vace

scripts/convert_wan_to_diffusers.py

@@ -1,6 +1,6 @@
 import argparse
 import pathlib
-from typing import Any, Dict
+from typing import Any, Dict, Tuple
 
 import torch
 from accelerate import init_empty_weights
@@ -14,6 +14,8 @@ from diffusers import (
     WanImageToVideoPipeline,
     WanPipeline,
     WanTransformer3DModel,
+    WanVACEPipeline,
+    WanVACETransformer3DModel,
 )
 
 
@@ -59,7 +61,52 @@ TRANSFORMER_KEYS_RENAME_DICT = {
     "attn2.norm_k_img": "attn2.norm_added_k",
 }
 
+VACE_TRANSFORMER_KEYS_RENAME_DICT = {
+    "time_embedding.0": "condition_embedder.time_embedder.linear_1",
+    "time_embedding.2": "condition_embedder.time_embedder.linear_2",
+    "text_embedding.0": "condition_embedder.text_embedder.linear_1",
+    "text_embedding.2": "condition_embedder.text_embedder.linear_2",
+    "time_projection.1": "condition_embedder.time_proj",
+    "head.modulation": "scale_shift_table",
+    "head.head": "proj_out",
+    "modulation": "scale_shift_table",
+    "ffn.0": "ffn.net.0.proj",
+    "ffn.2": "ffn.net.2",
+    # Hack to swap the layer names
+    # The original model calls the norms in following order: norm1, norm3, norm2
+    # We convert it to: norm1, norm2, norm3
+    "norm2": "norm__placeholder",
+    "norm3": "norm2",
+    "norm__placeholder": "norm3",
+    # # For the I2V model
+    # "img_emb.proj.0": "condition_embedder.image_embedder.norm1",
+    # "img_emb.proj.1": "condition_embedder.image_embedder.ff.net.0.proj",
+    # "img_emb.proj.3": "condition_embedder.image_embedder.ff.net.2",
+    # "img_emb.proj.4": "condition_embedder.image_embedder.norm2",
+    # # for the FLF2V model
+    # "img_emb.emb_pos": "condition_embedder.image_embedder.pos_embed",
+    # Add attention component mappings
+    "self_attn.q": "attn1.to_q",
+    "self_attn.k": "attn1.to_k",
+    "self_attn.v": "attn1.to_v",
+    "self_attn.o": "attn1.to_out.0",
+    "self_attn.norm_q": "attn1.norm_q",
+    "self_attn.norm_k": "attn1.norm_k",
+    "cross_attn.q": "attn2.to_q",
+    "cross_attn.k": "attn2.to_k",
+    "cross_attn.v": "attn2.to_v",
+    "cross_attn.o": "attn2.to_out.0",
+    "cross_attn.norm_q": "attn2.norm_q",
+    "cross_attn.norm_k": "attn2.norm_k",
+    "attn2.to_k_img": "attn2.add_k_proj",
+    "attn2.to_v_img": "attn2.add_v_proj",
+    "attn2.norm_k_img": "attn2.norm_added_k",
+    "before_proj": "proj_in",
+    "after_proj": "proj_out",
+}
+
 TRANSFORMER_SPECIAL_KEYS_REMAP = {}
+VACE_TRANSFORMER_SPECIAL_KEYS_REMAP = {}
 
 
 def update_state_dict_(state_dict: Dict[str, Any], old_key: str, new_key: str) -> Dict[str, Any]:
@@ -74,7 +121,7 @@ def load_sharded_safetensors(dir: pathlib.Path):
     return state_dict
 
 
-def get_transformer_config(model_type: str) -> Dict[str, Any]:
+def get_transformer_config(model_type: str) -> Tuple[Dict[str, Any], ...]:
     if model_type == "Wan-T2V-1.3B":
         config = {
             "model_id": "StevenZhang/Wan2.1-T2V-1.3B-Diff",
@@ -94,6 +141,8 @@ def get_transformer_config(model_type: str) -> Dict[str, Any]:
                 "text_dim": 4096,
             },
         }
+        RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
+        SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
     elif model_type == "Wan-T2V-14B":
         config = {
             "model_id": "StevenZhang/Wan2.1-T2V-14B-Diff",
@@ -113,6 +162,8 @@ def get_transformer_config(model_type: str) -> Dict[str, Any]:
                 "text_dim": 4096,
             },
         }
+        RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
+        SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
     elif model_type == "Wan-I2V-14B-480p":
         config = {
             "model_id": "StevenZhang/Wan2.1-I2V-14B-480P-Diff",
@@ -133,6 +184,8 @@ def get_transformer_config(model_type: str) -> Dict[str, Any]:
                 "text_dim": 4096,
             },
         }
+        RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
+        SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
     elif model_type == "Wan-I2V-14B-720p":
         config = {
             "model_id": "StevenZhang/Wan2.1-I2V-14B-720P-Diff",
@@ -153,6 +206,8 @@ def get_transformer_config(model_type: str) -> Dict[str, Any]:
                 "text_dim": 4096,
             },
         }
+        RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
+        SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
     elif model_type == "Wan-FLF2V-14B-720P":
         config = {
             "model_id": "ypyp/Wan2.1-FLF2V-14B-720P",  # This is just a placeholder
@@ -175,11 +230,60 @@ def get_transformer_config(model_type: str) -> Dict[str, Any]:
                 "pos_embed_seq_len": 257 * 2,
             },
         }
-    return config
+        RENAME_DICT = TRANSFORMER_KEYS_RENAME_DICT
+        SPECIAL_KEYS_REMAP = TRANSFORMER_SPECIAL_KEYS_REMAP
+    elif model_type == "Wan-VACE-1.3B":
+        config = {
+            "model_id": "Wan-AI/Wan2.1-VACE-1.3B",
+            "diffusers_config": {
+                "added_kv_proj_dim": None,
+                "attention_head_dim": 128,
+                "cross_attn_norm": True,
+                "eps": 1e-06,
+                "ffn_dim": 8960,
+                "freq_dim": 256,
+                "in_channels": 16,
+                "num_attention_heads": 12,
+                "num_layers": 30,
+                "out_channels": 16,
+                "patch_size": [1, 2, 2],
+                "qk_norm": "rms_norm_across_heads",
+                "text_dim": 4096,
+                "vace_layers": [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28],
+                "vace_in_channels": 96,
+            },
+        }
+        RENAME_DICT = VACE_TRANSFORMER_KEYS_RENAME_DICT
+        SPECIAL_KEYS_REMAP = VACE_TRANSFORMER_SPECIAL_KEYS_REMAP
+    elif model_type == "Wan-VACE-14B":
+        config = {
+            "model_id": "Wan-AI/Wan2.1-VACE-14B",
+            "diffusers_config": {
+                "added_kv_proj_dim": None,
+                "attention_head_dim": 128,
+                "cross_attn_norm": True,
+                "eps": 1e-06,
+                "ffn_dim": 13824,
+                "freq_dim": 256,
+                "in_channels": 16,
+                "num_attention_heads": 40,
+                "num_layers": 40,
+                "out_channels": 16,
+                "patch_size": [1, 2, 2],
+                "qk_norm": "rms_norm_across_heads",
+                "text_dim": 4096,
+                "vace_layers": [0, 5, 10, 15, 20, 25, 30, 35],
+                "vace_in_channels": 96,
+            },
+        }
+        RENAME_DICT = VACE_TRANSFORMER_KEYS_RENAME_DICT
+        SPECIAL_KEYS_REMAP = VACE_TRANSFORMER_SPECIAL_KEYS_REMAP
+    return config, RENAME_DICT, SPECIAL_KEYS_REMAP
 
 
 def convert_transformer(model_type: str):
-    config = get_transformer_config(model_type)
+    config, RENAME_DICT, SPECIAL_KEYS_REMAP = get_transformer_config(model_type)
+
     diffusers_config = config["diffusers_config"]
     model_id = config["model_id"]
     model_dir = pathlib.Path(snapshot_download(model_id, repo_type="model"))
@@ -187,16 +291,19 @@ def convert_transformer(model_type: str):
     original_state_dict = load_sharded_safetensors(model_dir)
 
     with init_empty_weights():
-        transformer = WanTransformer3DModel.from_config(diffusers_config)
+        if "VACE" not in model_type:
+            transformer = WanTransformer3DModel.from_config(diffusers_config)
+        else:
+            transformer = WanVACETransformer3DModel.from_config(diffusers_config)
 
     for key in list(original_state_dict.keys()):
         new_key = key[:]
-        for replace_key, rename_key in TRANSFORMER_KEYS_RENAME_DICT.items():
+        for replace_key, rename_key in RENAME_DICT.items():
             new_key = new_key.replace(replace_key, rename_key)
         update_state_dict_(original_state_dict, key, new_key)
 
     for key in list(original_state_dict.keys()):
-        for special_key, handler_fn_inplace in TRANSFORMER_SPECIAL_KEYS_REMAP.items():
+        for special_key, handler_fn_inplace in SPECIAL_KEYS_REMAP.items():
             if special_key not in key:
                 continue
             handler_fn_inplace(key, original_state_dict)
@@ -412,7 +519,7 @@ def get_args():
     parser = argparse.ArgumentParser()
     parser.add_argument("--model_type", type=str, default=None)
     parser.add_argument("--output_path", type=str, required=True)
-    parser.add_argument("--dtype", default="fp32")
+    parser.add_argument("--dtype", default="fp32", choices=["fp32", "fp16", "bf16", "none"])
     return parser.parse_args()
 
 
@@ -426,18 +533,20 @@ DTYPE_MAPPING = {
 if __name__ == "__main__":
     args = get_args()
 
-    transformer = None
-    dtype = DTYPE_MAPPING[args.dtype]
-
-    transformer = convert_transformer(args.model_type).to(dtype=dtype)
+    transformer = convert_transformer(args.model_type)
     vae = convert_vae()
-    text_encoder = UMT5EncoderModel.from_pretrained("google/umt5-xxl")
+    text_encoder = UMT5EncoderModel.from_pretrained("google/umt5-xxl", torch_dtype=torch.bfloat16)
     tokenizer = AutoTokenizer.from_pretrained("google/umt5-xxl")
     flow_shift = 16.0 if "FLF2V" in args.model_type else 3.0
     scheduler = UniPCMultistepScheduler(
         prediction_type="flow_prediction", use_flow_sigmas=True, num_train_timesteps=1000, flow_shift=flow_shift
     )
 
+    # If user has specified "none", we keep the original dtypes of the state dict without any conversion
+    if args.dtype != "none":
+        dtype = DTYPE_MAPPING[args.dtype]
+        transformer.to(dtype)
+
     if "I2V" in args.model_type or "FLF2V" in args.model_type:
         image_encoder = CLIPVisionModelWithProjection.from_pretrained(
             "laion/CLIP-ViT-H-14-laion2B-s32B-b79K", torch_dtype=torch.bfloat16
@@ -452,6 +561,14 @@ if __name__ == "__main__":
             image_encoder=image_encoder,
             image_processor=image_processor,
         )
+    elif "VACE" in args.model_type:
+        pipe = WanVACEPipeline(
+            transformer=transformer,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            vae=vae,
+            scheduler=scheduler,
+        )
     else:
         pipe = WanPipeline(
             transformer=transformer,

src/diffusers/__init__.py

@@ -215,6 +215,7 @@ else:
             "UVit2DModel",
             "VQModel",
             "WanTransformer3DModel",
+            "WanVACETransformer3DModel",
         ]
     )
     _import_structure["optimization"] = [
@@ -526,6 +527,7 @@ else:
             "VQDiffusionPipeline",
             "WanImageToVideoPipeline",
             "WanPipeline",
+            "WanVACEPipeline",
             "WanVideoToVideoPipeline",
             "WuerstchenCombinedPipeline",
             "WuerstchenDecoderPipeline",
@@ -819,6 +821,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             UVit2DModel,
             VQModel,
             WanTransformer3DModel,
+            WanVACETransformer3DModel,
         )
         from .optimization import (
             get_constant_schedule,
@@ -1109,6 +1112,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             VQDiffusionPipeline,
             WanImageToVideoPipeline,
             WanPipeline,
+            WanVACEPipeline,
             WanVideoToVideoPipeline,
             WuerstchenCombinedPipeline,
             WuerstchenDecoderPipeline,

src/diffusers/loaders/lora_pipeline.py

@@ -4852,6 +4852,65 @@ class WanLoraLoaderMixin(LoraBaseMixin):
 
         return state_dict
 
+    @classmethod
+    def _maybe_expand_t2v_lora_for_vace(
+            cls,
+            transformer: torch.nn.Module,
+            state_dict,
+    ):
+        target_device = transformer.device
+        if hasattr(transformer, 'vace_blocks'):
+            print("HERE 1")
+            inferred_rank_for_vace = None
+            lora_weights_dtype_for_vace = next(iter(transformer.parameters())).dtype  # Fallback dtype
+
+            for k_lora_any, v_lora_tensor_any in state_dict.items():
+                if k_lora_any.endswith(".lora_A.weight"):
+                    print("HERE 2")
+                    inferred_rank_for_vace = v_lora_tensor_any.shape[0]
+                    lora_weights_dtype_for_vace = v_lora_tensor_any.dtype
+                    break  # Found one, good enough for rank and dtype
+
+            if inferred_rank_for_vace is not None:
+                print("HERE 3")
+                # Determine if the LoRA format (as potentially modified by I2V expansion) includes bias
+                # This re-checks 'has_bias' based on the *current* state_dict.
+                current_lora_has_bias = any(".lora_B.bias" in k for k in state_dict.keys())
+
+                for i, vace_block_module_in_model in enumerate(transformer.vace_blocks):
+                    # Specifically target proj_out as per the error message
+                    if hasattr(vace_block_module_in_model, 'proj_out'):
+                        print("HERE 4")
+                        proj_out_linear_layer_in_model = vace_block_module_in_model.proj_out
+
+                        vace_lora_A_key = f"vace_blocks.{i}.proj_out.lora_A.weight"
+                        vace_lora_B_key = f"vace_blocks.{i}.proj_out.lora_B.weight"
+
+                        if vace_lora_A_key not in state_dict:
+                            state_dict[vace_lora_A_key] = torch.zeros(
+                                (inferred_rank_for_vace, proj_out_linear_layer_in_model.in_features),
+                                device=target_device, dtype=lora_weights_dtype_for_vace
+                            )
+
+                        if vace_lora_B_key not in state_dict:
+                            state_dict[vace_lora_B_key] = torch.zeros(
+                                (proj_out_linear_layer_in_model.out_features, inferred_rank_for_vace),
+                                device=target_device, dtype=lora_weights_dtype_for_vace
+                            )
+
+                        # Use 'current_lora_has_bias' to decide on padding bias for VACE blocks
+                        if current_lora_has_bias and proj_out_linear_layer_in_model.bias is not None:
+                            print("HERE 5")
+                            vace_lora_B_bias_key = f"vace_blocks.{i}.proj_out.lora_B.bias"
+                            if vace_lora_B_bias_key not in state_dict:
+                                state_dict[vace_lora_B_bias_key] = torch.zeros_like(
+                                    proj_out_linear_layer_in_model.bias,  # Shape from model's bias
+                                    device=target_device  # Dtype from model's bias implicitly by zeros_like
+                                )
+
+        print("AFTER 2:", list(state_dict.keys()))
+        return state_dict
+
     def load_lora_weights(
         self,
         pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
@@ -4900,10 +4959,15 @@ class WanLoraLoaderMixin(LoraBaseMixin):
             transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
             state_dict=state_dict,
         )
+        state_dict = self._maybe_expand_t2v_lora_for_vace(
+            transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
+            state_dict=state_dict,
+        )
         is_correct_format = all("lora" in key for key in state_dict.keys())
         if not is_correct_format:
             raise ValueError("Invalid LoRA checkpoint.")
 
+        print("WTF")
         self.load_lora_into_transformer(
             state_dict,
             transformer=getattr(self, self.transformer_name) if not hasattr(self, "transformer") else self.transformer,
@@ -5713,4 +5777,4 @@ class LoraLoaderMixin(StableDiffusionLoraLoaderMixin):
     def __init__(self, *args, **kwargs):
         deprecation_message = "LoraLoaderMixin is deprecated and this will be removed in a future version. Please use `StableDiffusionLoraLoaderMixin`, instead."
         deprecate("LoraLoaderMixin", "1.0.0", deprecation_message)
-        super().__init__(*args, **kwargs)
+        super().__init__(*args, **kwargs)

src/diffusers/loaders/peft.py

@@ -58,6 +58,7 @@ _SET_ADAPTER_SCALE_FN_MAPPING = {
     "CogView4Transformer2DModel": lambda model_cls, weights: weights,
     "HiDreamImageTransformer2DModel": lambda model_cls, weights: weights,
     "HunyuanVideoFramepackTransformer3DModel": lambda model_cls, weights: weights,
+    "WanVACETransformer3DModel": lambda model_cls, weights: weights,
 }
 
 

src/diffusers/models/__init__.py

@@ -89,6 +89,7 @@ if is_torch_available():
     _import_structure["transformers.transformer_sd3"] = ["SD3Transformer2DModel"]
     _import_structure["transformers.transformer_temporal"] = ["TransformerTemporalModel"]
     _import_structure["transformers.transformer_wan"] = ["WanTransformer3DModel"]
+    _import_structure["transformers.transformer_wan_vace"] = ["WanVACETransformer3DModel"]
     _import_structure["unets.unet_1d"] = ["UNet1DModel"]
     _import_structure["unets.unet_2d"] = ["UNet2DModel"]
     _import_structure["unets.unet_2d_condition"] = ["UNet2DConditionModel"]
@@ -178,6 +179,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             Transformer2DModel,
             TransformerTemporalModel,
             WanTransformer3DModel,
+            WanVACETransformer3DModel,
         )
         from .unets import (
             I2VGenXLUNet,

src/diffusers/models/transformers/__init__.py

@@ -32,3 +32,4 @@ if is_torch_available():
     from .transformer_sd3 import SD3Transformer2DModel
     from .transformer_temporal import TransformerTemporalModel
     from .transformer_wan import WanTransformer3DModel
+    from .transformer_wan_vace import WanVACETransformer3DModel

src/diffusers/models/transformers/transformer_wan_vace.py

@@ -0,0 +1,393 @@
+# Copyright 2025 The Wan 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 math
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
+from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
+from ..attention import FeedForward
+from ..attention_processor import Attention
+from ..cache_utils import CacheMixin
+from ..modeling_outputs import Transformer2DModelOutput
+from ..modeling_utils import ModelMixin
+from ..normalization import FP32LayerNorm
+from .transformer_wan import WanAttnProcessor2_0, WanRotaryPosEmbed, WanTimeTextImageEmbedding, WanTransformerBlock
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+class WanVACETransformerBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        ffn_dim: int,
+        num_heads: int,
+        qk_norm: str = "rms_norm_across_heads",
+        cross_attn_norm: bool = False,
+        eps: float = 1e-6,
+        added_kv_proj_dim: Optional[int] = None,
+        apply_input_projection: bool = False,
+        apply_output_projection: bool = False,
+    ):
+        super().__init__()
+
+        # 1. Input projection
+        self.proj_in = None
+        if apply_input_projection:
+            self.proj_in = nn.Linear(dim, dim)
+
+        # 2. Self-attention
+        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
+        self.attn1 = Attention(
+            query_dim=dim,
+            heads=num_heads,
+            kv_heads=num_heads,
+            dim_head=dim // num_heads,
+            qk_norm=qk_norm,
+            eps=eps,
+            bias=True,
+            cross_attention_dim=None,
+            out_bias=True,
+            processor=WanAttnProcessor2_0(),
+        )
+
+        # 3. Cross-attention
+        self.attn2 = Attention(
+            query_dim=dim,
+            heads=num_heads,
+            kv_heads=num_heads,
+            dim_head=dim // num_heads,
+            qk_norm=qk_norm,
+            eps=eps,
+            bias=True,
+            cross_attention_dim=None,
+            out_bias=True,
+            added_kv_proj_dim=added_kv_proj_dim,
+            added_proj_bias=True,
+            processor=WanAttnProcessor2_0(),
+        )
+        self.norm2 = FP32LayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()
+
+        # 4. Feed-forward
+        self.ffn = FeedForward(dim, inner_dim=ffn_dim, activation_fn="gelu-approximate")
+        self.norm3 = FP32LayerNorm(dim, eps, elementwise_affine=False)
+
+        # 5. Output projection
+        self.proj_out = None
+        if apply_output_projection:
+            self.proj_out = nn.Linear(dim, dim)
+
+        self.scale_shift_table = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        control_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        rotary_emb: torch.Tensor,
+    ) -> torch.Tensor:
+        if self.proj_in is not None:
+            control_hidden_states = self.proj_in(control_hidden_states)
+            control_hidden_states = control_hidden_states + hidden_states
+
+        shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
+            self.scale_shift_table + temb.float()
+        ).chunk(6, dim=1)
+
+        # 1. Self-attention
+        norm_hidden_states = (self.norm1(control_hidden_states.float()) * (1 + scale_msa) + shift_msa).type_as(
+            control_hidden_states
+        )
+        attn_output = self.attn1(hidden_states=norm_hidden_states, rotary_emb=rotary_emb)
+        control_hidden_states = (control_hidden_states.float() + attn_output * gate_msa).type_as(control_hidden_states)
+
+        # 2. Cross-attention
+        norm_hidden_states = self.norm2(control_hidden_states.float()).type_as(control_hidden_states)
+        attn_output = self.attn2(hidden_states=norm_hidden_states, encoder_hidden_states=encoder_hidden_states)
+        control_hidden_states = control_hidden_states + attn_output
+
+        # 3. Feed-forward
+        norm_hidden_states = (self.norm3(control_hidden_states.float()) * (1 + c_scale_msa) + c_shift_msa).type_as(
+            control_hidden_states
+        )
+        ff_output = self.ffn(norm_hidden_states)
+        control_hidden_states = (control_hidden_states.float() + ff_output.float() * c_gate_msa).type_as(
+            control_hidden_states
+        )
+
+        conditioning_states = None
+        if self.proj_out is not None:
+            conditioning_states = self.proj_out(control_hidden_states)
+
+        return conditioning_states, control_hidden_states
+
+
+class WanVACETransformer3DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin, CacheMixin):
+    r"""
+    A Transformer model for video-like data used in the Wan model.
+
+    Args:
+        patch_size (`Tuple[int]`, defaults to `(1, 2, 2)`):
+            3D patch dimensions for video embedding (t_patch, h_patch, w_patch).
+        num_attention_heads (`int`, defaults to `40`):
+            Fixed length for text embeddings.
+        attention_head_dim (`int`, defaults to `128`):
+            The number of channels in each head.
+        in_channels (`int`, defaults to `16`):
+            The number of channels in the input.
+        out_channels (`int`, defaults to `16`):
+            The number of channels in the output.
+        text_dim (`int`, defaults to `512`):
+            Input dimension for text embeddings.
+        freq_dim (`int`, defaults to `256`):
+            Dimension for sinusoidal time embeddings.
+        ffn_dim (`int`, defaults to `13824`):
+            Intermediate dimension in feed-forward network.
+        num_layers (`int`, defaults to `40`):
+            The number of layers of transformer blocks to use.
+        window_size (`Tuple[int]`, defaults to `(-1, -1)`):
+            Window size for local attention (-1 indicates global attention).
+        cross_attn_norm (`bool`, defaults to `True`):
+            Enable cross-attention normalization.
+        qk_norm (`bool`, defaults to `True`):
+            Enable query/key normalization.
+        eps (`float`, defaults to `1e-6`):
+            Epsilon value for normalization layers.
+        add_img_emb (`bool`, defaults to `False`):
+            Whether to use img_emb.
+        added_kv_proj_dim (`int`, *optional*, defaults to `None`):
+            The number of channels to use for the added key and value projections. If `None`, no projection is used.
+    """
+
+    _supports_gradient_checkpointing = True
+    _skip_layerwise_casting_patterns = ["patch_embedding", "vace_patch_embedding", "condition_embedder", "norm"]
+    _no_split_modules = ["WanTransformerBlock", "WanVACETransformerBlock"]
+    _keep_in_fp32_modules = ["time_embedder", "scale_shift_table", "norm1", "norm2", "norm3"]
+    _keys_to_ignore_on_load_unexpected = ["norm_added_q"]
+
+    @register_to_config
+    def __init__(
+        self,
+        patch_size: Tuple[int] = (1, 2, 2),
+        num_attention_heads: int = 40,
+        attention_head_dim: int = 128,
+        in_channels: int = 16,
+        out_channels: int = 16,
+        text_dim: int = 4096,
+        freq_dim: int = 256,
+        ffn_dim: int = 13824,
+        num_layers: int = 40,
+        cross_attn_norm: bool = True,
+        qk_norm: Optional[str] = "rms_norm_across_heads",
+        eps: float = 1e-6,
+        image_dim: Optional[int] = None,
+        added_kv_proj_dim: Optional[int] = None,
+        rope_max_seq_len: int = 1024,
+        pos_embed_seq_len: Optional[int] = None,
+        vace_layers: List[int] = [0, 5, 10, 15, 20, 25, 30, 35],
+        vace_in_channels: int = 96,
+    ) -> None:
+        super().__init__()
+
+        inner_dim = num_attention_heads * attention_head_dim
+        out_channels = out_channels or in_channels
+
+        if max(vace_layers) >= num_layers:
+            raise ValueError(f"VACE layers {vace_layers} exceed the number of transformer layers {num_layers}.")
+        if 0 not in vace_layers:
+            raise ValueError("VACE layers must include layer 0.")
+
+        # 1. Patch & position embedding
+        self.rope = WanRotaryPosEmbed(attention_head_dim, patch_size, rope_max_seq_len)
+        self.patch_embedding = nn.Conv3d(in_channels, inner_dim, kernel_size=patch_size, stride=patch_size)
+        self.vace_patch_embedding = nn.Conv3d(vace_in_channels, inner_dim, kernel_size=patch_size, stride=patch_size)
+
+        # 2. Condition embeddings
+        # image_embedding_dim=1280 for I2V model
+        self.condition_embedder = WanTimeTextImageEmbedding(
+            dim=inner_dim,
+            time_freq_dim=freq_dim,
+            time_proj_dim=inner_dim * 6,
+            text_embed_dim=text_dim,
+            image_embed_dim=image_dim,
+            pos_embed_seq_len=pos_embed_seq_len,
+        )
+
+        # 3. Transformer blocks
+        self.blocks = nn.ModuleList(
+            [
+                WanTransformerBlock(
+                    inner_dim, ffn_dim, num_attention_heads, qk_norm, cross_attn_norm, eps, added_kv_proj_dim
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        self.vace_blocks = nn.ModuleList(
+            [
+                WanVACETransformerBlock(
+                    inner_dim,
+                    ffn_dim,
+                    num_attention_heads,
+                    qk_norm,
+                    cross_attn_norm,
+                    eps,
+                    added_kv_proj_dim,
+                    apply_input_projection=i == 0,  # Layer 0 always has input projection and is in vace_layers
+                    apply_output_projection=True,
+                )
+                for i in range(len(vace_layers))
+            ]
+        )
+
+        # 4. Output norm & projection
+        self.norm_out = FP32LayerNorm(inner_dim, eps, elementwise_affine=False)
+        self.proj_out = nn.Linear(inner_dim, out_channels * math.prod(patch_size))
+        self.scale_shift_table = nn.Parameter(torch.randn(1, 2, inner_dim) / inner_dim**0.5)
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        timestep: torch.LongTensor,
+        encoder_hidden_states: torch.Tensor,
+        encoder_hidden_states_image: Optional[torch.Tensor] = None,
+        control_hidden_states: torch.Tensor = None,
+        control_hidden_states_scale: torch.Tensor = None,
+        return_dict: bool = True,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> Union[torch.Tensor, Dict[str, torch.Tensor]]:
+        if attention_kwargs is not None:
+            attention_kwargs = attention_kwargs.copy()
+            lora_scale = attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if attention_kwargs is not None and attention_kwargs.get("scale", None) is not None:
+                logger.warning(
+                    "Passing `scale` via `attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        p_t, p_h, p_w = self.config.patch_size
+        post_patch_num_frames = num_frames // p_t
+        post_patch_height = height // p_h
+        post_patch_width = width // p_w
+
+        if control_hidden_states_scale is None:
+            control_hidden_states_scale = control_hidden_states.new_ones(len(self.config.vace_layers))
+        control_hidden_states_scale = torch.unbind(control_hidden_states_scale)
+        if len(control_hidden_states_scale) != len(self.config.vace_layers):
+            raise ValueError(
+                f"Length of `control_hidden_states_scale` {len(control_hidden_states_scale)} should be "
+                f"equal to {len(self.config.vace_layers)}."
+            )
+
+        # 1. Rotary position embedding
+        rotary_emb = self.rope(hidden_states)
+
+        # 2. Patch embedding
+        hidden_states = self.patch_embedding(hidden_states)
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)
+
+        control_hidden_states = self.vace_patch_embedding(control_hidden_states)
+        control_hidden_states = control_hidden_states.flatten(2).transpose(1, 2)
+        control_hidden_states_padding = control_hidden_states.new_zeros(
+            batch_size, hidden_states.size(1) - control_hidden_states.size(1), control_hidden_states.size(2)
+        )
+        control_hidden_states = torch.cat([control_hidden_states, control_hidden_states_padding], dim=1)
+
+        # 3. Time embedding
+        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
+            timestep, encoder_hidden_states, encoder_hidden_states_image
+        )
+        timestep_proj = timestep_proj.unflatten(1, (6, -1))
+
+        # 4. Image embedding
+        if encoder_hidden_states_image is not None:
+            encoder_hidden_states = torch.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1)
+
+        # 5. Transformer blocks
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            # Prepare VACE hints
+            control_hidden_states_list = []
+            for i, block in enumerate(self.vace_blocks):
+                conditioning_states, control_hidden_states = self._gradient_checkpointing_func(
+                    block, hidden_states, encoder_hidden_states, control_hidden_states, timestep_proj, rotary_emb
+                )
+                control_hidden_states_list.append((conditioning_states, control_hidden_states_scale[i]))
+            control_hidden_states_list = control_hidden_states_list[::-1]
+
+            for i, block in enumerate(self.blocks):
+                hidden_states = self._gradient_checkpointing_func(
+                    block, hidden_states, encoder_hidden_states, timestep_proj, rotary_emb
+                )
+                if i in self.config.vace_layers:
+                    control_hint, scale = control_hidden_states_list.pop()
+                    hidden_states = hidden_states + control_hint * scale
+        else:
+            # Prepare VACE hints
+            control_hidden_states_list = []
+            for i, block in enumerate(self.vace_blocks):
+                conditioning_states, control_hidden_states = block(
+                    hidden_states, encoder_hidden_states, control_hidden_states, timestep_proj, rotary_emb
+                )
+                control_hidden_states_list.append((conditioning_states, control_hidden_states_scale[i]))
+            control_hidden_states_list = control_hidden_states_list[::-1]
+
+            for i, block in enumerate(self.blocks):
+                hidden_states = block(hidden_states, encoder_hidden_states, timestep_proj, rotary_emb)
+                if i in self.config.vace_layers:
+                    control_hint, scale = control_hidden_states_list.pop()
+                    hidden_states = hidden_states + control_hint * scale
+
+        # 6. Output norm, projection & unpatchify
+        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)
+
+        # Move the shift and scale tensors to the same device as hidden_states.
+        # When using multi-GPU inference via accelerate these will be on the
+        # first device rather than the last device, which hidden_states ends up
+        # on.
+        shift = shift.to(hidden_states.device)
+        scale = scale.to(hidden_states.device)
+
+        hidden_states = (self.norm_out(hidden_states.float()) * (1 + scale) + shift).type_as(hidden_states)
+        hidden_states = self.proj_out(hidden_states)
+
+        hidden_states = hidden_states.reshape(
+            batch_size, post_patch_num_frames, post_patch_height, post_patch_width, p_t, p_h, p_w, -1
+        )
+        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
+        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (output,)
+
+        return Transformer2DModelOutput(sample=output)

src/diffusers/pipelines/__init__.py

@@ -366,7 +366,7 @@ else:
         "WuerstchenDecoderPipeline",
         "WuerstchenPriorPipeline",
     ]
-    _import_structure["wan"] = ["WanPipeline", "WanImageToVideoPipeline", "WanVideoToVideoPipeline"]
+    _import_structure["wan"] = ["WanPipeline", "WanImageToVideoPipeline", "WanVideoToVideoPipeline", "WanVACEPipeline"]
 try:
     if not is_onnx_available():
         raise OptionalDependencyNotAvailable()
@@ -734,7 +734,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             UniDiffuserTextDecoder,
         )
         from .visualcloze import VisualClozeGenerationPipeline, VisualClozePipeline
-        from .wan import WanImageToVideoPipeline, WanPipeline, WanVideoToVideoPipeline
+        from .wan import WanImageToVideoPipeline, WanPipeline, WanVACEPipeline, WanVideoToVideoPipeline
         from .wuerstchen import (
             WuerstchenCombinedPipeline,
             WuerstchenDecoderPipeline,

src/diffusers/pipelines/wan/__init__.py

@@ -24,6 +24,7 @@ except OptionalDependencyNotAvailable:
 else:
     _import_structure["pipeline_wan"] = ["WanPipeline"]
     _import_structure["pipeline_wan_i2v"] = ["WanImageToVideoPipeline"]
+    _import_structure["pipeline_wan_vace"] = ["WanVACEPipeline"]
     _import_structure["pipeline_wan_video2video"] = ["WanVideoToVideoPipeline"]
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     try:
@@ -35,6 +36,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     else:
         from .pipeline_wan import WanPipeline
         from .pipeline_wan_i2v import WanImageToVideoPipeline
+        from .pipeline_wan_vace import WanVACEPipeline
         from .pipeline_wan_video2video import WanVideoToVideoPipeline
 
 else:

src/diffusers/pipelines/wan/pipeline_wan_vace.py

@@ -0,0 +1,917 @@
+# Copyright 2025 The Wan 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 html
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import PIL.Image
+import regex as re
+import torch
+from transformers import AutoTokenizer, UMT5EncoderModel
+
+from ...callbacks import MultiPipelineCallbacks, PipelineCallback
+from ...image_processor import PipelineImageInput
+from ...loaders import WanLoraLoaderMixin
+from ...models import AutoencoderKLWan, WanVACETransformer3DModel
+from ...schedulers import FlowMatchEulerDiscreteScheduler
+from ...utils import is_ftfy_available, is_torch_xla_available, logging, replace_example_docstring
+from ...utils.torch_utils import randn_tensor
+from ...video_processor import VideoProcessor
+from ..pipeline_utils import DiffusionPipeline
+from .pipeline_output import WanPipelineOutput
+
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+if is_ftfy_available():
+    import ftfy
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```python
+        >>> import torch
+        >>> from diffusers.utils import export_to_video
+        >>> from diffusers import AutoencoderKLWan, WanPipeline
+        >>> from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
+
+        >>> # Available models: Wan-AI/Wan2.1-T2V-14B-Diffusers, Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+        >>> model_id = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
+        >>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+        >>> pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
+        >>> flow_shift = 5.0  # 5.0 for 720P, 3.0 for 480P
+        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)
+        >>> pipe.to("cuda")
+
+        >>> prompt = "A cat and a dog baking a cake together in a kitchen. The cat is carefully measuring flour, while the dog is stirring the batter with a wooden spoon. The kitchen is cozy, with sunlight streaming through the window."
+        >>> negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
+
+        >>> output = pipe(
+        ...     prompt=prompt,
+        ...     negative_prompt=negative_prompt,
+        ...     height=720,
+        ...     width=1280,
+        ...     num_frames=81,
+        ...     guidance_scale=5.0,
+        ... ).frames[0]
+        >>> export_to_video(output, "output.mp4", fps=16)
+        ```
+"""
+
+
+def basic_clean(text):
+    text = ftfy.fix_text(text)
+    text = html.unescape(html.unescape(text))
+    return text.strip()
+
+
+def whitespace_clean(text):
+    text = re.sub(r"\s+", " ", text)
+    text = text.strip()
+    return text
+
+
+def prompt_clean(text):
+    text = whitespace_clean(basic_clean(text))
+    return text
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.retrieve_latents
+def retrieve_latents(
+    encoder_output: torch.Tensor, generator: Optional[torch.Generator] = None, sample_mode: str = "sample"
+):
+    if hasattr(encoder_output, "latent_dist") and sample_mode == "sample":
+        return encoder_output.latent_dist.sample(generator)
+    elif hasattr(encoder_output, "latent_dist") and sample_mode == "argmax":
+        return encoder_output.latent_dist.mode()
+    elif hasattr(encoder_output, "latents"):
+        return encoder_output.latents
+    else:
+        raise AttributeError("Could not access latents of provided encoder_output")
+
+
+class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
+    r"""
+    Pipeline for controllable generation using Wan.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
+    implemented for all pipelines (downloading, saving, running on a particular device, etc.).
+
+    Args:
+        tokenizer ([`T5Tokenizer`]):
+            Tokenizer from [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5Tokenizer),
+            specifically the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.
+        text_encoder ([`T5EncoderModel`]):
+            [T5](https://huggingface.co/docs/transformers/en/model_doc/t5#transformers.T5EncoderModel), specifically
+            the [google/umt5-xxl](https://huggingface.co/google/umt5-xxl) variant.
+        transformer ([`WanTransformer3DModel`]):
+            Conditional Transformer to denoise the input latents.
+        scheduler ([`UniPCMultistepScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKLWan`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
+    """
+
+    model_cpu_offload_seq = "text_encoder->transformer->vae"
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+
+    def __init__(
+        self,
+        tokenizer: AutoTokenizer,
+        text_encoder: UMT5EncoderModel,
+        transformer: WanVACETransformer3DModel,
+        vae: AutoencoderKLWan,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            transformer=transformer,
+            scheduler=scheduler,
+        )
+
+        self.vae_scale_factor_temporal = 2 ** sum(self.vae.temperal_downsample) if getattr(self, "vae", None) else 4
+        self.vae_scale_factor_spatial = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8
+        self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
+
+    # Copied from diffusers.pipelines.wan.pipeline_wan.WanPipeline._get_t5_prompt_embeds
+    def _get_t5_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_videos_per_prompt: int = 1,
+        max_sequence_length: int = 226,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        prompt = [prompt_clean(u) for u in prompt]
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            add_special_tokens=True,
+            return_attention_mask=True,
+            return_tensors="pt",
+        )
+        text_input_ids, mask = text_inputs.input_ids, text_inputs.attention_mask
+        seq_lens = mask.gt(0).sum(dim=1).long()
+
+        prompt_embeds = self.text_encoder(text_input_ids.to(device), mask.to(device)).last_hidden_state
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+        prompt_embeds = [u[:v] for u, v in zip(prompt_embeds, seq_lens)]
+        prompt_embeds = torch.stack(
+            [torch.cat([u, u.new_zeros(max_sequence_length - u.size(0), u.size(1))]) for u in prompt_embeds], dim=0
+        )
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        _, seq_len, _ = prompt_embeds.shape
+        prompt_embeds = prompt_embeds.repeat(1, num_videos_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_videos_per_prompt, seq_len, -1)
+
+        return prompt_embeds
+
+    # Copied from diffusers.pipelines.wan.pipeline_wan.WanPipeline.encode_prompt
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        negative_prompt: Optional[Union[str, List[str]]] = None,
+        do_classifier_free_guidance: bool = True,
+        num_videos_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        max_sequence_length: int = 226,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        r"""
+        Encodes the prompt into text encoder hidden states.
+
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            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`).
+            do_classifier_free_guidance (`bool`, *optional*, defaults to `True`):
+                Whether to use classifier free guidance or not.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                Number of videos that should be generated per prompt. torch device to place the resulting embeddings on
+            prompt_embeds (`torch.Tensor`, *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.Tensor`, *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.
+            device: (`torch.device`, *optional*):
+                torch device
+            dtype: (`torch.dtype`, *optional*):
+                torch dtype
+        """
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        if prompt_embeds is None:
+            prompt_embeds = self._get_t5_prompt_embeds(
+                prompt=prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        if do_classifier_free_guidance and negative_prompt_embeds is None:
+            negative_prompt = negative_prompt or ""
+            negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt
+
+            if prompt is not None and 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 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`."
+                )
+
+            negative_prompt_embeds = self._get_t5_prompt_embeds(
+                prompt=negative_prompt,
+                num_videos_per_prompt=num_videos_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+                dtype=dtype,
+            )
+
+        return prompt_embeds, negative_prompt_embeds
+
+    def check_inputs(
+        self,
+        prompt,
+        negative_prompt,
+        height,
+        width,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+        video=None,
+        mask=None,
+        reference_images=None,
+    ):
+        base = self.vae_scale_factor_spatial * self.transformer.config.patch_size[1]
+        if height % base != 0 or width % base != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by {base} but are {height} and {width}.")
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif negative_prompt is not None and negative_prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`: {negative_prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (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)}")
+        elif negative_prompt is not None and (
+            not isinstance(negative_prompt, str) and not isinstance(negative_prompt, list)
+        ):
+            raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
+
+        if video is not None:
+            if mask is not None:
+                if len(video) != len(mask):
+                    raise ValueError(
+                        f"Length of `video` {len(video)} and `mask` {len(mask)} do not match. Please make sure that"
+                        " they have the same length."
+                    )
+            if reference_images is not None:
+                is_pil_image = isinstance(reference_images, PIL.Image.Image)
+                is_list_of_pil_images = isinstance(reference_images, list) and all(
+                    isinstance(ref_img, PIL.Image.Image) for ref_img in reference_images
+                )
+                is_list_of_list_of_pil_images = isinstance(reference_images, list) and all(
+                    isinstance(ref_img, list) and all(isinstance(ref_img_, PIL.Image.Image) for ref_img_ in ref_img)
+                    for ref_img in reference_images
+                )
+                if not (is_pil_image or is_list_of_pil_images or is_list_of_list_of_pil_images):
+                    raise ValueError(
+                        "`reference_images` has to be of type `PIL.Image.Image` or `list` of `PIL.Image.Image`, or "
+                        "`list` of `list` of `PIL.Image.Image`, but is {type(reference_images)}"
+                    )
+                if is_list_of_list_of_pil_images and len(reference_images) != 1:
+                    raise ValueError(
+                        "The pipeline only supports generating one video at a time at the moment. When passing a list "
+                        "of list of reference images, where the outer list corresponds to the batch size and the inner "
+                        "list corresponds to list of conditioning images per video, please make sure to only pass "
+                        "one inner list of reference images (i.e., `[[<image1>, <image2>, ...]]`"
+                    )
+        elif mask is not None:
+            raise ValueError("`mask` can only be passed if `video` is passed as well.")
+
+    def preprocess_conditions(
+        self,
+        video: Optional[List[PipelineImageInput]] = None,
+        mask: Optional[List[PipelineImageInput]] = None,
+        reference_images: Optional[List[PipelineImageInput]] = None,
+        batch_size: int = 1,
+        height: int = 480,
+        width: int = 832,
+        num_frames: int = 81,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+    ):
+        if video is not None:
+            base = self.vae_scale_factor_spatial * self.transformer.config.patch_size[1]
+            video_height, video_width = self.video_processor.get_default_height_width(video[0])
+
+            if video_height * video_width > height * width:
+                scale = min(width / video_width, height / video_height)
+                video_height, video_width = int(video_height * scale), int(video_width * scale)
+
+            if video_height % base != 0 or video_width % base != 0:
+                logger.warning(
+                    f"Video height and width should be divisible by {base}, but got {video_height} and {video_width}. "
+                )
+                video_height = (video_height // base) * base
+                video_width = (video_width // base) * base
+
+            assert video_height * video_width <= height * width
+
+            video = self.video_processor.preprocess_video(video, video_height, video_width)
+            image_size = (video_height, video_width)  # Use the height/width of video (with possible rescaling)
+        else:
+            video = torch.zeros(batch_size, 3, num_frames, height, width, dtype=dtype, device=device)
+            image_size = (height, width)  # Use the height/width provider by user
+
+        if mask is not None:
+            mask = self.video_processor.preprocess_video(mask, image_size[0], image_size[1])
+            mask = torch.clamp((mask + 1) / 2, min=0, max=1)
+        else:
+            mask = torch.ones_like(video)
+
+        video = video.to(dtype=dtype, device=device)
+        mask = mask.to(dtype=dtype, device=device)
+
+        # Make a list of list of images where the outer list corresponds to video batch size and the inner list
+        # corresponds to list of conditioning images per video
+        if reference_images is None or isinstance(reference_images, PIL.Image.Image):
+            reference_images = [[reference_images] for _ in range(video.shape[0])]
+        elif isinstance(reference_images, (list, tuple)) and isinstance(next(iter(reference_images)), PIL.Image.Image):
+            reference_images = [reference_images]
+        elif (
+            isinstance(reference_images, (list, tuple))
+            and isinstance(next(iter(reference_images)), list)
+            and isinstance(next(iter(reference_images[0])), PIL.Image.Image)
+        ):
+            reference_images = reference_images
+        else:
+            raise ValueError(
+                "`reference_images` has to be of type `PIL.Image.Image` or `list` of `PIL.Image.Image`, or "
+                "`list` of `list` of `PIL.Image.Image`, but is {type(reference_images)}"
+            )
+
+        if video.shape[0] != len(reference_images):
+            raise ValueError(
+                f"Batch size of `video` {video.shape[0]} and length of `reference_images` {len(reference_images)} does not match."
+            )
+
+        ref_images_lengths = [len(reference_images_batch) for reference_images_batch in reference_images]
+        if any(l != ref_images_lengths[0] for l in ref_images_lengths):
+            raise ValueError(
+                f"All batches of `reference_images` should have the same length, but got {ref_images_lengths}. Support for this "
+                "may be added in the future."
+            )
+
+        reference_images_preprocessed = []
+        for i, reference_images_batch in enumerate(reference_images):
+            preprocessed_images = []
+            for j, image in enumerate(reference_images_batch):
+                if image is None:
+                    continue
+                image = self.video_processor.preprocess(image, None, None)
+                img_height, img_width = image.shape[-2:]
+                scale = min(image_size[0] / img_height, image_size[1] / img_width)
+                new_height, new_width = int(img_height * scale), int(img_width * scale)
+                resized_image = torch.nn.functional.interpolate(
+                    image, size=(new_height, new_width), mode="bilinear", align_corners=False
+                ).squeeze(0)  # [C, H, W]
+                top = (image_size[0] - new_height) // 2
+                left = (image_size[1] - new_width) // 2
+                canvas = torch.ones(3, *image_size, device=device, dtype=dtype)
+                canvas[:, top : top + new_height, left : left + new_width] = resized_image
+                preprocessed_images.append(canvas)
+            reference_images_preprocessed.append(preprocessed_images)
+
+        return video, mask, reference_images_preprocessed
+
+    def prepare_video_latents(
+        self,
+        video: torch.Tensor,
+        mask: torch.Tensor,
+        reference_images: Optional[List[List[torch.Tensor]]] = None,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        device: Optional[torch.device] = None,
+    ) -> torch.Tensor:
+        device = device or self._execution_device
+
+        if isinstance(generator, list):
+            # TODO: support this
+            raise ValueError("Passing a list of generators is not yet supported. This may be supported in the future.")
+
+        if reference_images is None:
+            # For each batch of video, we set no re
+            # ference image (as one or more can be passed by user)
+            reference_images = [[None] for _ in range(video.shape[0])]
+        else:
+            if video.shape[0] != len(reference_images):
+                raise ValueError(
+                    f"Batch size of `video` {video.shape[0]} and length of `reference_images` {len(reference_images)} does not match."
+                )
+
+        if video.shape[0] != 1:
+            # TODO: support this
+            raise ValueError(
+                "Generating with more than one video is not yet supported. This may be supported in the future."
+            )
+
+        vae_dtype = self.vae.dtype
+        video = video.to(dtype=vae_dtype)
+
+        latents_mean = torch.tensor(self.vae.config.latents_mean, device=device, dtype=torch.float32).view(
+            1, self.vae.config.z_dim, 1, 1, 1
+        )
+        latents_std = 1.0 / torch.tensor(self.vae.config.latents_std, device=device, dtype=torch.float32).view(
+            1, self.vae.config.z_dim, 1, 1, 1
+        )
+
+        if mask is None:
+            latents = retrieve_latents(self.vae.encode(video), generator, sample_mode="argmax").unbind(0)
+            latents = ((latents.float() - latents_mean) * latents_std).to(vae_dtype)
+        else:
+            mask = mask.to(dtype=vae_dtype)
+            mask = torch.where(mask > 0.5, 1.0, 0.0)
+            inactive = video * (1 - mask)
+            reactive = video * mask
+            inactive = retrieve_latents(self.vae.encode(inactive), generator, sample_mode="argmax")
+            reactive = retrieve_latents(self.vae.encode(reactive), generator, sample_mode="argmax")
+            inactive = ((inactive.float() - latents_mean) * latents_std).to(vae_dtype)
+            reactive = ((reactive.float() - latents_mean) * latents_std).to(vae_dtype)
+            latents = torch.cat([inactive, reactive], dim=1)
+
+        latent_list = []
+        for latent, reference_images_batch in zip(latents, reference_images):
+            for reference_image in reference_images_batch:
+                assert reference_image.ndim == 3
+                reference_image = reference_image.to(dtype=vae_dtype)
+                reference_image = reference_image[None, :, None, :, :]  # [1, C, 1, H, W]
+                reference_latent = retrieve_latents(self.vae.encode(reference_image), generator, sample_mode="argmax")
+                reference_latent = ((reference_latent.float() - latents_mean) * latents_std).to(vae_dtype)
+                reference_latent = torch.cat([reference_latent, torch.zeros_like(reference_latent)], dim=1)
+                latent = torch.cat([reference_latent.squeeze(0), latent], dim=1)  # Concat across frame dimension
+            latent_list.append(latent)
+        return torch.stack(latent_list)
+
+    def prepare_masks(
+        self,
+        mask: torch.Tensor,
+        reference_images: Optional[List[torch.Tensor]] = None,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+    ) -> torch.Tensor:
+        if isinstance(generator, list):
+            # TODO: support this
+            raise ValueError("Passing a list of generators is not yet supported. This may be supported in the future.")
+
+        if reference_images is None:
+            # For each batch of video, we set no reference image (as one or more can be passed by user)
+            reference_images = [[None] for _ in range(mask.shape[0])]
+        else:
+            if mask.shape[0] != len(reference_images):
+                raise ValueError(
+                    f"Batch size of `mask` {mask.shape[0]} and length of `reference_images` {len(reference_images)} does not match."
+                )
+
+        if mask.shape[0] != 1:
+            # TODO: support this
+            raise ValueError(
+                "Generating with more than one video is not yet supported. This may be supported in the future."
+            )
+
+        transformer_patch_size = self.transformer.config.patch_size[1]
+
+        mask_list = []
+        for mask_, reference_images_batch in zip(mask, reference_images):
+            num_channels, num_frames, height, width = mask_.shape
+            new_num_frames = (num_frames + self.vae_scale_factor_temporal - 1) // self.vae_scale_factor_temporal
+            new_height = height // (self.vae_scale_factor_spatial * transformer_patch_size) * transformer_patch_size
+            new_width = width // (self.vae_scale_factor_spatial * transformer_patch_size) * transformer_patch_size
+            mask_ = mask_[0, :, :, :]
+            mask_ = mask_.view(
+                num_frames, new_height, self.vae_scale_factor_spatial, new_width, self.vae_scale_factor_spatial
+            )
+            mask_ = mask_.permute(2, 4, 0, 1, 3).flatten(0, 1)  # [8x8, num_frames, new_height, new_width]
+            mask_ = torch.nn.functional.interpolate(
+                mask_.unsqueeze(0), size=(new_num_frames, new_height, new_width), mode="nearest-exact"
+            ).squeeze(0)
+            num_ref_images = len(reference_images_batch)
+            if num_ref_images > 0:
+                mask_padding = torch.zeros_like(mask_[:, :num_ref_images, :, :])
+                mask_ = torch.cat([mask_, mask_padding], dim=1)
+            mask_list.append(mask_)
+        return torch.stack(mask_list)
+
+    def prepare_latents(
+        self,
+        batch_size: int,
+        num_channels_latents: int = 16,
+        height: int = 480,
+        width: int = 832,
+        num_frames: int = 81,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if latents is not None:
+            return latents.to(device=device, dtype=dtype)
+
+        num_latent_frames = (num_frames - 1) // self.vae_scale_factor_temporal + 1
+        shape = (
+            batch_size,
+            num_channels_latents,
+            num_latent_frames,
+            int(height) // self.vae_scale_factor_spatial,
+            int(width) // self.vae_scale_factor_spatial,
+        )
+        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."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        return latents
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def do_classifier_free_guidance(self):
+        return self._guidance_scale > 1.0
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def current_timestep(self):
+        return self._current_timestep
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @property
+    def attention_kwargs(self):
+        return self._attention_kwargs
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        negative_prompt: Union[str, List[str]] = None,
+        video: Optional[List[PipelineImageInput]] = None,
+        mask: Optional[List[PipelineImageInput]] = None,
+        reference_images: Optional[List[PipelineImageInput]] = None,
+        conditioning_scale: Union[float, List[float], torch.Tensor] = 1.0,
+        height: int = 480,
+        width: int = 832,
+        num_frames: int = 81,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 5.0,
+        num_videos_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.Tensor] = None,
+        prompt_embeds: Optional[torch.Tensor] = None,
+        negative_prompt_embeds: Optional[torch.Tensor] = None,
+        output_type: Optional[str] = "np",
+        return_dict: bool = True,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[
+            Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks]
+        ] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+    ):
+        r"""
+        The call function to 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.
+            height (`int`, defaults to `480`):
+                The height in pixels of the generated image.
+            width (`int`, defaults to `832`):
+                The width in pixels of the generated image.
+            num_frames (`int`, defaults to `81`):
+                The number of frames in the generated video.
+            num_inference_steps (`int`, 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`, defaults to `5.0`):
+                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.
+            num_videos_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
+                A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
+                generation deterministic.
+            latents (`torch.Tensor`, *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 is generated by sampling using the supplied random `generator`.
+            prompt_embeds (`torch.Tensor`, *optional*):
+                Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not
+                provided, text embeddings are generated from the `prompt` input argument.
+            output_type (`str`, *optional*, defaults to `"np"`):
+                The output format of the generated image. Choose between `PIL.Image` or `np.array`.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`WanPipelineOutput`] instead of a plain tuple.
+            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).
+            callback_on_step_end (`Callable`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*):
+                A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of
+                each denoising step during the inference. with the following arguments: `callback_on_step_end(self:
+                DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a
+                list of all tensors as specified by `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            autocast_dtype (`torch.dtype`, *optional*, defaults to `torch.bfloat16`):
+                The dtype to use for the torch.amp.autocast.
+
+        Examples:
+
+        Returns:
+            [`~WanPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`WanPipelineOutput`] is returned, otherwise a `tuple` is returned where
+                the first element is a list with the generated images and the second element is a list of `bool`s
+                indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content.
+        """
+
+        if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)):
+            callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs
+
+        # Simplification of implementation for now
+        if not isinstance(prompt, str):
+            raise ValueError("Passing a list of prompts is not yet supported. This may be supported in the future.")
+        if num_videos_per_prompt != 1:
+            raise ValueError(
+                "Generating multiple videos per prompt is not yet supported. This may be supported in the future."
+            )
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            negative_prompt,
+            height,
+            width,
+            prompt_embeds,
+            negative_prompt_embeds,
+            callback_on_step_end_tensor_inputs,
+            video,
+            mask,
+            reference_images,
+        )
+
+        if num_frames % self.vae_scale_factor_temporal != 1:
+            logger.warning(
+                f"`num_frames - 1` has to be divisible by {self.vae_scale_factor_temporal}. Rounding to the nearest number."
+            )
+            num_frames = num_frames // self.vae_scale_factor_temporal * self.vae_scale_factor_temporal + 1
+        num_frames = max(num_frames, 1)
+
+        self._guidance_scale = guidance_scale
+        self._attention_kwargs = attention_kwargs
+        self._current_timestep = None
+        self._interrupt = False
+
+        device = self._execution_device
+
+        # 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]
+
+        vae_dtype = self.vae.dtype
+        transformer_dtype = self.transformer.dtype
+
+        if isinstance(conditioning_scale, (int, float)):
+            conditioning_scale = [conditioning_scale] * len(self.transformer.config.vace_layers)
+        if isinstance(conditioning_scale, list):
+            if len(conditioning_scale) != len(self.transformer.config.vace_layers):
+                raise ValueError(
+                    f"Length of `conditioning_scale` {len(conditioning_scale)} does not match number of layers {len(self.transformer.config.vace_layers)}."
+                )
+            conditioning_scale = torch.tensor(conditioning_scale)
+        if isinstance(conditioning_scale, torch.Tensor):
+            if conditioning_scale.size(0) != len(self.transformer.config.vace_layers):
+                raise ValueError(
+                    f"Length of `conditioning_scale` {conditioning_scale.size(0)} does not match number of layers {len(self.transformer.config.vace_layers)}."
+                )
+            conditioning_scale = conditioning_scale.to(device=device, dtype=transformer_dtype)
+
+        # 3. Encode input prompt
+        prompt_embeds, negative_prompt_embeds = self.encode_prompt(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            do_classifier_free_guidance=self.do_classifier_free_guidance,
+            num_videos_per_prompt=num_videos_per_prompt,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            max_sequence_length=max_sequence_length,
+            device=device,
+        )
+
+        prompt_embeds = prompt_embeds.to(transformer_dtype)
+        if negative_prompt_embeds is not None:
+            negative_prompt_embeds = negative_prompt_embeds.to(transformer_dtype)
+
+        # 4. Prepare timesteps
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self.scheduler.timesteps
+
+        # 5. Prepare latent variables
+        video, mask, reference_images = self.preprocess_conditions(
+            video,
+            mask,
+            reference_images,
+            batch_size,
+            height,
+            width,
+            num_frames,
+            torch.float32,
+            device,
+        )
+
+        conditioning_latents = self.prepare_video_latents(video, mask, reference_images, generator, device)
+        mask = self.prepare_masks(mask, reference_images, generator)
+        conditioning_latents = torch.cat([conditioning_latents, mask], dim=1)
+        conditioning_latents = conditioning_latents.to(transformer_dtype)
+
+        num_channels_latents = self.transformer.config.in_channels
+        latents = self.prepare_latents(
+            batch_size * num_videos_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            num_frames,
+            torch.float32,
+            device,
+            generator,
+            latents,
+        )
+
+        if conditioning_latents.shape[2] != latents.shape[2]:
+            logger.warning(
+                "The number of frames in the conditioning latents does not match the number of frames to be generated. Generation quality may be affected."
+            )
+
+        # 6. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        self._num_timesteps = len(timesteps)
+
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                self._current_timestep = t
+                latent_model_input = latents.to(transformer_dtype)
+                timestep = t.expand(latents.shape[0])
+
+                noise_pred = self.transformer(
+                    hidden_states=latent_model_input,
+                    timestep=timestep,
+                    encoder_hidden_states=prompt_embeds,
+                    control_hidden_states=conditioning_latents,
+                    control_hidden_states_scale=conditioning_scale,
+                    attention_kwargs=attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                if self.do_classifier_free_guidance:
+                    noise_uncond = self.transformer(
+                        hidden_states=latent_model_input,
+                        timestep=timestep,
+                        encoder_hidden_states=negative_prompt_embeds,
+                        control_hidden_states=conditioning_latents,
+                        control_hidden_states_scale=conditioning_scale,
+                        attention_kwargs=attention_kwargs,
+                        return_dict=False,
+                    )[0]
+                    noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)
+
+                # compute the previous noisy sample x_t -> x_t-1
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                if callback_on_step_end is not None:
+                    callback_kwargs = {}
+                    for k in callback_on_step_end_tensor_inputs:
+                        callback_kwargs[k] = locals()[k]
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+
+                    latents = callback_outputs.pop("latents", latents)
+                    prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds)
+                    negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds)
+
+                # 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 XLA_AVAILABLE:
+                    xm.mark_step()
+
+        self._current_timestep = None
+
+        if not output_type == "latent":
+            latents = latents.to(vae_dtype)
+            latents_mean = (
+                torch.tensor(self.vae.config.latents_mean)
+                .view(1, self.vae.config.z_dim, 1, 1, 1)
+                .to(latents.device, latents.dtype)
+            )
+            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+                latents.device, latents.dtype
+            )
+            latents = latents / latents_std + latents_mean
+            video = self.vae.decode(latents, return_dict=False)[0]
+            video = self.video_processor.postprocess_video(video, output_type=output_type)
+        else:
+            video = latents
+
+        # Offload all models
+        self.maybe_free_model_hooks()
+
+        if not return_dict:
+            return (video,)
+
+        return WanPipelineOutput(frames=video)

src/diffusers/utils/dummy_pt_objects.py

@@ -1150,6 +1150,21 @@ class WanTransformer3DModel(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class WanVACETransformer3DModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
 def get_constant_schedule(*args, **kwargs):
     requires_backends(get_constant_schedule, ["torch"])
 

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -2882,6 +2882,21 @@ class WanPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class WanVACEPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class WanVideoToVideoPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/pipelines/wan/test_wan_vace.py

@@ -0,0 +1,189 @@
+# Copyright 2024 The HuggingFace Team.
+#
+# 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 unittest
+
+import numpy as np
+import torch
+from PIL import Image
+from transformers import AutoTokenizer, T5EncoderModel
+
+from diffusers import AutoencoderKLWan, FlowMatchEulerDiscreteScheduler, WanVACEPipeline, WanVACETransformer3DModel
+from diffusers.utils.testing_utils import enable_full_determinism
+
+from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
+from ..test_pipelines_common import PipelineTesterMixin
+
+
+enable_full_determinism()
+
+
+class WanVACEPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
+    pipeline_class = WanVACEPipeline
+    params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs"}
+    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
+    image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
+    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
+    required_optional_params = frozenset(
+        [
+            "num_inference_steps",
+            "generator",
+            "latents",
+            "return_dict",
+            "callback_on_step_end",
+            "callback_on_step_end_tensor_inputs",
+        ]
+    )
+    test_xformers_attention = False
+    supports_dduf = False
+
+    def get_dummy_components(self):
+        torch.manual_seed(0)
+        vae = AutoencoderKLWan(
+            base_dim=3,
+            z_dim=16,
+            dim_mult=[1, 1, 1, 1],
+            num_res_blocks=1,
+            temperal_downsample=[False, True, True],
+        )
+
+        torch.manual_seed(0)
+        scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0)
+        text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
+
+        torch.manual_seed(0)
+        transformer = WanVACETransformer3DModel(
+            patch_size=(1, 2, 2),
+            num_attention_heads=2,
+            attention_head_dim=12,
+            in_channels=16,
+            out_channels=16,
+            text_dim=32,
+            freq_dim=256,
+            ffn_dim=32,
+            num_layers=3,
+            cross_attn_norm=True,
+            qk_norm="rms_norm_across_heads",
+            rope_max_seq_len=32,
+            vace_layers=[0, 2],
+            vace_in_channels=96,
+        )
+
+        components = {
+            "transformer": transformer,
+            "vae": vae,
+            "scheduler": scheduler,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+        }
+        return components
+
+    def get_dummy_inputs(self, device, seed=0):
+        if str(device).startswith("mps"):
+            generator = torch.manual_seed(seed)
+        else:
+            generator = torch.Generator(device=device).manual_seed(seed)
+
+        num_frames = 17
+        height = 16
+        width = 16
+
+        video = [Image.new("RGB", (height, width))] * num_frames
+        mask = [Image.new("L", (height, width), 0)] * num_frames
+
+        inputs = {
+            "video": video,
+            "mask": mask,
+            "prompt": "dance monkey",
+            "negative_prompt": "negative",  # TODO
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 6.0,
+            "height": 16,
+            "width": 16,
+            "num_frames": num_frames,
+            "max_sequence_length": 16,
+            "output_type": "pt",
+        }
+        return inputs
+
+    def test_inference(self):
+        device = "cpu"
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        video = pipe(**inputs).frames
+        generated_video = video[0]
+
+        self.assertEqual(generated_video.shape, (17, 3, 16, 16))
+        expected_video = torch.randn(17, 3, 16, 16)
+        max_diff = np.abs(generated_video - expected_video).max()
+        self.assertLessEqual(max_diff, 1e10)
+
+    def test_inference_with_single_reference_image(self):
+        device = "cpu"
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        inputs["reference_images"] = Image.new("RGB", (16, 16))
+        video = pipe(**inputs).frames
+        generated_video = video[0]
+
+        self.assertEqual(generated_video.shape, (17, 3, 16, 16))
+        expected_video = torch.randn(17, 3, 16, 16)
+        max_diff = np.abs(generated_video - expected_video).max()
+        self.assertLessEqual(max_diff, 1e10)
+
+    def test_inference_with_multiple_reference_image(self):
+        device = "cpu"
+
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.to(device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(device)
+        inputs["reference_images"] = [[Image.new("RGB", (16, 16))] * 2]
+        video = pipe(**inputs).frames
+        generated_video = video[0]
+
+        self.assertEqual(generated_video.shape, (17, 3, 16, 16))
+        expected_video = torch.randn(17, 3, 16, 16)
+        max_diff = np.abs(generated_video - expected_video).max()
+        self.assertLessEqual(max_diff, 1e10)
+
+    @unittest.skip("Test not supported")
+    def test_attention_slicing_forward_pass(self):
+        pass
+
+    @unittest.skip("Errors out because passing multiple prompts at once is not yet supported by this pipeline.")
+    def test_encode_prompt_works_in_isolation(self):
+        pass
+
+    @unittest.skip("Batching is not yet supported with this pipeline")
+    def test_inference_batch_consistent(self):
+        pass
+
+    @unittest.skip("Batching is not yet supported with this pipeline")
+    def test_inference_batch_single_identical(self):
+        return super().test_inference_batch_single_identical()