diff --git a/src/diffusers/models/unets/unet_motion_model.py b/src/diffusers/models/unets/unet_motion_model.py
index a092daa662..1b62d16d5d 100644
--- a/src/diffusers/models/unets/unet_motion_model.py
+++ b/src/diffusers/models/unets/unet_motion_model.py
@@ -15,6 +15,7 @@ from typing import Any, Dict, Optional, Tuple, Union
 
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 import torch.utils.checkpoint
 
 from ...configuration_utils import ConfigMixin, FrozenDict, register_to_config
@@ -27,6 +28,9 @@ from ..attention_processor import (
     AttentionProcessor,
     AttnAddedKVProcessor,
     AttnProcessor,
+    AttnProcessor2_0,
+    IPAdapterAttnProcessor,
+    IPAdapterAttnProcessor2_0,
 )
 from ..embeddings import TimestepEmbedding, Timesteps
 from ..modeling_utils import ModelMixin
@@ -490,6 +494,36 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
         model.time_proj.load_state_dict(unet.time_proj.state_dict())
         model.time_embedding.load_state_dict(unet.time_embedding.state_dict())
 
+        if any(
+            isinstance(proc, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0))
+            for proc in unet.attn_processors.values()
+        ):
+            attn_procs = {}
+            for name, processor in unet.attn_processors.items():
+                if name.endswith("attn1.processor"):
+                    attn_processor_class = (
+                        AttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else AttnProcessor
+                    )
+                    attn_procs[name] = attn_processor_class()
+                else:
+                    attn_processor_class = (
+                        IPAdapterAttnProcessor2_0
+                        if hasattr(F, "scaled_dot_product_attention")
+                        else IPAdapterAttnProcessor
+                    )
+                    attn_procs[name] = attn_processor_class(
+                        hidden_size=processor.hidden_size,
+                        cross_attention_dim=processor.cross_attention_dim,
+                        scale=processor.scale,
+                        num_tokens=processor.num_tokens,
+                    )
+            for name, processor in model.attn_processors.items():
+                if name not in attn_procs:
+                    attn_procs[name] = processor.__class__()
+            model.set_attn_processor(attn_procs)
+            model.config.encoder_hid_dim_type = "ip_image_proj"
+            model.encoder_hid_proj = unet.encoder_hid_proj
+
         for i, down_block in enumerate(unet.down_blocks):
             model.down_blocks[i].resnets.load_state_dict(down_block.resnets.state_dict())
             if hasattr(model.down_blocks[i], "attentions"):