual: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ) -> Union[UNet3DConditionOutput, Tuple]:
+        r"""
+        Args:
+            sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor
+            timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps
+            encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`:
+            [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When
+            returning a tuple, the first element is the sample tensor.
+        """
+        # By default samples have to be AT least a multiple of the overall upsampling factor.
+        # The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
+        # However, the upsampling interpolation output size can be forced to fit any upsampling size
+        # on the fly if necessary.
+        default_overall_up_factor = 2**self.num_upsamplers
+
+        # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
+        forward_upsample_size = False
+        upsample_size = None
+
+        if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
+            logger.info("Forward upsample size to force interpolation output size.")
+            forward_upsample_size = True
+
+        # prepare attention_mask
+        if attention_mask is not None:
+            attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0
+            attention_mask = attention_mask.unsqueeze(1)
+
+        # center input if necessary
+        if self.config.center_input_sample:
+            sample = 2 * sample - 1.0
+
+        # time
+        timesteps = timestep
+        if not torch.is_tensor(timesteps):
+            # This would be a good case for the `match` statement (Python 3.10+)
+            is_mps = sample.device.type == "mps"
+            if isinstance(timestep, float):
+                dtype = torch.float32 if is_mps else torch.float64
+            else:
+                dtype = torch.int32 if is_mps else torch.int64
+            timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
+        elif len(timesteps.shape) == 0:
+            timesteps = timesteps[None].to(sample.device)
+
+        # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
+        timesteps = timesteps.expand(sample.shape[0])
+
+        t_emb = self.time_proj(timesteps)
+
+        # timesteps does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=self.dtype)
+        emb = self.time_embedding(t_emb)
+
+        if self.class_embedding is not None:
+            if class_labels is None:
+                raise ValueError("class_labels should be provided when num_class_embeds > 0")
+
+            if self.config.class_embed_type == "timestep":
+                class_labels = self.time_proj(class_labels)
+
+            class_emb = self.class_embedding(class_labels).to(dtype=self.dtype)
+            emb = emb + class_emb
+
+        # pre-process
+        sample = self.conv_in(sample)
+
+        # down
+        is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None
+
+        down_block_res_samples = (sample,)
+        for downsample_block in self.down_blocks:
+            if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
+                sample, res_samples = downsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    encoder_hidden_states=encoder_hidden_states,
+                    attention_mask=attention_mask,
+                )
+            else:
+                sample, res_samples = downsample_block(hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states)
+
+            down_block_res_samples += res_samples
+
+        if is_controlnet:
+            new_down_block_res_samples = ()
+
+            for down_block_res_sample, down_block_additional_residual in zip(
+                down_block_res_samples, down_block_additional_residuals
+            ):
+                down_block_res_sample = down_block_res_sample + down_block_additional_residual
+                new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,)
+
+            down_block_res_samples = new_down_block_res_samples
+
+        # mid
+        sample = self.mid_block(
+            sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask
+        )
+
+        if is_controlnet:
+            sample = sample + mid_block_additional_residual
+
+        # up
+        for i, upsample_block in enumerate(self.up_blocks):
+            is_final_block = i == len(self.up_blocks) - 1
+
+            res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
+            down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
+
+            # if we have not reached the final block and need to forward the
+            # upsample size, we do it here
+            if not is_final_block and forward_upsample_size:
+                upsample_size = down_block_res_samples[-1].shape[2:]
+
+            if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
+                sample = upsample_block(
+                    hidden_states=sample,
+                    temb=emb,
+                    res_hidden_states_tuple=res_samples,
+                    encoder_hidden_states=encoder_hidden_states,
+                    upsample_size=upsample_size,
+                    attention_mask=attention_mask,
+                )
+            else:
+                sample = upsample_block(
+                    hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, encoder_hidden_states=encoder_hidden_states,
+                )
+
+        # post-process
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        if not return_dict:
+            return (sample,)
+
+        return UNet3DConditionOutput(sample=sample)
+
+    @classmethod
+    def from_pretrained_2d(cls, pretrained_model_path, subfolder=None, unet_additional_kwargs=None):
+        if subfolder is not None:
+            pretrained_model_path = os.path.join(pretrained_model_path, subfolder)
+        print(f"loaded temporal unet's pretrained weights from {pretrained_model_path} ...")
+
+        config_file = os.path.join(pretrained_model_path, 'config.json')
+        if not os.path.isfile(config_file):
+            raise RuntimeError(f"{config_file} does not exist")
+        with open(config_file, "r") as f:
+            config = json.load(f)
+        config["_class_name"] = cls.__name__
+        config["down_block_types"] = [
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "CrossAttnDownBlock3D",
+            "DownBlock3D"
+        ]
+        config["up_block_types"] = [
+            "UpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D",
+            "CrossAttnUpBlock3D"
+        ]
+        # config["mid_block_type"] = "UNetMidBlock3DCrossAttn"
+
+        from diffusers.utils import WEIGHTS_NAME
+        model = cls.from_config(config, **unet_additional_kwargs)
+        model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
+        if not os.path.isfile(model_file):
+            raise RuntimeError(f"{model_file} does not exist")
+        state_dict = torch.load(model_file, map_location="cpu")
+
+        m, u = model.load_state_dict(state_dict, strict=False)
+        print(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
+        # print(f"### missing keys:\n{m}\n### unexpected keys:\n{u}\n")
+        
+        params = [p.numel() if "temporal" in n else 0 for n, p in model.named_parameters()]
+        print(f"### Temporal Module Parameters: {sum(params) / 1e6} M")
+        
+        return model

magicanimate/pipelines/animation.py

+# Copyright 2023 ByteDance and/or its affiliates.
+#
+# Copyright (2023) MagicAnimate Authors
+#
+# ByteDance, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from ByteDance or
+# its affiliates is strictly prohibited.
+import argparse
+import datetime
+import inspect
+import os
+import random
+import numpy as np
+
+from PIL import Image
+from omegaconf import OmegaConf
+from collections import OrderedDict
+
+import torch
+import torch.distributed as dist
+
+from diffusers import AutoencoderKL, DDIMScheduler, UniPCMultistepScheduler
+
+from tqdm import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from magicanimate.models.unet_controlnet import UNet3DConditionModel
+from magicanimate.models.controlnet import ControlNetModel
+from magicanimate.models.appearance_encoder import AppearanceEncoderModel
+from magicanimate.models.mutual_self_attention import ReferenceAttentionControl
+from magicanimate.pipelines.pipeline_animation import AnimationPipeline
+from magicanimate.utils.util import save_videos_grid
+from magicanimate.utils.dist_tools import distributed_init
+from accelerate.utils import set_seed
+
+from magicanimate.utils.videoreader import VideoReader
+
+from einops import rearrange
+
+from pathlib import Path
+
+
+def main(args):
+
+    *_, func_args = inspect.getargvalues(inspect.currentframe())
+    func_args = dict(func_args)
+    
+    config  = OmegaConf.load(args.config)
+      
+    # Initialize distributed training
+    device = torch.device(f"cuda:{args.rank}")
+    dist_kwargs = {"rank":args.rank, "world_size":args.world_size, "dist":args.dist}
+    
+    if config.savename is None:
+        time_str = datetime.datetime.now().strftime("%Y-%m-%dT%H-%M-%S")
+        savedir = f"samples/{Path(args.config).stem}-{time_str}"
+    else:
+        savedir = f"samples/{config.savename}"
+        
+    if args.dist:
+        dist.broadcast_object_list([savedir], 0)
+        dist.barrier()
+    
+    if args.rank == 0:
+        os.makedirs(savedir, exist_ok=True)
+
+    inference_config = OmegaConf.load(config.inference_config)
+        
+    motion_module = config.motion_module
+    
+    ### >>> create animation pipeline >>> ###
+    tokenizer = CLIPTokenizer.from_pretrained(config.pretrained_model_path, subfolder="tokenizer")
+    text_encoder = CLIPTextModel.from_pretrained(config.pretrained_model_path, subfolder="text_encoder")
+    if config.pretrained_unet_path:
+        unet = UNet3DConditionModel.from_pretrained_2d(config.pretrained_unet_path, unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs))
+    else:
+        unet = UNet3DConditionModel.from_pretrained_2d(config.pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs))
+    appearance_encoder = AppearanceEncoderModel.from_pretrained(config.pretrained_appearance_encoder_path, subfolder="appearance_encoder").to(device)
+    reference_control_writer = ReferenceAttentionControl(appearance_encoder, do_classifier_free_guidance=True, mode='write', fusion_blocks=config.fusion_blocks)
+    reference_control_reader = ReferenceAttentionControl(unet, do_classifier_free_guidance=True, mode='read', fusion_blocks=config.fusion_blocks)
+    if config.pretrained_vae_path is not None:
+        vae = AutoencoderKL.from_pretrained(config.pretrained_vae_path)
+    else:
+        vae = AutoencoderKL.from_pretrained(config.pretrained_model_path, subfolder="vae")
+
+    ### Load controlnet
+    controlnet   = ControlNetModel.from_pretrained(config.pretrained_controlnet_path)
+
+    # DCU环境下无法使用
+    # unet.enable_xformers_memory_efficient_attention()
+    # appearance_encoder.enable_xformers_memory_efficient_attention()
+    # controlnet.enable_xformers_memory_efficient_attention()
+
+    vae.to(torch.float16)
+    unet.to(torch.float16)
+    text_encoder.to(torch.float16)
+    appearance_encoder.to(torch.float16)
+    controlnet.to(torch.float16)
+
+    pipeline = AnimationPipeline(
+        vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, controlnet=controlnet,
+        scheduler=DDIMScheduler(**OmegaConf.to_container(inference_config.noise_scheduler_kwargs)),
+        # NOTE: UniPCMultistepScheduler
+    )
+
+    # 1. unet ckpt
+    # 1.1 motion module
+    motion_module_state_dict = torch.load(motion_module, map_location="cpu")
+    if "global_step" in motion_module_state_dict: func_args.update({"global_step": motion_module_state_dict["global_step"]})
+    motion_module_state_dict = motion_module_state_dict['state_dict'] if 'state_dict' in motion_module_state_dict else motion_module_state_dict
+    try:
+        # extra steps for self-trained models
+        state_dict = OrderedDict()
+        for key in motion_module_state_dict.keys():
+            if key.startswith("module."):
+                _key = key.split("module.")[-1]
+                state_dict[_key] = motion_module_state_dict[key]
+            else:
+                state_dict[key] = motion_module_state_dict[key]
+        motion_module_state_dict = state_dict
+        del state_dict
+        missing, unexpected = pipeline.unet.load_state_dict(motion_module_state_dict, strict=False)
+        assert len(unexpected) == 0
+    except:
+        _tmp_ = OrderedDict()
+        for key in motion_module_state_dict.keys():
+            if "motion_modules" in key:
+                if key.startswith("unet."):
+                    _key = key.split('unet.')[-1]
+                    _tmp_[_key] = motion_module_state_dict[key]
+                else:
+                    _tmp_[key] = motion_module_state_dict[key]
+        missing, unexpected = unet.load_state_dict(_tmp_, strict=False)
+        assert len(unexpected) == 0
+        del _tmp_
+    del motion_module_state_dict
+
+    pipeline.to(device)
+    ### <<< create validation pipeline <<< ###
+    
+    random_seeds = config.get("seed", [-1])
+    random_seeds = [random_seeds] if isinstance(random_seeds, int) else list(random_seeds)
+    random_seeds = random_seeds * len(config.source_image) if len(random_seeds) == 1 else random_seeds
+    
+    # input test videos (either source video/ conditions)
+    
+    test_videos = config.video_path
+    source_images = config.source_image
+    num_actual_inference_steps = config.get("num_actual_inference_steps", config.steps)
+
+    # read size, step from yaml file
+    sizes = [config.size] * len(test_videos)
+    steps = [config.S] * len(test_videos)
+
+    config.random_seed = []
+    prompt = n_prompt = ""
+    for idx, (source_image, test_video, random_seed, size, step) in tqdm(
+        enumerate(zip(source_images, test_videos, random_seeds, sizes, steps)), 
+        total=len(test_videos), 
+        disable=(args.rank!=0)
+    ):
+        samples_per_video = []
+        samples_per_clip = []
+        # manually set random seed for reproduction
+        if random_seed != -1: 
+            torch.manual_seed(random_seed)
+            set_seed(random_seed)
+        else:
+            torch.seed()
+        config.random_seed.append(torch.initial_seed())
+
+        if test_video.endswith('.mp4'):
+            control = VideoReader(test_video).read()
+            if control[0].shape[0] != size:
+                control = [np.array(Image.fromarray(c).resize((size, size))) for c in control]
+            if config.max_length is not None:
+                control = control[config.offset: (config.offset+config.max_length)]
+            control = np.array(control)
+        
+        if source_image.endswith(".mp4"):
+            source_image = np.array(Image.fromarray(VideoReader(source_image).read()[0]).resize((size, size)))
+        else:
+            source_image = np.array(Image.open(source_image).resize((size, size)))
+        H, W, C = source_image.shape
+        
+        print(f"current seed: {torch.initial_seed()}")
+        init_latents = None
+        
+        # print(f"sampling {prompt} ...")
+        original_length = control.shape[0]
+        if control.shape[0] % config.L > 0:
+            control = np.pad(control, ((0, config.L-control.shape[0] % config.L), (0, 0), (0, 0), (0, 0)), mode='edge')
+        generator = torch.Generator(device=torch.device("cuda:0"))
+        generator.manual_seed(torch.initial_seed())
+        sample = pipeline(
+            prompt,
+            negative_prompt         = n_prompt,
+            num_inference_steps     = config.steps,
+            guidance_scale          = config.guidance_scale,
+            width                   = W,
+            height                  = H,
+            video_length            = len(control),
+            controlnet_condition    = control,
+            init_latents            = init_latents,
+            generator               = generator,
+            num_actual_inference_steps = num_actual_inference_steps,
+            appearance_encoder       = appearance_encoder, 
+            reference_control_writer = reference_control_writer,
+            reference_control_reader = reference_control_reader,
+            source_image             = source_image,
+            **dist_kwargs,
+        ).videos
+
+        if args.rank == 0:
+            source_images = np.array([source_image] * original_length)
+            source_images = rearrange(torch.from_numpy(source_images), "t h w c -> 1 c t h w") / 255.0
+            samples_per_video.append(source_images)
+            
+            control = control / 255.0
+            control = rearrange(control, "t h w c -> 1 c t h w")
+            control = torch.from_numpy(control)
+            samples_per_video.append(control[:, :, :original_length])
+
+            samples_per_video.append(sample[:, :, :original_length])
+                
+            samples_per_video = torch.cat(samples_per_video)
+
+            video_name = os.path.basename(test_video)[:-4]
+            source_name = os.path.basename(config.source_image[idx]).split(".")[0]
+            save_videos_grid(samples_per_video[-1:], f"{savedir}/videos/{source_name}_{video_name}.mp4")
+            save_videos_grid(samples_per_video, f"{savedir}/videos/{source_name}_{video_name}/grid.mp4")
+
+            if config.save_individual_videos:
+                save_videos_grid(samples_per_video[1:2], f"{savedir}/videos/{source_name}_{video_name}/ctrl.mp4")
+                save_videos_grid(samples_per_video[0:1], f"{savedir}/videos/{source_name}_{video_name}/orig.mp4")
+                
+        if args.dist:
+            dist.barrier()
+               
+    if args.rank == 0:
+        OmegaConf.save(config, f"{savedir}/config.yaml")
+
+
+def distributed_main(device_id, args):
+    args.rank = device_id
+    args.device_id = device_id
+    if torch.cuda.is_available():
+        torch.cuda.set_device(args.device_id)
+        torch.cuda.init()
+    distributed_init(args)
+    main(args)
+
+
+def run(args):
+
+    if args.dist:
+        args.world_size = max(1, torch.cuda.device_count())
+        assert args.world_size <= torch.cuda.device_count()
+
+        if args.world_size > 0 and torch.cuda.device_count() > 1:
+            port = random.randint(10000, 20000)
+            args.init_method = f"tcp://localhost:{port}"
+            torch.multiprocessing.spawn(
+                fn=distributed_main,
+                args=(args,),
+                nprocs=args.world_size,
+            )
+    else:
+        main(args)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--dist", action="store_true", required=False)
+    parser.add_argument("--rank", type=int, default=0, required=False)
+    parser.add_argument("--world_size", type=int, default=1, required=False)
+
+    args = parser.parse_args()
+    run(args)

magicanimate/pipelines/context.py

+# *************************************************************************
+# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
+# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
+# ytedance Inc..  
+# *************************************************************************
+
+# Adapted from https://github.com/s9roll7/animatediff-cli-prompt-travel/tree/main
+import numpy as np
+from typing import Callable, Optional, List
+
+
+def ordered_halving(val):
+    bin_str = f"{val:064b}"
+    bin_flip = bin_str[::-1]
+    as_int = int(bin_flip, 2)
+
+    return as_int / (1 << 64)
+
+
+def uniform(
+    step: int = ...,
+    num_steps: Optional[int] = None,
+    num_frames: int = ...,
+    context_size: Optional[int] = None,
+    context_stride: int = 3,
+    context_overlap: int = 4,
+    closed_loop: bool = True,
+):
+    if num_frames <= context_size:
+        yield list(range(num_frames))
+        return
+
+    context_stride = min(context_stride, int(np.ceil(np.log2(num_frames / context_size))) + 1)
+
+    for context_step in 1 << np.arange(context_stride):
+        pad = int(round(num_frames * ordered_halving(step)))
+        for j in range(
+            int(ordered_halving(step) * context_step) + pad,
+            num_frames + pad + (0 if closed_loop else -context_overlap),
+            (context_size * context_step - context_overlap),
+        ):
+            yield [e % num_frames for e in range(j, j + context_size * context_step, context_step)]
+
+
+def get_context_scheduler(name: str) -> Callable:
+    if name == "uniform":
+        return uniform
+    else:
+        raise ValueError(f"Unknown context_overlap policy {name}")
+
+
+def get_total_steps(
+    scheduler,
+    timesteps: List[int],
+    num_steps: Optional[int] = None,
+    num_frames: int = ...,
+    context_size: Optional[int] = None,
+    context_stride: int = 3,
+    context_overlap: int = 4,
+    closed_loop: bool = True,
+):
+    return sum(
+        len(
+            list(
+                scheduler(
+                    i,
+                    num_steps,
+                    num_frames,
+                    context_size,
+                    context_stride,
+                    context_overlap,
+                )
+            )
+        )
+        for i in range(len(timesteps))
+    )

magicanimate/pipelines/pipeline_animation.py

+# *************************************************************************
+# This file may have been modified by Bytedance Inc. (“Bytedance Inc.'s Mo-
+# difications”). All Bytedance Inc.'s Modifications are Copyright (2023) B-
+# ytedance Inc..  
+# *************************************************************************
+
+# Adapted from https://github.com/showlab/Tune-A-Video/blob/main/tuneavideo/pipelines/pipeline_tuneavideo.py
+
+# Copyright 2023 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.
+"""
+TODO:
+1. support multi-controlnet
+2. [DONE] support DDIM inversion
+3. support Prompt-to-prompt
+"""
+
+import inspect, math
+from typing import Callable, List, Optional, Union
+from dataclasses import dataclass
+from PIL import Image
+import numpy as np
+import torch
+import torch.distributed as dist
+from tqdm import tqdm
+from diffusers.utils import is_accelerate_available
+from packaging import version
+from transformers import CLIPTextModel, CLIPTokenizer
+
+from diffusers.configuration_utils import FrozenDict
+from diffusers.models import AutoencoderKL
+from diffusers.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers import (
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+)
+from diffusers.utils import deprecate, logging, BaseOutput
+
+from einops import rearrange
+
+from magicanimate.models.unet_controlnet import UNet3DConditionModel
+from magicanimate.models.controlnet import ControlNetModel
+from magicanimate.models.mutual_self_attention import ReferenceAttentionControl
+from magicanimate.pipelines.context import (
+    get_context_scheduler,
+    get_total_steps
+)
+from magicanimate.utils.util import get_tensor_interpolation_method
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class AnimationPipelineOutput(BaseOutput):
+    videos: Union[torch.Tensor, np.ndarray]
+
+
+class AnimationPipeline(DiffusionPipeline):
+    _optional_components = []
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet3DConditionModel,
+        controlnet: ControlNetModel,
+        scheduler: Union[
+            DDIMScheduler,
+            PNDMScheduler,
+            LMSDiscreteScheduler,
+            EulerDiscreteScheduler,
+            EulerAncestralDiscreteScheduler,
+            DPMSolverMultistepScheduler,
+        ],
+    ):
+        super().__init__()
+
+        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
+                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
+                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
+                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
+                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
+                " file"
+            )
+            deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["steps_offset"] = 1
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True:
+            deprecation_message = (
+                f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`."
+                " `clip_sample` should be set to False in the configuration file. Please make sure to update the"
+                " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in"
+                " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very"
+                " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file"
+            )
+            deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(scheduler.config)
+            new_config["clip_sample"] = False
+            scheduler._internal_dict = FrozenDict(new_config)
+
+        is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse(
+            version.parse(unet.config._diffusers_version).base_version
+        ) < version.parse("0.9.0.dev0")
+        is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64
+        if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64:
+            deprecation_message = (
+                "The configuration file of the unet has set the default `sample_size` to smaller than"
+                " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
+                " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
+                " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
+                " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
+                " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
+                " in the config might lead to incorrect results in future versions. If you have downloaded this"
+                " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
+                " the `unet/config.json` file"
+            )
+            deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False)
+            new_config = dict(unet.config)
+            new_config["sample_size"] = 64
+            unet._internal_dict = FrozenDict(new_config)
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            controlnet=controlnet,
+            scheduler=scheduler,
+        )
+        self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
+
+    def enable_vae_slicing(self):
+        self.vae.enable_slicing()
+
+    def disable_vae_slicing(self):
+        self.vae.disable_slicing()
+
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        if is_accelerate_available():
+            from accelerate import cpu_offload
+        else:
+            raise ImportError("Please install accelerate via `pip install accelerate`")
+
+        device = torch.device(f"cuda:{gpu_id}")
+
+        for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]:
+            if cpu_offloaded_model is not None:
+                cpu_offload(cpu_offloaded_model, device)
+
+
+    @property
+    def _execution_device(self):
+        if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"):
+            return self.device
+        for module in self.unet.modules():
+            if (
+                hasattr(module, "_hf_hook")
+                and hasattr(module._hf_hook, "execution_device")
+                and module._hf_hook.execution_device is not None
+            ):
+                return torch.device(module._hf_hook.execution_device)
+        return self.device
+
+    def _encode_prompt(self, prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt):
+        batch_size = len(prompt) if isinstance(prompt, list) else 1
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
+
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
+            )
+
+        if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+            attention_mask = text_inputs.attention_mask.to(device)
+        else:
+            attention_mask = None
+
+        text_embeddings = self.text_encoder(
+            text_input_ids.to(device),
+            attention_mask=attention_mask,
+        )
+        text_embeddings = text_embeddings[0]
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        bs_embed, seq_len, _ = text_embeddings.shape
+        text_embeddings = text_embeddings.repeat(1, num_videos_per_prompt, 1)
+        text_embeddings = text_embeddings.view(bs_embed * num_videos_per_prompt, seq_len, -1)
+
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            uncond_tokens: List[str]
+            if negative_prompt is None:
+                uncond_tokens = [""] * batch_size
+            elif type(prompt) is not type(negative_prompt):
+                raise TypeError(
+                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
+                    f" {type(prompt)}."
+                )
+            elif isinstance(negative_prompt, str):
+                uncond_tokens = [negative_prompt]
+            elif batch_size != len(negative_prompt):
+                raise ValueError(
+                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
+                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
+                    " the batch size of `prompt`."
+                )
+            else:
+                uncond_tokens = negative_prompt
+
+            max_length = text_input_ids.shape[-1]
+            uncond_input = self.tokenizer(
+                uncond_tokens,
+                padding="max_length",
+                max_length=max_length,
+                truncation=True,
+                return_tensors="pt",
+            )
+
+            if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
+                attention_mask = uncond_input.attention_mask.to(device)
+            else:
+                attention_mask = None
+
+            uncond_embeddings = self.text_encoder(
+                uncond_input.input_ids.to(device),
+                attention_mask=attention_mask,
+            )
+            uncond_embeddings = uncond_embeddings[0]
+
+            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
+            seq_len = uncond_embeddings.shape[1]
+            uncond_embeddings = uncond_embeddings.repeat(1, num_videos_per_prompt, 1)
+            uncond_embeddings = uncond_embeddings.view(batch_size * num_videos_per_prompt, seq_len, -1)
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+
+        return text_embeddings
+
+    def decode_latents(self, latents, rank, decoder_consistency=None):
+        video_length = latents.shape[2]
+        latents = 1 / 0.18215 * latents
+        latents = rearrange(latents, "b c f h w -> (b f) c h w")
+        # video = self.vae.decode(latents).sample
+        video = []
+        for frame_idx in tqdm(range(latents.shape[0]), disable=(rank!=0)):
+            if decoder_consistency is not None:
+                video.append(decoder_consistency(latents[frame_idx:frame_idx+1]))
+            else:
+                video.append(self.vae.decode(latents[frame_idx:frame_idx+1]).sample)
+        video = torch.cat(video)
+        video = rearrange(video, "(b f) c h w -> b c f h w", f=video_length)
+        video = (video / 2 + 0.5).clamp(0, 1)
+        # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
+        video = video.cpu().float().numpy()
+        return video
+
+    def prepare_extra_step_kwargs(self, generator, eta):
+        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
+        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
+        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
+        # and should be between [0, 1]
+
+        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        extra_step_kwargs = {}
+        if accepts_eta:
+            extra_step_kwargs["eta"] = eta
+
+        # check if the scheduler accepts generator
+        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
+        if accepts_generator:
+            extra_step_kwargs["generator"] = generator
+        return extra_step_kwargs
+
+    def check_inputs(self, prompt, height, width, callback_steps):
+        if not isinstance(prompt, str) and not isinstance(prompt, list):
+            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if (callback_steps is None) or (
+            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
+        ):
+            raise ValueError(
+                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
+                f" {type(callback_steps)}."
+            )
+
+    def prepare_latents(self, batch_size, num_channels_latents, video_length, height, width, dtype, device, generator, latents=None, clip_length=16):
+        shape = (batch_size, num_channels_latents, clip_length, height // self.vae_scale_factor, width // self.vae_scale_factor)
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+        if latents is None:
+            rand_device = "cpu" if device.type == "mps" else device
+
+            if isinstance(generator, list):
+                latents = [
+                    torch.randn(shape, generator=generator[i], device=rand_device, dtype=dtype)
+                    for i in range(batch_size)
+                ]
+                latents = torch.cat(latents, dim=0).to(device)
+            else:
+                latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device)
+                
+            latents = latents.repeat(1, 1, video_length//clip_length, 1, 1)
+        else:
+            if latents.shape != shape:
+                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}")
+            latents = latents.to(device)
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents = latents * self.scheduler.init_noise_sigma
+        return latents
+
+    def prepare_condition(self, condition, num_videos_per_prompt, device, dtype, do_classifier_free_guidance):
+        # prepare conditions for controlnet
+        condition = torch.from_numpy(condition.copy()).to(device=device, dtype=dtype) / 255.0
+        condition = torch.stack([condition for _ in range(num_videos_per_prompt)], dim=0)
+        condition = rearrange(condition, 'b f h w c -> (b f) c h w').clone()
+        if do_classifier_free_guidance:
+            condition = torch.cat([condition] * 2)
+        return condition
+
+    def next_step(
+        self,
+        model_output: torch.FloatTensor,
+        timestep: int,
+        x: torch.FloatTensor,
+        eta=0.,
+        verbose=False
+    ):
+        """
+        Inverse sampling for DDIM Inversion
+        """
+        if verbose:
+            print("timestep: ", timestep)
+        next_step = timestep
+        timestep = min(timestep - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps, 999)
+        alpha_prod_t = self.scheduler.alphas_cumprod[timestep] if timestep >= 0 else self.scheduler.final_alpha_cumprod
+        alpha_prod_t_next = self.scheduler.alphas_cumprod[next_step]
+        beta_prod_t = 1 - alpha_prod_t
+        pred_x0 = (x - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5
+        pred_dir = (1 - alpha_prod_t_next)**0.5 * model_output
+        x_next = alpha_prod_t_next**0.5 * pred_x0 + pred_dir
+        return x_next, pred_x0
+
+    @torch.no_grad()
+    def images2latents(self, images, dtype):
+        """
+        Convert RGB image to VAE latents
+        """
+        device = self._execution_device
+        images = torch.from_numpy(images).float().to(dtype) / 127.5 - 1
+        images = rearrange(images, "f h w c -> f c h w").to(device)
+        latents = []
+        for frame_idx in range(images.shape[0]):
+            latents.append(self.vae.encode(images[frame_idx:frame_idx+1])['latent_dist'].mean * 0.18215)
+        latents = torch.cat(latents)
+        return latents
+
+    @torch.no_grad()
+    def invert(
+        self,
+        image: torch.Tensor,
+        prompt,
+        num_inference_steps=20,
+        num_actual_inference_steps=10,
+        eta=0.0,
+        return_intermediates=False,
+        **kwargs):
+        """
+        Adapted from: https://github.com/Yujun-Shi/DragDiffusion/blob/main/drag_pipeline.py#L440
+        invert a real image into noise map with determinisc DDIM inversion
+        """
+        device = self._execution_device
+        batch_size = image.shape[0]
+        if isinstance(prompt, list):
+            if batch_size == 1:
+                image = image.expand(len(prompt), -1, -1, -1)
+        elif isinstance(prompt, str):
+            if batch_size > 1:
+                prompt = [prompt] * batch_size
+
+        # text embeddings
+        text_input = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=77,
+            return_tensors="pt"
+        )
+        text_embeddings = self.text_encoder(text_input.input_ids.to(device))[0]
+        print("input text embeddings :", text_embeddings.shape)
+        # define initial latents
+        latents = self.images2latents(image)
+
+        print("latents shape: ", latents.shape)
+        # interative sampling
+        self.scheduler.set_timesteps(num_inference_steps)
+        print("Valid timesteps: ", reversed(self.scheduler.timesteps))
+        latents_list = [latents]
+        pred_x0_list = [latents]
+        for i, t in enumerate(tqdm(reversed(self.scheduler.timesteps), desc="DDIM Inversion")):
+
+            if num_actual_inference_steps is not None and i >= num_actual_inference_steps:
+                continue
+            model_inputs = latents
+
+            # predict the noise
+            # NOTE: the u-net here is UNet3D, therefore the model_inputs need to be of shape (b c f h w)
+            model_inputs = rearrange(model_inputs, "f c h w -> 1 c f h w")
+            noise_pred = self.unet(model_inputs, t, encoder_hidden_states=text_embeddings).sample
+            noise_pred = rearrange(noise_pred, "b c f h w -> (b f) c h w")
+            
+            # compute the previous noise sample x_t-1 -> x_t
+            latents, pred_x0 = self.next_step(noise_pred, t, latents)
+            latents_list.append(latents)
+            pred_x0_list.append(pred_x0)
+
+        if return_intermediates:
+            # return the intermediate laters during inversion
+            return latents, latents_list
+        return latents
+    
+    def interpolate_latents(self, latents: torch.Tensor, interpolation_factor:int, device ):
+        if interpolation_factor < 2:
+            return latents
+
+        new_latents = torch.zeros(
+                    (latents.shape[0],latents.shape[1],((latents.shape[2]-1) * interpolation_factor)+1, latents.shape[3],latents.shape[4]),
+                    device=latents.device,
+                    dtype=latents.dtype,
+                )
+
+        org_video_length = latents.shape[2]
+        rate = [i/interpolation_factor for i in range(interpolation_factor)][1:]
+
+        new_index = 0
+
+        v0 = None
+        v1 = None
+
+        for i0,i1 in zip( range( org_video_length ),range( org_video_length )[1:] ):
+            v0 = latents[:,:,i0,:,:]
+            v1 = latents[:,:,i1,:,:]
+
+            new_latents[:,:,new_index,:,:] = v0
+            new_index += 1
+
+            for f in rate:
+                v = get_tensor_interpolation_method()(v0.to(device=device),v1.to(device=device),f)
+                new_latents[:,:,new_index,:,:] = v.to(latents.device)
+                new_index += 1
+
+        new_latents[:,:,new_index,:,:] = v1
+        new_index += 1
+
+        return new_latents
+    
+    def select_controlnet_res_samples(self, controlnet_res_samples_cache_dict, context, do_classifier_free_guidance, b, f):
+        _down_block_res_samples = []
+        _mid_block_res_sample = []
+        for i in np.concatenate(np.array(context)):
+            _down_block_res_samples.append(controlnet_res_samples_cache_dict[i][0])
+            _mid_block_res_sample.append(controlnet_res_samples_cache_dict[i][1])
+        down_block_res_samples = [[] for _ in range(len(controlnet_res_samples_cache_dict[i][0]))]
+        for res_t in _down_block_res_samples:
+            for i, res in enumerate(res_t):
+                down_block_res_samples[i].append(res)
+        down_block_res_samples = [torch.cat(res) for res in down_block_res_samples]
+        mid_block_res_sample = torch.cat(_mid_block_res_sample)
+        
+        # reshape controlnet output to match the unet3d inputs
+        b = b // 2 if do_classifier_free_guidance else b
+        _down_block_res_samples = []
+        for sample in down_block_res_samples:
+            sample = rearrange(sample, '(b f) c h w -> b c f h w', b=b, f=f)
+            if do_classifier_free_guidance:
+                sample = sample.repeat(2, 1, 1, 1, 1)
+            _down_block_res_samples.append(sample)
+        down_block_res_samples = _down_block_res_samples
+        mid_block_res_sample = rearrange(mid_block_res_sample, '(b f) c h w -> b c f h w', b=b, f=f)
+        if do_classifier_free_guidance:
+            mid_block_res_sample = mid_block_res_sample.repeat(2, 1, 1, 1, 1)
+            
+        return down_block_res_samples, mid_block_res_sample
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        video_length: Optional[int],
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 7.5,
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        num_videos_per_prompt: Optional[int] = 1,
+        eta: float = 0.0,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "tensor",
+        return_dict: bool = True,
+        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
+        callback_steps: Optional[int] = 1,
+        controlnet_condition: list = None,
+        controlnet_conditioning_scale: float = 1.0,
+        context_frames: int = 16,
+        context_stride: int = 1,
+        context_overlap: int = 4,
+        context_batch_size: int = 1, 
+        context_schedule: str = "uniform",
+        init_latents: Optional[torch.FloatTensor] = None,
+        num_actual_inference_steps: Optional[int] = None,
+        appearance_encoder = None, 
+        reference_control_writer = None,
+        reference_control_reader = None,
+        source_image: str = None,
+        decoder_consistency = None, 
+        **kwargs,
+    ):
+        """
+        New args:
+        - controlnet_condition          : condition map (e.g., depth, canny, keypoints) for controlnet
+        - controlnet_conditioning_scale : conditioning scale for controlnet
+        - init_latents                  : initial latents to begin with (used along with invert())
+        - num_actual_inference_steps    : number of actual inference steps (while total steps is num_inference_steps) 
+        """
+        controlnet = self.controlnet
+
+        # Default height and width to unet
+        height = height or self.unet.config.sample_size * self.vae_scale_factor
+        width = width or self.unet.config.sample_size * self.vae_scale_factor
+
+        # Check inputs. Raise error if not correct
+        self.check_inputs(prompt, height, width, callback_steps)
+
+        # Define call parameters
+        # batch_size = 1 if isinstance(prompt, str) else len(prompt)
+        batch_size = 1
+        if latents is not None:
+            batch_size = latents.shape[0]
+        if isinstance(prompt, list):
+            batch_size = len(prompt)
+
+        device = self._execution_device
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # Encode input prompt
+        prompt = prompt if isinstance(prompt, list) else [prompt] * batch_size
+        if negative_prompt is not None:
+            negative_prompt = negative_prompt if isinstance(negative_prompt, list) else [negative_prompt] * batch_size 
+        text_embeddings = self._encode_prompt(
+            prompt, device, num_videos_per_prompt, do_classifier_free_guidance, negative_prompt
+        )
+        text_embeddings = torch.cat([text_embeddings] * context_batch_size)
+        
+        reference_control_writer = ReferenceAttentionControl(appearance_encoder, do_classifier_free_guidance=True, mode='write', batch_size=context_batch_size)
+        reference_control_reader = ReferenceAttentionControl(self.unet, do_classifier_free_guidance=True, mode='read', batch_size=context_batch_size)
+        
+        is_dist_initialized = kwargs.get("dist", False)
+        rank = kwargs.get("rank", 0)
+        world_size = kwargs.get("world_size", 1)
+
+        # Prepare video
+        assert num_videos_per_prompt == 1   # FIXME: verify if num_videos_per_prompt > 1 works
+        assert batch_size == 1              # FIXME: verify if batch_size > 1 works
+        control = self.prepare_condition(
+                condition=controlnet_condition,
+                device=device,
+                dtype=controlnet.dtype,
+                num_videos_per_prompt=num_videos_per_prompt,
+                do_classifier_free_guidance=do_classifier_free_guidance,
+            )
+        controlnet_uncond_images, controlnet_cond_images = control.chunk(2)
+
+        # Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # Prepare latent variables
+        if init_latents is not None:
+            latents = rearrange(init_latents, "(b f) c h w -> b c f h w", f=video_length)
+        else:
+            num_channels_latents = self.unet.in_channels
+            latents = self.prepare_latents(
+                batch_size * num_videos_per_prompt,
+                num_channels_latents,
+                video_length,
+                height,
+                width,
+                text_embeddings.dtype,
+                device,
+                generator,
+                latents,
+            )
+        latents_dtype = latents.dtype
+
+        # Prepare extra step kwargs.
+        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
+
+        # Prepare text embeddings for controlnet
+        controlnet_text_embeddings = text_embeddings.repeat_interleave(video_length, 0)
+        _, controlnet_text_embeddings_c = controlnet_text_embeddings.chunk(2)
+        
+        controlnet_res_samples_cache_dict = {i:None for i in range(video_length)}
+
+        # For img2img setting
+        if num_actual_inference_steps is None:
+            num_actual_inference_steps = num_inference_steps
+        
+        if isinstance(source_image, str):
+            ref_image_latents = self.images2latents(np.array(Image.open(source_image).resize((width, height)))[None, :], latents_dtype).cuda()
+        elif isinstance(source_image, np.ndarray):
+            ref_image_latents = self.images2latents(source_image[None, :], latents_dtype).cuda()
+        
+        context_scheduler = get_context_scheduler(context_schedule)
+        
+        # Denoising loop
+        for i, t in tqdm(enumerate(timesteps), total=len(timesteps), disable=(rank!=0)):
+            if num_actual_inference_steps is not None and i < num_inference_steps - num_actual_inference_steps:
+                continue
+
+            noise_pred = torch.zeros(
+                (latents.shape[0] * (2 if do_classifier_free_guidance else 1), *latents.shape[1:]),
+                device=latents.device,
+                dtype=latents.dtype,
+            )
+            counter = torch.zeros(
+                (1, 1, latents.shape[2], 1, 1), device=latents.device, dtype=latents.dtype
+            )
+
+            appearance_encoder(
+                ref_image_latents.repeat(context_batch_size * (2 if do_classifier_free_guidance else 1), 1, 1, 1),
+                t,
+                encoder_hidden_states=text_embeddings,
+                return_dict=False,
+            )
+            
+            context_queue = list(context_scheduler(
+                0, num_inference_steps, latents.shape[2], context_frames, context_stride, 0
+            ))
+            num_context_batches = math.ceil(len(context_queue) / context_batch_size)
+            for i in range(num_context_batches):
+                context = context_queue[i*context_batch_size: (i+1)*context_batch_size]
+                # expand the latents if we are doing classifier free guidance
+                controlnet_latent_input = (
+                    torch.cat([latents[:, :, c] for c in context])
+                    .to(device)
+                )
+                controlnet_latent_input = self.scheduler.scale_model_input(controlnet_latent_input, t)
+
+                # prepare inputs for controlnet
+                b, c, f, h, w = controlnet_latent_input.shape
+                controlnet_latent_input = rearrange(controlnet_latent_input, "b c f h w -> (b f) c h w")
+                
+                # controlnet inference
+                down_block_res_samples, mid_block_res_sample = self.controlnet(
+                    controlnet_latent_input,
+                    t,
+                    encoder_hidden_states=torch.cat([controlnet_text_embeddings_c[c] for c in context]),
+                    controlnet_cond=torch.cat([controlnet_cond_images[c] for c in context]),
+                    conditioning_scale=controlnet_conditioning_scale,
+                    return_dict=False,
+                )
+
+                for j, k in enumerate(np.concatenate(np.array(context))):
+                    controlnet_res_samples_cache_dict[k] = ([sample[j:j+1] for sample in down_block_res_samples], mid_block_res_sample[j:j+1])
+
+            context_queue = list(context_scheduler(
+                0, num_inference_steps, latents.shape[2], context_frames, context_stride, context_overlap
+            ))
+
+            num_context_batches = math.ceil(len(context_queue) / context_batch_size)
+            global_context = []
+            for i in range(num_context_batches):
+                global_context.append(context_queue[i*context_batch_size: (i+1)*context_batch_size])
+            
+            for context in global_context[rank::world_size]:
+                # expand the latents if we are doing classifier free guidance
+                latent_model_input = (
+                    torch.cat([latents[:, :, c] for c in context])
+                    .to(device)
+                    .repeat(2 if do_classifier_free_guidance else 1, 1, 1, 1, 1)
+                )
+                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+                b, c, f, h, w = latent_model_input.shape
+                down_block_res_samples, mid_block_res_sample = self.select_controlnet_res_samples(
+                    controlnet_res_samples_cache_dict, 
+                    context,
+                    do_classifier_free_guidance,
+                    b, f
+                )
+                
+                reference_control_reader.update(reference_control_writer)
+                
+                # predict the noise residual
+                pred = self.unet(
+                    latent_model_input, 
+                    t, 
+                    encoder_hidden_states=text_embeddings[:b],
+                    down_block_additional_residuals=down_block_res_samples,
+                    mid_block_additional_residual=mid_block_res_sample,
+                    return_dict=False,
+                )[0]
+                
+                reference_control_reader.clear()
+                
+                pred_uc, pred_c = pred.chunk(2)
+                pred = torch.cat([pred_uc.unsqueeze(0), pred_c.unsqueeze(0)])
+                for j, c in enumerate(context):
+                    noise_pred[:, :, c] = noise_pred[:, :, c] + pred[:, j]
+                    counter[:, :, c] = counter[:, :, c] + 1
+                    
+            if is_dist_initialized:
+                noise_pred_gathered = [torch.zeros_like(noise_pred) for _ in range(world_size)]
+                if rank == 0:
+                    dist.gather(tensor=noise_pred, gather_list=noise_pred_gathered, dst=0)
+                else:
+                    dist.gather(tensor=noise_pred, gather_list=[], dst=0)
+                dist.barrier()
+
+                if rank == 0:
+                    for k in range(1, world_size):
+                        for context in global_context[k::world_size]:
+                            for j, c in enumerate(context):
+                                noise_pred[:, :, c] = noise_pred[:, :, c] + noise_pred_gathered[k][:, :, c] 
+                                counter[:, :, c] = counter[:, :, c] + 1
+
+            # perform guidance
+            if do_classifier_free_guidance:
+                noise_pred_uncond, noise_pred_text = (noise_pred / counter).chunk(2)
+                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+            # compute the previous noisy sample x_t -> x_t-1
+            latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
+            
+            if is_dist_initialized:
+                dist.broadcast(latents, 0)
+                dist.barrier()
+            
+            reference_control_writer.clear()
+
+        interpolation_factor = 1
+        latents = self.interpolate_latents(latents, interpolation_factor, device)
+        # Post-processing
+        video = self.decode_latents(latents, rank, decoder_consistency=decoder_consistency)
+
+        if is_dist_initialized:
+            dist.barrier()
+
+        # Convert to tensor
+        if output_type == "tensor":
+            video = torch.from_numpy(video)
+
+        if not return_dict:
+            return video
+        
+        return AnimationPipelineOutput(videos=video)

magicanimate/utils/dist_tools.py

+# Copyright 2023 ByteDance and/or its affiliates.
+#
+# Copyright (2023) MagicAnimate Authors
+#
+# ByteDance, its affiliates and licensors retain all intellectual
+# property and proprietary rights in and to this material, related
+# documentation and any modifications thereto. Any use, reproduction,
+# disclosure or distribution of this material and related documentation
+# without an express license agreement from ByteDance or
+# its affiliates is strictly prohibited.
+import os
+import socket
+import warnings
+import torch
+from torch import distributed as dist
+
+
+def distributed_init(args):
+
+    if dist.is_initialized():
+        warnings.warn("Distributed is already initialized, cannot initialize twice!")
+        args.rank = dist.get_rank()
+    else:
+        print(
+            f"Distributed Init (Rank {args.rank}): "
+            f"{args.init_method}"
+        )
+        dist.init_process_group(
+            backend='nccl',
+            init_method=args.init_method,
+            world_size=args.world_size,
+            rank=args.rank,
+        )
+        print(
+            f"Initialized Host {socket.gethostname()} as Rank "
+            f"{args.rank}"
+        )
+
+        if "MASTER_ADDR" not in os.environ or "MASTER_PORT" not in os.environ:
+            # Set for onboxdataloader support
+            split = args.init_method.split("//")
+            assert len(split) == 2, (
+                "host url for distributed should be split by '//' "
+                + "into exactly two elements"
+            )
+
+            split = split[1].split(":")
+            assert (
+                len(split) == 2
+            ), "host url should be of the form <host_url>:<host_port>"
+            os.environ["MASTER_ADDR"] = split[0]
+            os.environ["MASTER_PORT"] = split[1]
+
+        # perform a dummy all-reduce to initialize the NCCL communicator
+        dist.all_reduce(torch.zeros(1).cuda())
+
+        suppress_output(is_master())
+        args.rank = dist.get_rank()
+    return args.rank
+
+
+def get_rank():
+    if not dist.is_available():
+        return 0
+    if not dist.is_nccl_available():
+        return 0
+    if not dist.is_initialized():
+        return 0
+    return dist.get_rank()
+
+
+def is_master():
+    return get_rank() == 0
+
+
+def synchronize():
+    if dist.is_initialized():
+        dist.barrier()
+
+
+def suppress_output(is_master):
+    """Suppress printing on the current device. Force printing with `force=True`."""
+    import builtins as __builtin__
+
+    builtin_print = __builtin__.print
+
+    def print(*args, **kwargs):
+        force = kwargs.pop("force", False)
+        if is_master or force:
+            builtin_print(*args, **kwargs)
+
+    __builtin__.print = print
+
+    import warnings
+
+    builtin_warn = warnings.warn
+
+    def warn(*args, **kwargs):
+        force = kwargs.pop("force", False)
+        if is_master or force:
+            builtin_warn(*args, **kwargs)
+
+    # Log warnings only once
+    warnings.warn = warn
+    warnings.simplefilter("once", UserWarning)
\ No newline at end of file