add import & stype

missing copied from

scripts/convert_wan_to_diffusers.py

@@ -278,6 +278,29 @@ def get_transformer_config(model_type: str) -> Tuple[Dict[str, Any], ...]:
         }
         RENAME_DICT = VACE_TRANSFORMER_KEYS_RENAME_DICT
         SPECIAL_KEYS_REMAP = VACE_TRANSFORMER_SPECIAL_KEYS_REMAP
+    elif model_type == "Wan2.2-VACE-Fun-14B":
+        config = {
+            "model_id": "alibaba-pai/Wan2.2-VACE-Fun-A14B",
+            "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
     elif model_type == "Wan2.2-I2V-14B-720p":
         config = {
             "model_id": "Wan-AI/Wan2.2-I2V-A14B",
@@ -975,7 +998,17 @@ if __name__ == "__main__":
             image_encoder=image_encoder,
             image_processor=image_processor,
         )
-    elif "VACE" in args.model_type:
+    elif "Wan2.2-VACE" in args.model_type:
+        pipe = WanVACEPipeline(
+            transformer=transformer,
+            transformer_2=transformer_2,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            vae=vae,
+            scheduler=scheduler,
+            boundary_ratio=0.875,
+        )
+    elif "Wan-VACE" in args.model_type:
         pipe = WanVACEPipeline(
             transformer=transformer,
             text_encoder=text_encoder,

src/diffusers/__init__.py

@@ -495,6 +495,7 @@ else:
             "LTXImageToVideoPipeline",
             "LTXLatentUpsamplePipeline",
             "LTXPipeline",
+            "LucyEditPipeline",
             "Lumina2Pipeline",
             "Lumina2Text2ImgPipeline",
             "LuminaPipeline",
@@ -1149,6 +1150,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             LTXImageToVideoPipeline,
             LTXLatentUpsamplePipeline,
             LTXPipeline,
+            LucyEditPipeline,
             Lumina2Pipeline,
             Lumina2Text2ImgPipeline,
             LuminaPipeline,

src/diffusers/models/activations.py

@@ -17,11 +17,10 @@ import torch
 import torch.nn.functional as F
 from torch import nn
 
-from ..utils import deprecate, get_logger, is_torch_npu_available, is_torch_version
+from ..utils import deprecate
+from ..utils.import_utils import is_torch_npu_available, is_torch_version
 
 
-logger = get_logger(__name__)
-
 if is_torch_npu_available():
     import torch_npu
 
@@ -32,7 +31,6 @@ ACT2CLS = {
     "gelu": nn.GELU,
     "relu": nn.ReLU,
 }
-KERNELS_REPO_ID = "kernels-community/activation"
 
 
 def get_activation(act_fn: str) -> nn.Module:
@@ -92,27 +90,6 @@ class GELU(nn.Module):
         return hidden_states
 
 
-# TODO: validation checks / consider making Python classes of activations like `transformers`
-# All of these are temporary for now.
-class CUDAOptimizedGELU(GELU):
-    def __init__(self, *args, **kwargs):
-        from kernels import get_kernel
-
-        activation = get_kernel("kernels-community/activation", revision="add_more_act")
-        approximate = kwargs.get("approximate", "none")
-
-        super().__init__(*args, **kwargs)
-        if approximate == "none":
-            self.act_fn = activation.layers.Gelu()
-        elif approximate == "tanh":
-            self.act_fn = activation.layers.GeluTanh()
-
-    def forward(self, hidden_states):
-        hidden_states = self.proj(hidden_states)
-        hidden_states = self.act_fn(hidden_states)
-        return hidden_states
-
-
 class GEGLU(nn.Module):
     r"""
     A [variant](https://huggingface.co/papers/2002.05202) of the gated linear unit activation function.

src/diffusers/models/normalization.py

@@ -20,20 +20,11 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from ..utils import is_kernels_available, is_torch_npu_available, is_torch_version
-from ..utils.constants import DIFFUSERS_ENABLE_HUB_KERNELS
-from ..utils.kernels_utils import use_kernel_forward_from_hub
+from ..utils import is_torch_npu_available, is_torch_version
 from .activations import get_activation
 from .embeddings import CombinedTimestepLabelEmbeddings, PixArtAlphaCombinedTimestepSizeEmbeddings
 
 
-if is_kernels_available() and DIFFUSERS_ENABLE_HUB_KERNELS:
-    from kernels import get_kernel
-
-    activation = get_kernel("kernels-community/activation", revision="add_more_act")
-    silu_kernel = activation.layers.Silu
-
-
 class AdaLayerNorm(nn.Module):
     r"""
     Norm layer modified to incorporate timestep embeddings.
@@ -66,10 +57,7 @@ class AdaLayerNorm(nn.Module):
         else:
             self.emb = None
 
-        if DIFFUSERS_ENABLE_HUB_KERNELS:
-            self.silu = silu_kernel()
-        else:
-            self.silu = nn.SiLU()
+        self.silu = nn.SiLU()
         self.linear = nn.Linear(embedding_dim, output_dim)
         self.norm = nn.LayerNorm(output_dim // 2, norm_eps, norm_elementwise_affine)
 
@@ -156,10 +144,7 @@ class AdaLayerNormZero(nn.Module):
         else:
             self.emb = None
 
-        if DIFFUSERS_ENABLE_HUB_KERNELS:
-            self.silu = silu_kernel()
-        else:
-            self.silu = nn.SiLU()
+        self.silu = nn.SiLU()
         self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=bias)
         if norm_type == "layer_norm":
             self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)
@@ -198,10 +183,7 @@ class AdaLayerNormZeroSingle(nn.Module):
     def __init__(self, embedding_dim: int, norm_type="layer_norm", bias=True):
         super().__init__()
 
-        if DIFFUSERS_ENABLE_HUB_KERNELS:
-            self.silu = silu_kernel()
-        else:
-            self.silu = nn.SiLU()
+        self.silu = nn.SiLU()
         self.linear = nn.Linear(embedding_dim, 3 * embedding_dim, bias=bias)
         if norm_type == "layer_norm":
             self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)
@@ -353,10 +335,7 @@ class AdaLayerNormContinuous(nn.Module):
         norm_type="layer_norm",
     ):
         super().__init__()
-        if DIFFUSERS_ENABLE_HUB_KERNELS:
-            self.silu = silu_kernel()
-        else:
-            self.silu = nn.SiLU()
+        self.silu = nn.SiLU()
         self.linear = nn.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias)
         if norm_type == "layer_norm":
             self.norm = LayerNorm(embedding_dim, eps, elementwise_affine, bias)
@@ -529,7 +508,6 @@ else:
             return F.layer_norm(input, self.dim, self.weight, self.bias, self.eps)
 
 
-@use_kernel_forward_from_hub("RMSNorm")
 class RMSNorm(nn.Module):
     r"""
     RMS Norm as introduced in https://huggingface.co/papers/1910.07467 by Zhang et al.

src/diffusers/models/transformers/transformer_flux.py

@@ -22,8 +22,7 @@ import torch.nn.functional as F
 
 from ...configuration_utils import ConfigMixin, register_to_config
 from ...loaders import FluxTransformer2DLoadersMixin, FromOriginalModelMixin, PeftAdapterMixin
-from ...utils import USE_PEFT_BACKEND, is_kernels_available, logging, scale_lora_layers, unscale_lora_layers
-from ...utils.constants import DIFFUSERS_ENABLE_HUB_KERNELS
+from ...utils import USE_PEFT_BACKEND, logging, scale_lora_layers, unscale_lora_layers
 from ...utils.torch_utils import maybe_allow_in_graph
 from ..attention import AttentionMixin, AttentionModuleMixin, FeedForward
 from ..attention_dispatch import dispatch_attention_fn
@@ -41,12 +40,6 @@ from ..normalization import AdaLayerNormContinuous, AdaLayerNormZero, AdaLayerNo
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
-if is_kernels_available() and DIFFUSERS_ENABLE_HUB_KERNELS:
-    from kernels import get_kernel
-
-    activation = get_kernel("kernels-community/activation", revision="add_more_act")
-    gelu_tanh_kernel = activation.layers.GeluTanh
-
 
 def _get_projections(attn: "FluxAttention", hidden_states, encoder_hidden_states=None):
     query = attn.to_q(hidden_states)
@@ -307,14 +300,8 @@ class FluxAttention(torch.nn.Module, AttentionModuleMixin):
         self.added_kv_proj_dim = added_kv_proj_dim
         self.added_proj_bias = added_proj_bias
 
-        if DIFFUSERS_ENABLE_HUB_KERNELS:
-            from ..normalization import RMSNorm
-
-            self.norm_q = RMSNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
-            self.norm_k = RMSNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
-        else:
-            self.norm_q = torch.nn.RMSNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
-            self.norm_k = torch.nn.RMSNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
+        self.norm_q = torch.nn.RMSNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
+        self.norm_k = torch.nn.RMSNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
         self.to_q = torch.nn.Linear(query_dim, self.inner_dim, bias=bias)
         self.to_k = torch.nn.Linear(query_dim, self.inner_dim, bias=bias)
         self.to_v = torch.nn.Linear(query_dim, self.inner_dim, bias=bias)
@@ -325,14 +312,8 @@ class FluxAttention(torch.nn.Module, AttentionModuleMixin):
             self.to_out.append(torch.nn.Dropout(dropout))
 
         if added_kv_proj_dim is not None:
-            if DIFFUSERS_ENABLE_HUB_KERNELS:
-                from ..normalization import RMSNorm
-
-                self.norm_added_q = RMSNorm(dim_head, eps=eps)
-                self.norm_added_k = RMSNorm(dim_head, eps=eps)
-            else:
-                self.norm_added_q = torch.nn.RMSNorm(dim_head, eps=eps)
-                self.norm_added_k = torch.nn.RMSNorm(dim_head, eps=eps)
+            self.norm_added_q = torch.nn.RMSNorm(dim_head, eps=eps)
+            self.norm_added_k = torch.nn.RMSNorm(dim_head, eps=eps)
             self.add_q_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
             self.add_k_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
             self.add_v_proj = torch.nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
@@ -370,11 +351,6 @@ class FluxSingleTransformerBlock(nn.Module):
         self.norm = AdaLayerNormZeroSingle(dim)
         self.proj_mlp = nn.Linear(dim, self.mlp_hidden_dim)
         self.act_mlp = nn.GELU(approximate="tanh")
-        # if not DIFFUSERS_ENABLE_HUB_KERNELS:
-        #     self.act_mlp = nn.GELU(approximate="tanh")
-        # else:
-        #     self.act_mlp = gelu_tanh_kernel()
-
         self.proj_out = nn.Linear(dim + self.mlp_hidden_dim, dim)
 
         self.attn = FluxAttention(

src/diffusers/models/unets/unet_1d.py

@@ -82,6 +82,7 @@ class UNet1DModel(ModelMixin, ConfigMixin):
         out_channels: int = 2,
         extra_in_channels: int = 0,
         time_embedding_type: str = "fourier",
+        time_embedding_dim: Optional[int] = None,
         flip_sin_to_cos: bool = True,
         use_timestep_embedding: bool = False,
         freq_shift: float = 0.0,
@@ -100,15 +101,23 @@ class UNet1DModel(ModelMixin, ConfigMixin):
 
         # time
         if time_embedding_type == "fourier":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 2
+            if time_embed_dim % 2 != 0:
+                raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.")
             self.time_proj = GaussianFourierProjection(
-                embedding_size=8, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
+                embedding_size=time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos
             )
-            timestep_input_dim = 2 * block_out_channels[0]
+            timestep_input_dim = time_embed_dim
         elif time_embedding_type == "positional":
+            time_embed_dim = time_embedding_dim or block_out_channels[0] * 4
             self.time_proj = Timesteps(
                 block_out_channels[0], flip_sin_to_cos=flip_sin_to_cos, downscale_freq_shift=freq_shift
             )
             timestep_input_dim = block_out_channels[0]
+        else:
+            raise ValueError(
+                f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`."
+            )
 
         if use_timestep_embedding:
             time_embed_dim = block_out_channels[0] * 4

src/diffusers/pipelines/__init__.py

@@ -285,6 +285,7 @@ else:
     ]
     _import_structure["lumina"] = ["LuminaPipeline", "LuminaText2ImgPipeline"]
     _import_structure["lumina2"] = ["Lumina2Pipeline", "Lumina2Text2ImgPipeline"]
+    _import_structure["lucy"] = ["LucyEditPipeline"]
     _import_structure["marigold"].extend(
         [
             "MarigoldDepthPipeline",
@@ -682,6 +683,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             LEditsPPPipelineStableDiffusionXL,
         )
         from .ltx import LTXConditionPipeline, LTXImageToVideoPipeline, LTXLatentUpsamplePipeline, LTXPipeline
+        from .lucy import LucyEditPipeline
         from .lumina import LuminaPipeline, LuminaText2ImgPipeline
         from .lumina2 import Lumina2Pipeline, Lumina2Text2ImgPipeline
         from .marigold import (

src/diffusers/pipelines/lucy/__init__.py

@@ -0,0 +1,47 @@
+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    get_objects_from_module,
+    is_torch_available,
+    is_transformers_available,
+)
+
+
+_dummy_objects = {}
+_import_structure = {}
+
+
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["pipeline_lucy_edit"] = ["LucyEditPipeline"]
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *
+    else:
+        from .pipeline_lucy_edit import LucyEditPipeline
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)

src/diffusers/pipelines/lucy/pipeline_lucy_edit.py

@@ -0,0 +1,736 @@
+# Copyright 2025 The Wan Team and The HuggingFace Team. All rights reserved.
+# Copyright 2025 The Decart AI 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.
+#
+# Modifications by Decart AI Team:
+# - Based on pipeline_wan.py, but with supports recieving a condition video appended to the channel dimension.
+
+import html
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import regex as re
+import torch
+from PIL import Image
+from transformers import AutoTokenizer, UMT5EncoderModel
+
+from ...callbacks import MultiPipelineCallbacks, PipelineCallback
+from ...loaders import WanLoraLoaderMixin
+from ...models import AutoencoderKLWan, WanTransformer3DModel
+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 LucyPipelineOutput
+
+
+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
+        >>> from typing import List
+
+        >>> import torch
+        >>> from PIL import Image
+
+        >>> from diffusers import AutoencoderKLWan, WanTransformer3DModel
+        >>> from diffusers.pipelines.lucy.pipeline_lucy_edit import LucyEditPipeline
+        >>> from diffusers.utils import export_to_video, load_video
+
+        >>> # Arguments
+        >>> url = "https://d2drjpuinn46lb.cloudfront.net/painter_original_edit.mp4"
+        >>> prompt = "Change the apron and blouse to a classic clown costume: satin polka-dot jumpsuit in bright primary colors, ruffled white collar, oversized pom-pom buttons, white gloves, oversized red shoes, red foam nose; soft window light from left, eye-level medium shot, natural folds and fabric highlights."
+        >>> negative_prompt = ""
+        >>> num_frames = 81
+        >>> height = 480
+        >>> width = 832
+
+
+        >>> # Load video
+        >>> def convert_video(video: List[Image.Image]) -> List[Image.Image]:
+        ...     video = load_video(url)[:num_frames]
+        ...     video = [video[i].resize((width, height)) for i in range(num_frames)]
+        ...     return video
+
+
+        >>> video = load_video(url, convert_method=convert_video)
+
+        >>> # Load model
+        >>> model_id = "decart-ai/Lucy-Edit-Dev"
+        >>> vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
+        >>> pipe = LucyEditPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
+        >>> pipe.to("cuda")
+
+        >>> # Generate video
+        >>> output = pipe(
+        ...     prompt=prompt,
+        ...     video=video,
+        ...     negative_prompt=negative_prompt,
+        ...     height=480,
+        ...     width=832,
+        ...     num_frames=81,
+        ...     guidance_scale=5.0,
+        ... ).frames[0]
+
+        >>> # Export video
+        >>> export_to_video(output, "output.mp4", fps=24)
+        ```
+"""
+
+
+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 LucyEditPipeline(DiffusionPipeline, WanLoraLoaderMixin):
+    r"""
+    Pipeline for video-to-video generation using Lucy Edit.
+
+    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.
+        transformer_2 ([`WanTransformer3DModel`], *optional*):
+            Conditional Transformer to denoise the input latents during the low-noise stage. If provided, enables
+            two-stage denoising where `transformer` handles high-noise stages and `transformer_2` handles low-noise
+            stages. If not provided, only `transformer` is used.
+        boundary_ratio (`float`, *optional*, defaults to `None`):
+            Ratio of total timesteps to use as the boundary for switching between transformers in two-stage denoising.
+            The actual boundary timestep is calculated as `boundary_ratio * num_train_timesteps`. When provided,
+            `transformer` handles timesteps >= boundary_timestep and `transformer_2` handles timesteps <
+            boundary_timestep. If `None`, only `transformer` is used for the entire denoising process.
+    """
+
+    model_cpu_offload_seq = "text_encoder->transformer->transformer_2->vae"
+    _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+    _optional_components = ["transformer", "transformer_2"]
+
+    def __init__(
+        self,
+        tokenizer: AutoTokenizer,
+        text_encoder: UMT5EncoderModel,
+        vae: AutoencoderKLWan,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        transformer: Optional[WanTransformer3DModel] = None,
+        transformer_2: Optional[WanTransformer3DModel] = None,
+        boundary_ratio: Optional[float] = None,
+        expand_timesteps: bool = False,  # Wan2.2 ti2v
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            transformer=transformer,
+            scheduler=scheduler,
+            transformer_2=transformer_2,
+        )
+        self.register_to_config(boundary_ratio=boundary_ratio)
+        self.register_to_config(expand_timesteps=expand_timesteps)
+        self.vae_scale_factor_temporal = self.vae.config.scale_factor_temporal if getattr(self, "vae", None) else 4
+        self.vae_scale_factor_spatial = self.vae.config.scale_factor_spatial 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,
+        video,
+        prompt,
+        negative_prompt,
+        height,
+        width,
+        prompt_embeds=None,
+        negative_prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+        guidance_scale_2=None,
+    ):
+        if height % 16 != 0 or width % 16 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 16 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 self.config.boundary_ratio is None and guidance_scale_2 is not None:
+            raise ValueError("`guidance_scale_2` is only supported when the pipeline's `boundary_ratio` is not None.")
+
+        if video is None:
+            raise ValueError("`video` is required, received None.")
+
+    def prepare_latents(
+        self,
+        video: Optional[torch.Tensor] = None,
+        batch_size: int = 1,
+        num_channels_latents: int = 16,
+        height: int = 480,
+        width: int = 832,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        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."
+            )
+
+        num_latent_frames = (
+            (video.size(2) - 1) // self.vae_scale_factor_temporal + 1 if latents is None else latents.size(1)
+        )
+        shape = (
+            batch_size,
+            num_channels_latents,
+            num_latent_frames,
+            height // self.vae_scale_factor_spatial,
+            width // self.vae_scale_factor_spatial,
+        )
+        # Prepare noise latents
+        if latents is None:
+            latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        else:
+            latents = latents.to(device)
+
+        # Prepare condition latents
+        condition_latents = [
+            retrieve_latents(self.vae.encode(vid.unsqueeze(0)), sample_mode="argmax") for vid in video
+        ]
+
+        condition_latents = torch.cat(condition_latents, dim=0).to(dtype)
+
+        latents_mean = (
+            torch.tensor(self.vae.config.latents_mean).view(1, self.vae.config.z_dim, 1, 1, 1).to(device, dtype)
+        )
+        latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+            device, dtype
+        )
+
+        condition_latents = (condition_latents - latents_mean) * latents_std
+
+        # Check shapes
+        assert latents.shape == condition_latents.shape, (
+            f"Latents shape {latents.shape} does not match expected shape {condition_latents.shape}. Please check the input."
+        )
+
+        return latents, condition_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,
+        video: List[Image.Image],
+        prompt: Union[str, List[str]] = None,
+        negative_prompt: Union[str, List[str]] = None,
+        height: int = 480,
+        width: int = 832,
+        num_frames: int = 81,
+        num_inference_steps: int = 50,
+        guidance_scale: float = 5.0,
+        guidance_scale_2: Optional[float] = None,
+        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:
+            video (`List[Image.Image]`):
+                The video to use as the condition for the video generation.
+            prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to guide the image generation. If not defined, pass `prompt_embeds` instead.
+            negative_prompt (`str` or `List[str]`, *optional*):
+                The prompt or prompts to avoid during image generation. If not defined, pass `negative_prompt_embeds`
+                instead. Ignored when not using guidance (`guidance_scale` < `1`).
+            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://huggingface.co/papers/2207.12598). `guidance_scale` is defined as `w` of equation 2.
+                of [Imagen Paper](https://huggingface.co/papers/2205.11487). 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.
+            guidance_scale_2 (`float`, *optional*, defaults to `None`):
+                Guidance scale for the low-noise stage transformer (`transformer_2`). If `None` and the pipeline's
+                `boundary_ratio` is not None, uses the same value as `guidance_scale`. Only used when `transformer_2`
+                and the pipeline's `boundary_ratio` are not None.
+            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 [`LucyPipelineOutput`] 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.
+            max_sequence_length (`int`, defaults to `512`):
+                The maximum sequence length of the text encoder. If the prompt is longer than this, it will be
+                truncated. If the prompt is shorter, it will be padded to this length.
+
+        Examples:
+
+        Returns:
+            [`~LucyPipelineOutput`] or `tuple`:
+                If `return_dict` is `True`, [`LucyPipelineOutput`] 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
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            video,
+            prompt,
+            negative_prompt,
+            height,
+            width,
+            prompt_embeds,
+            negative_prompt_embeds,
+            callback_on_step_end_tensor_inputs,
+            guidance_scale_2,
+        )
+
+        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)
+
+        if self.config.boundary_ratio is not None and guidance_scale_2 is None:
+            guidance_scale_2 = guidance_scale
+
+        self._guidance_scale = guidance_scale
+        self._guidance_scale_2 = guidance_scale_2
+        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]
+
+        # 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,
+        )
+
+        transformer_dtype = self.transformer.dtype if self.transformer is not None else self.transformer_2.dtype
+        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
+        num_channels_latents = (
+            self.transformer.config.out_channels
+            if self.transformer is not None
+            else self.transformer_2.config.out_channels
+        )
+        video = self.video_processor.preprocess_video(video, height=height, width=width).to(
+            device, dtype=torch.float32
+        )
+        latents, condition_latents = self.prepare_latents(
+            video,
+            batch_size * num_videos_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            torch.float32,
+            device,
+            generator,
+            latents,
+        )
+
+        mask = torch.ones(latents.shape, dtype=torch.float32, device=device)
+
+        # 6. Denoising loop
+        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
+        self._num_timesteps = len(timesteps)
+
+        if self.config.boundary_ratio is not None:
+            boundary_timestep = self.config.boundary_ratio * self.scheduler.config.num_train_timesteps
+        else:
+            boundary_timestep = None
+
+        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
+
+                if boundary_timestep is None or t >= boundary_timestep:
+                    # wan2.1 or high-noise stage in wan2.2
+                    current_model = self.transformer
+                    current_guidance_scale = guidance_scale
+                else:
+                    # low-noise stage in wan2.2
+                    current_model = self.transformer_2
+                    current_guidance_scale = guidance_scale_2
+
+                # latent_model_input = latents.to(transformer_dtype)
+                latent_model_input = torch.cat([latents, condition_latents], dim=1).to(transformer_dtype)
+                # latent_model_input = torch.cat([latents, latents], dim=1).to(transformer_dtype)
+                if self.config.expand_timesteps:
+                    # seq_len: num_latent_frames * latent_height//2 * latent_width//2
+                    temp_ts = (mask[0][0][:, ::2, ::2] * t).flatten()
+                    # batch_size, seq_len
+                    timestep = temp_ts.unsqueeze(0).expand(latents.shape[0], -1)
+                else:
+                    timestep = t.expand(latents.shape[0])
+
+                with current_model.cache_context("cond"):
+                    noise_pred = current_model(
+                        hidden_states=latent_model_input,
+                        timestep=timestep,
+                        encoder_hidden_states=prompt_embeds,
+                        attention_kwargs=attention_kwargs,
+                        return_dict=False,
+                    )[0]
+
+                if self.do_classifier_free_guidance:
+                    with current_model.cache_context("uncond"):
+                        noise_uncond = current_model(
+                            hidden_states=latent_model_input,
+                            timestep=timestep,
+                            encoder_hidden_states=negative_prompt_embeds,
+                            attention_kwargs=attention_kwargs,
+                            return_dict=False,
+                        )[0]
+                    noise_pred = noise_uncond + current_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(self.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 LucyPipelineOutput(frames=video)

src/diffusers/pipelines/lucy/pipeline_output.py

@@ -0,0 +1,20 @@
+from dataclasses import dataclass
+
+import torch
+
+from diffusers.utils import BaseOutput
+
+
+@dataclass
+class LucyPipelineOutput(BaseOutput):
+    r"""
+    Output class for Lucy pipelines.
+
+    Args:
+        frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
+            List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing
+            denoised PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape
+            `(batch_size, num_frames, channels, height, width)`.
+    """
+
+    frames: torch.Tensor

src/diffusers/pipelines/wan/pipeline_wan_vace.py

@@ -152,16 +152,26 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
         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`]):
+        transformer ([`WanVACETransformer3DModel`]):
             Conditional Transformer to denoise the input latents.
+        transformer_2 ([`WanVACETransformer3DModel`], *optional*):
+            Conditional Transformer to denoise the input latents during the low-noise stage. In two-stage denoising,
+            `transformer` handles high-noise stages and `transformer_2` handles low-noise stages. If not provided, only
+            `transformer` is used.
         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.
+        boundary_ratio (`float`, *optional*, defaults to `None`):
+            Ratio of total timesteps to use as the boundary for switching between transformers in two-stage denoising.
+            The actual boundary timestep is calculated as `boundary_ratio * num_train_timesteps`. When provided,
+            `transformer` handles timesteps >= boundary_timestep and `transformer_2` handles timesteps <
+            boundary_timestep. If `None`, only `transformer` is used for the entire denoising process.
     """
 
     model_cpu_offload_seq = "text_encoder->transformer->vae"
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+    _optional_components = ["transformer_2"]
 
     def __init__(
         self,
@@ -170,6 +180,8 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
         transformer: WanVACETransformer3DModel,
         vae: AutoencoderKLWan,
         scheduler: FlowMatchEulerDiscreteScheduler,
+        transformer_2: WanVACETransformer3DModel = None,
+        boundary_ratio: Optional[float] = None,
     ):
         super().__init__()
 
@@ -178,9 +190,10 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
             text_encoder=text_encoder,
             tokenizer=tokenizer,
             transformer=transformer,
+            transformer_2=transformer_2,
             scheduler=scheduler,
         )
-
+        self.register_to_config(boundary_ratio=boundary_ratio)
         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)
@@ -321,6 +334,7 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
         video=None,
         mask=None,
         reference_images=None,
+        guidance_scale_2=None,
     ):
         base = self.vae_scale_factor_spatial * self.transformer.config.patch_size[1]
         if height % base != 0 or width % base != 0:
@@ -332,6 +346,8 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
             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 self.config.boundary_ratio is None and guidance_scale_2 is not None:
+            raise ValueError("`guidance_scale_2` is only supported when the pipeline's `boundary_ratio` is not None.")
 
         if prompt is not None and prompt_embeds is not None:
             raise ValueError(
@@ -667,6 +683,7 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
         num_frames: int = 81,
         num_inference_steps: int = 50,
         guidance_scale: float = 5.0,
+        guidance_scale_2: Optional[float] = None,
         num_videos_per_prompt: Optional[int] = 1,
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         latents: Optional[torch.Tensor] = None,
@@ -728,6 +745,10 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
                 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.
+            guidance_scale_2 (`float`, *optional*, defaults to `None`):
+                Guidance scale for the low-noise stage transformer (`transformer_2`). If `None` and the pipeline's
+                `boundary_ratio` is not None, uses the same value as `guidance_scale`. Only used when `transformer_2`
+                and the pipeline's `boundary_ratio` are not None.
             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*):
@@ -793,6 +814,7 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
             video,
             mask,
             reference_images,
+            guidance_scale_2,
         )
 
         if num_frames % self.vae_scale_factor_temporal != 1:
@@ -802,7 +824,11 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
             num_frames = num_frames // self.vae_scale_factor_temporal * self.vae_scale_factor_temporal + 1
         num_frames = max(num_frames, 1)
 
+        if self.config.boundary_ratio is not None and guidance_scale_2 is None:
+            guidance_scale_2 = guidance_scale
+
         self._guidance_scale = guidance_scale
+        self._guidance_scale_2 = guidance_scale_2
         self._attention_kwargs = attention_kwargs
         self._current_timestep = None
         self._interrupt = False
@@ -896,36 +922,53 @@ class WanVACEPipeline(DiffusionPipeline, WanLoraLoaderMixin):
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
         self._num_timesteps = len(timesteps)
 
+        if self.config.boundary_ratio is not None:
+            boundary_timestep = self.config.boundary_ratio * self.scheduler.config.num_train_timesteps
+        else:
+            boundary_timestep = None
+
         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
+
+                if boundary_timestep is None or t >= boundary_timestep:
+                    # wan2.1 or high-noise stage in wan2.2
+                    current_model = self.transformer
+                    current_guidance_scale = guidance_scale
+                else:
+                    # low-noise stage in wan2.2
+                    current_model = self.transformer_2
+                    current_guidance_scale = guidance_scale_2
+
                 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(
+                with current_model.cache_context("cond"):
+                    noise_pred = current_model(
                         hidden_states=latent_model_input,
                         timestep=timestep,
-                        encoder_hidden_states=negative_prompt_embeds,
+                        encoder_hidden_states=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)
+
+                if self.do_classifier_free_guidance:
+                    with current_model.cache_context("uncond"):
+                        noise_uncond = current_model(
+                            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]

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -1592,6 +1592,21 @@ class LTXPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class LucyEditPipeline(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 Lumina2Pipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

src/diffusers/utils/kernels_utils.py

@@ -1,5 +1,3 @@
-from typing import Union
-
 from ..utils import get_logger
 from .import_utils import is_kernels_available
 
@@ -23,42 +21,3 @@ def _get_fa3_from_hub():
         except Exception as e:
             logger.error(f"An error occurred while fetching kernel '{_DEFAULT_HUB_ID_FA3}' from the Hub: {e}")
             raise
-
-
-if is_kernels_available():
-    from kernels import (
-        Device,
-        LayerRepository,
-        register_kernel_mapping,
-        replace_kernel_forward_from_hub,
-        use_kernel_forward_from_hub,
-    )
-
-    _KERNEL_MAPPING: dict[str, dict[Union[Device, str], LayerRepository]] = {
-        "RMSNorm": {
-            "cuda": LayerRepository(repo_id="kernels-community/liger_kernels", layer_name="LigerRMSNorm"),
-        },
-    }
-
-    register_kernel_mapping(_KERNEL_MAPPING)
-
-else:
-    # Stub to make decorators int transformers work when `kernels`
-    # is not installed.
-    def use_kernel_forward_from_hub(*args, **kwargs):
-        def decorator(cls):
-            return cls
-
-        return decorator
-
-    class LayerRepository:
-        def __init__(self, *args, **kwargs):
-            raise RuntimeError("LayerRepository requires `kernels` to be installed. Run `pip install kernels`.")
-
-    def replace_kernel_forward_from_hub(*args, **kwargs):
-        raise RuntimeError(
-            "replace_kernel_forward_from_hub requires `kernels` to be installed. Run `pip install kernels`."
-        )
-
-    def register_kernel_mapping(*args, **kwargs):
-        raise RuntimeError("register_kernel_mapping requires `kernels` to be installed. Run `pip install kernels`.")

tests/pipelines/wan/test_wan_vace.py

@@ -87,6 +87,7 @@ class WanVACEPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             "scheduler": scheduler,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
+            "transformer_2": None,
         }
         return components