@@ -51,6 +51,7 @@
     _import_structure["controlnets.controlnet_union"] = ["ControlNetUnionModel"]
     _import_structure["controlnets.controlnet_xs"] = ["ControlNetXSAdapter", "UNetControlNetXSModel"]
     _import_structure["controlnets.multicontrolnet"] = ["MultiControlNetModel"]
+    _import_structure["controlnets.multicontrolnet_union"] = ["MultiControlNetUnionModel"]
     _import_structure["embeddings"] = ["ImageProjection"]
     _import_structure["modeling_utils"] = ["ModelMixin"]
     _import_structure["transformers.auraflow_transformer_2d"] = ["AuraFlowTransformer2DModel"]
@@ -122,6 +123,7 @@
             HunyuanDiT2DControlNetModel,
             HunyuanDiT2DMultiControlNetModel,
             MultiControlNetModel,
+            MultiControlNetUnionModel,
             SD3ControlNetModel,
             SD3MultiControlNetModel,
             SparseControlNetModel,
 
@@ -18,6 +18,7 @@
     from .controlnet_union import ControlNetUnionModel
     from .controlnet_xs import ControlNetXSAdapter, ControlNetXSOutput, UNetControlNetXSModel
     from .multicontrolnet import MultiControlNetModel
+    from .multicontrolnet_union import MultiControlNetUnionModel
 
 if is_flax_available():
     from .controlnet_flax import FlaxControlNetModel
@@ -0,0 +1,192 @@
+import os
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+
+from ...models.controlnets.controlnet import ControlNetOutput
+from ...models.controlnets.controlnet_union import ControlNetUnionModel
+from ...models.modeling_utils import ModelMixin
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MultiControlNetUnionModel(ModelMixin):
+    r"""
+    Multiple `ControlNetUnionModel` wrapper class for Multi-ControlNet-Union.
+
+    This module is a wrapper for multiple instances of the `ControlNetUnionModel`. The `forward()` API is designed to
+    be compatible with `ControlNetUnionModel`.
+
+    Args:
+        controlnets (`List[ControlNetUnionModel]`):
+            Provides additional conditioning to the unet during the denoising process. You must set multiple
+            `ControlNetUnionModel` as a list.
+    """
+
+    def __init__(self, controlnets: Union[List[ControlNetUnionModel], Tuple[ControlNetUnionModel]]):
+        super().__init__()
+        self.nets = nn.ModuleList(controlnets)
+
+    def forward(
+        self,
+        sample: torch.Tensor,
+        timestep: Union[torch.Tensor, float, int],
+        encoder_hidden_states: torch.Tensor,
+        controlnet_cond: List[torch.tensor],
+        control_type: List[torch.Tensor],
+        control_type_idx: List[List[int]],
+        conditioning_scale: List[float],
+        class_labels: Optional[torch.Tensor] = None,
+        timestep_cond: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
+        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
+        guess_mode: bool = False,
+        return_dict: bool = True,
+    ) -> Union[ControlNetOutput, Tuple]:
+        for i, (image, ctype, ctype_idx, scale, controlnet) in enumerate(
+            zip(controlnet_cond, control_type, control_type_idx, conditioning_scale, self.nets)
+        ):
+            down_samples, mid_sample = controlnet(
+                sample=sample,
+                timestep=timestep,
+                encoder_hidden_states=encoder_hidden_states,
+                controlnet_cond=image,
+                control_type=ctype,
+                control_type_idx=ctype_idx,
+                conditioning_scale=scale,
+                class_labels=class_labels,
+                timestep_cond=timestep_cond,
+                attention_mask=attention_mask,
+                added_cond_kwargs=added_cond_kwargs,
+                cross_attention_kwargs=cross_attention_kwargs,
+                guess_mode=guess_mode,
+                return_dict=return_dict,
+            )
+
+            # merge samples
+            if i == 0:
+                down_block_res_samples, mid_block_res_sample = down_samples, mid_sample
+            else:
+                down_block_res_samples = [
+                    samples_prev + samples_curr
+                    for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)
+                ]
+                mid_block_res_sample += mid_sample
+
+        return down_block_res_samples, mid_block_res_sample
+
+    # Copied from diffusers.models.controlnets.multicontrolnet.MultiControlNetModel.save_pretrained with ControlNet->ControlNetUnion
+    def save_pretrained(
+        self,
+        save_directory: Union[str, os.PathLike],
+        is_main_process: bool = True,
+        save_function: Callable = None,
+        safe_serialization: bool = True,
+        variant: Optional[str] = None,
+    ):
+        """
+        Save a model and its configuration file to a directory, so that it can be re-loaded using the
+        `[`~models.controlnets.multicontrolnet.MultiControlNetUnionModel.from_pretrained`]` class method.
+
+        Arguments:
+            save_directory (`str` or `os.PathLike`):
+                Directory to which to save. Will be created if it doesn't exist.
+            is_main_process (`bool`, *optional*, defaults to `True`):
+                Whether the process calling this is the main process or not. Useful when in distributed training like
+                TPUs and need to call this function on all processes. In this case, set `is_main_process=True` only on
+                the main process to avoid race conditions.
+            save_function (`Callable`):
+                The function to use to save the state dictionary. Useful on distributed training like TPUs when one
+                need to replace `torch.save` by another method. Can be configured with the environment variable
+                `DIFFUSERS_SAVE_MODE`.
+            safe_serialization (`bool`, *optional*, defaults to `True`):
+                Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
+            variant (`str`, *optional*):
+                If specified, weights are saved in the format pytorch_model.<variant>.bin.
+        """
+        for idx, controlnet in enumerate(self.nets):
+            suffix = "" if idx == 0 else f"_{idx}"
+            controlnet.save_pretrained(
+                save_directory + suffix,
+                is_main_process=is_main_process,
+                save_function=save_function,
+                safe_serialization=safe_serialization,
+                variant=variant,
+            )
+
+    @classmethod
+    # Copied from diffusers.models.controlnets.multicontrolnet.MultiControlNetModel.from_pretrained with ControlNet->ControlNetUnion
+    def from_pretrained(cls, pretrained_model_path: Optional[Union[str, os.PathLike]], **kwargs):
+        r"""
+        Instantiate a pretrained MultiControlNetUnion model from multiple pre-trained controlnet models.
+
+        The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
+        the model, you should first set it back in training mode with `model.train()`.
+
+        The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
+        pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
+        task.
+
+        The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
+        weights are discarded.
+
+        Parameters:
+            pretrained_model_path (`os.PathLike`):
+                A path to a *directory* containing model weights saved using
+                [`~models.controlnets.multicontrolnet.MultiControlNetUnionModel.save_pretrained`], e.g.,
+                `./my_model_directory/controlnet`.
+            torch_dtype (`str` or `torch.dtype`, *optional*):
+                Override the default `torch.dtype` and load the model under this dtype. If `"auto"` is passed the dtype
+                will be automatically derived from the model's weights.
+            output_loading_info(`bool`, *optional*, defaults to `False`):
+                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
+            device_map (`str` or `Dict[str, Union[int, str, torch.device]]`, *optional*):
+                A map that specifies where each submodule should go. It doesn't need to be refined to each
+                parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the
+                same device.
+
+                To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For
+                more information about each option see [designing a device
+                map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map).
+            max_memory (`Dict`, *optional*):
+                A dictionary device identifier to maximum memory. Will default to the maximum memory available for each
+                GPU and the available CPU RAM if unset.
+            low_cpu_mem_usage (`bool`, *optional*, defaults to `True` if torch version >= 1.9.0 else `False`):
+                Speed up model loading by not initializing the weights and only loading the pre-trained weights. This
+                also tries to not use more than 1x model size in CPU memory (including peak memory) while loading the
+                model. This is only supported when torch version >= 1.9.0. If you are using an older version of torch,
+                setting this argument to `True` will raise an error.
+            variant (`str`, *optional*):
+                If specified load weights from `variant` filename, *e.g.* pytorch_model.<variant>.bin. `variant` is
+                ignored when using `from_flax`.
+            use_safetensors (`bool`, *optional*, defaults to `None`):
+                If set to `None`, the `safetensors` weights will be downloaded if they're available **and** if the
+                `safetensors` library is installed. If set to `True`, the model will be forcibly loaded from
+                `safetensors` weights. If set to `False`, loading will *not* use `safetensors`.
+        """
+        idx = 0
+        controlnets = []
+
+        # load controlnet and append to list until no controlnet directory exists anymore
+        # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
+        # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
+        model_path_to_load = pretrained_model_path
+        while os.path.isdir(model_path_to_load):
+            controlnet = ControlNetUnionModel.from_pretrained(model_path_to_load, **kwargs)
+            controlnets.append(controlnet)
+
+            idx += 1
+            model_path_to_load = pretrained_model_path + f"_{idx}"
+
+        logger.info(f"{len(controlnets)} controlnets loaded from {pretrained_model_path}.")
+
+        if len(controlnets) == 0:
+            raise ValueError(
+                f"No ControlNetUnions found under {os.path.dirname(pretrained_model_path)}. Expected at least {pretrained_model_path + '_0'}."
+            )
+
+        return cls(controlnets)