[Pipeline] AnimateDiff SDXL (#6721)

* update conversion script to handle motion adapter sdxl checkpoint

* add animatediff xl

* handle addition_embed_type

* fix output

* update

* add imports

* make fix-copies

* add decode latents

* update docstrings

* add animatediff sdxl to docs

* remove unnecessary lines

* update example

* add test

* revert conv_in conv_out kernel param

* remove unused param addition_embed_type_num_heads

* latest IPAdapter impl

* make fix-copies

* fix return

* add IPAdapterTesterMixin to tests

* fix return

* revert based on suggestion

* add freeinit

* fix test_to_dtype test

* use StableDiffusionMixin instead of different helper methods

* fix progress bar iterations

* apply suggestions from review

* hardcode flip_sin_to_cos and freq_shift

* make fix-copies

* fix ip adapter implementation

* fix last failing test

* make style

* Update docs/source/en/api/pipelines/animatediff.md
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

* remove todo

* fix doc-builder errors

---------
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

docs/source/en/api/pipelines/animatediff.md

@@ -101,6 +101,53 @@ AnimateDiff tends to work better with finetuned Stable Diffusion models. If you
 
 </Tip>
 
+### AnimateDiffSDXLPipeline
+
+AnimateDiff can also be used with SDXL models. This is currently an experimental feature as only a beta release of the motion adapter checkpoint is available.
+
+```python
+import torch
+from diffusers.models import MotionAdapter
+from diffusers import AnimateDiffSDXLPipeline, DDIMScheduler
+from diffusers.utils import export_to_gif
+
+adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-sdxl-beta", torch_dtype=torch.float16)
+
+model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+scheduler = DDIMScheduler.from_pretrained(
+    model_id,
+    subfolder="scheduler",
+    clip_sample=False,
+    timestep_spacing="linspace",
+    beta_schedule="linear",
+    steps_offset=1,
+)
+pipe = AnimateDiffSDXLPipeline.from_pretrained(
+    model_id,
+    motion_adapter=adapter,
+    scheduler=scheduler,
+    torch_dtype=torch.float16,
+    variant="fp16",
+).to("cuda")
+
+# enable memory savings
+pipe.enable_vae_slicing()
+pipe.enable_vae_tiling()
+
+output = pipe(
+    prompt="a panda surfing in the ocean, realistic, high quality",
+    negative_prompt="low quality, worst quality",
+    num_inference_steps=20,
+    guidance_scale=8,
+    width=1024,
+    height=1024,
+    num_frames=16,
+)
+
+frames = output.frames[0]
+export_to_gif(frames, "animation.gif")
+```
+
 ### AnimateDiffVideoToVideoPipeline
 
 AnimateDiff can also be used to generate visually similar videos or enable style/character/background or other edits starting from an initial video, allowing you to seamlessly explore creative possibilities.
@@ -522,6 +569,12 @@ export_to_gif(frames, "animatelcm-motion-lora.gif")
   - all
   - __call__
 
+## AnimateDiffSDXLPipeline
+
+[[autodoc]] AnimateDiffSDXLPipeline
+  - all
+  - __call__
+
 ## AnimateDiffVideoToVideoPipeline
 
 [[autodoc]] AnimateDiffVideoToVideoPipeline

scripts/convert_animatediff_motion_module_to_diffusers.py

@@ -31,6 +31,7 @@ def get_args():
     parser.add_argument("--output_path", type=str, required=True)
     parser.add_argument("--use_motion_mid_block", action="store_true")
     parser.add_argument("--motion_max_seq_length", type=int, default=32)
+    parser.add_argument("--block_out_channels", nargs="+", default=[320, 640, 1280, 1280], type=int)
     parser.add_argument("--save_fp16", action="store_true")
 
     return parser.parse_args()
@@ -49,11 +50,13 @@ if __name__ == "__main__":
 
     conv_state_dict = convert_motion_module(state_dict)
     adapter = MotionAdapter(
-        use_motion_mid_block=args.use_motion_mid_block, motion_max_seq_length=args.motion_max_seq_length
+        block_out_channels=args.block_out_channels,
+        use_motion_mid_block=args.use_motion_mid_block,
+        motion_max_seq_length=args.motion_max_seq_length,
     )
     # skip loading position embeddings
     adapter.load_state_dict(conv_state_dict, strict=False)
     adapter.save_pretrained(args.output_path)
 
     if args.save_fp16:
-        adapter.to(torch.float16).save_pretrained(args.output_path, variant="fp16")
+        adapter.to(dtype=torch.float16).save_pretrained(args.output_path, variant="fp16")

src/diffusers/__init__.py

@@ -216,6 +216,7 @@ else:
             "AmusedInpaintPipeline",
             "AmusedPipeline",
             "AnimateDiffPipeline",
+            "AnimateDiffSDXLPipeline",
             "AnimateDiffVideoToVideoPipeline",
             "AudioLDM2Pipeline",
             "AudioLDM2ProjectionModel",
@@ -595,6 +596,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AmusedInpaintPipeline,
             AmusedPipeline,
             AnimateDiffPipeline,
+            AnimateDiffSDXLPipeline,
             AnimateDiffVideoToVideoPipeline,
             AudioLDM2Pipeline,
             AudioLDM2ProjectionModel,

src/diffusers/models/unets/unet_3d_blocks.py

@@ -121,6 +121,7 @@ def get_down_block(
             raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockMotion")
         return CrossAttnDownBlockMotion(
             num_layers=num_layers,
+            transformer_layers_per_block=transformer_layers_per_block,
             in_channels=in_channels,
             out_channels=out_channels,
             temb_channels=temb_channels,
@@ -255,6 +256,7 @@ def get_up_block(
             raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockMotion")
         return CrossAttnUpBlockMotion(
             num_layers=num_layers,
+            transformer_layers_per_block=transformer_layers_per_block,
             in_channels=in_channels,
             out_channels=out_channels,
             prev_output_channel=prev_output_channel,

src/diffusers/models/unets/unet_motion_model.py

@@ -211,6 +211,8 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
         norm_num_groups: int = 32,
         norm_eps: float = 1e-5,
         cross_attention_dim: int = 1280,
+        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
+        reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
         use_linear_projection: bool = False,
         num_attention_heads: Union[int, Tuple[int, ...]] = 8,
         motion_max_seq_length: int = 32,
@@ -218,6 +220,9 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
         use_motion_mid_block: int = True,
         encoder_hid_dim: Optional[int] = None,
         encoder_hid_dim_type: Optional[str] = None,
+        addition_embed_type: Optional[str] = None,
+        addition_time_embed_dim: Optional[int] = None,
+        projection_class_embeddings_input_dim: Optional[int] = None,
         time_cond_proj_dim: Optional[int] = None,
     ):
         super().__init__()
@@ -240,6 +245,21 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
                 f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
             )
 
+        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
+            )
+
+        if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:
+            for layer_number_per_block in transformer_layers_per_block:
+                if isinstance(layer_number_per_block, list):
+                    raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.")
+
         # input
         conv_in_kernel = 3
         conv_out_kernel = 3
@@ -260,6 +280,10 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
         if encoder_hid_dim_type is None:
             self.encoder_hid_proj = None
 
+        if addition_embed_type == "text_time":
+            self.add_time_proj = Timesteps(addition_time_embed_dim, True, 0)
+            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+
         # class embedding
         self.down_blocks = nn.ModuleList([])
         self.up_blocks = nn.ModuleList([])
@@ -267,6 +291,15 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
         if isinstance(num_attention_heads, int):
             num_attention_heads = (num_attention_heads,) * len(down_block_types)
 
+        if isinstance(cross_attention_dim, int):
+            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
+
+        if isinstance(layers_per_block, int):
+            layers_per_block = [layers_per_block] * len(down_block_types)
+
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
+
         # down
         output_channel = block_out_channels[0]
         for i, down_block_type in enumerate(down_block_types):
@@ -276,7 +309,7 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
 
             down_block = get_down_block(
                 down_block_type,
-                num_layers=layers_per_block,
+                num_layers=layers_per_block[i],
                 in_channels=input_channel,
                 out_channels=output_channel,
                 temb_channels=time_embed_dim,
@@ -284,13 +317,14 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
                 resnet_eps=norm_eps,
                 resnet_act_fn=act_fn,
                 resnet_groups=norm_num_groups,
-                cross_attention_dim=cross_attention_dim,
+                cross_attention_dim=cross_attention_dim[i],
                 num_attention_heads=num_attention_heads[i],
                 downsample_padding=downsample_padding,
                 use_linear_projection=use_linear_projection,
                 dual_cross_attention=False,
                 temporal_num_attention_heads=motion_num_attention_heads,
                 temporal_max_seq_length=motion_max_seq_length,
+                transformer_layers_per_block=transformer_layers_per_block[i],
             )
             self.down_blocks.append(down_block)
 
@@ -302,13 +336,14 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
                 resnet_eps=norm_eps,
                 resnet_act_fn=act_fn,
                 output_scale_factor=mid_block_scale_factor,
-                cross_attention_dim=cross_attention_dim,
+                cross_attention_dim=cross_attention_dim[-1],
                 num_attention_heads=num_attention_heads[-1],
                 resnet_groups=norm_num_groups,
                 dual_cross_attention=False,
                 use_linear_projection=use_linear_projection,
                 temporal_num_attention_heads=motion_num_attention_heads,
                 temporal_max_seq_length=motion_max_seq_length,
+                transformer_layers_per_block=transformer_layers_per_block[-1],
             )
 
         else:
@@ -318,11 +353,12 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
                 resnet_eps=norm_eps,
                 resnet_act_fn=act_fn,
                 output_scale_factor=mid_block_scale_factor,
-                cross_attention_dim=cross_attention_dim,
+                cross_attention_dim=cross_attention_dim[-1],
                 num_attention_heads=num_attention_heads[-1],
                 resnet_groups=norm_num_groups,
                 dual_cross_attention=False,
                 use_linear_projection=use_linear_projection,
+                transformer_layers_per_block=transformer_layers_per_block[-1],
             )
 
         # count how many layers upsample the images
@@ -331,6 +367,9 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
         # up
         reversed_block_out_channels = list(reversed(block_out_channels))
         reversed_num_attention_heads = list(reversed(num_attention_heads))
+        reversed_layers_per_block = list(reversed(layers_per_block))
+        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
+        reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
 
         output_channel = reversed_block_out_channels[0]
         for i, up_block_type in enumerate(up_block_types):
@@ -349,7 +388,7 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
 
             up_block = get_up_block(
                 up_block_type,
-                num_layers=layers_per_block + 1,
+                num_layers=reversed_layers_per_block[i] + 1,
                 in_channels=input_channel,
                 out_channels=output_channel,
                 prev_output_channel=prev_output_channel,
@@ -358,13 +397,14 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
                 resnet_eps=norm_eps,
                 resnet_act_fn=act_fn,
                 resnet_groups=norm_num_groups,
-                cross_attention_dim=cross_attention_dim,
+                cross_attention_dim=reversed_cross_attention_dim[i],
                 num_attention_heads=reversed_num_attention_heads[i],
                 dual_cross_attention=False,
                 resolution_idx=i,
                 use_linear_projection=use_linear_projection,
                 temporal_num_attention_heads=motion_num_attention_heads,
                 temporal_max_seq_length=motion_max_seq_length,
+                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
             )
             self.up_blocks.append(up_block)
             prev_output_channel = output_channel
@@ -835,6 +875,28 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
         t_emb = t_emb.to(dtype=self.dtype)
 
         emb = self.time_embedding(t_emb, timestep_cond)
+        aug_emb = None
+
+        if self.config.addition_embed_type == "text_time":
+            if "text_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
+                )
+
+            text_embeds = added_cond_kwargs.get("text_embeds")
+            if "time_ids" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
+                )
+            time_ids = added_cond_kwargs.get("time_ids")
+            time_embeds = self.add_time_proj(time_ids.flatten())
+            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
+
+            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
+            add_embeds = add_embeds.to(emb.dtype)
+            aug_emb = self.add_embedding(add_embeds)
+
+        emb = emb if aug_emb is None else emb + aug_emb
         emb = emb.repeat_interleave(repeats=num_frames, dim=0)
         encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0)
 

src/diffusers/pipelines/__init__.py

@@ -114,6 +114,7 @@ else:
     _import_structure["amused"] = ["AmusedImg2ImgPipeline", "AmusedInpaintPipeline", "AmusedPipeline"]
     _import_structure["animatediff"] = [
         "AnimateDiffPipeline",
+        "AnimateDiffSDXLPipeline",
         "AnimateDiffVideoToVideoPipeline",
     ]
     _import_structure["audioldm"] = ["AudioLDMPipeline"]
@@ -367,7 +368,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from ..utils.dummy_torch_and_transformers_objects import *
     else:
         from .amused import AmusedImg2ImgPipeline, AmusedInpaintPipeline, AmusedPipeline
-        from .animatediff import AnimateDiffPipeline, AnimateDiffVideoToVideoPipeline
+        from .animatediff import AnimateDiffPipeline, AnimateDiffSDXLPipeline, AnimateDiffVideoToVideoPipeline
         from .audioldm import AudioLDMPipeline
         from .audioldm2 import (
             AudioLDM2Pipeline,

src/diffusers/pipelines/animatediff/__init__.py

@@ -22,6 +22,7 @@ except OptionalDependencyNotAvailable:
     _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
 else:
     _import_structure["pipeline_animatediff"] = ["AnimateDiffPipeline"]
+    _import_structure["pipeline_animatediff_sdxl"] = ["AnimateDiffSDXLPipeline"]
     _import_structure["pipeline_animatediff_video2video"] = ["AnimateDiffVideoToVideoPipeline"]
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
@@ -33,6 +34,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
 
     else:
         from .pipeline_animatediff import AnimateDiffPipeline
+        from .pipeline_animatediff_sdxl import AnimateDiffSDXLPipeline
         from .pipeline_animatediff_video2video import AnimateDiffVideoToVideoPipeline
         from .pipeline_output import AnimateDiffPipelineOutput
 

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -92,6 +92,21 @@ class AnimateDiffPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class AnimateDiffSDXLPipeline(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 AnimateDiffVideoToVideoPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]
 

tests/pipelines/animatediff/test_animatediff_sdxl.py

+import unittest
+
+import numpy as np
+import torch
+from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
+
+import diffusers
+from diffusers import (
+    AnimateDiffSDXLPipeline,
+    AutoencoderKL,
+    DDIMScheduler,
+    MotionAdapter,
+    UNet2DConditionModel,
+    UNetMotionModel,
+)
+from diffusers.utils import is_xformers_available, logging
+from diffusers.utils.testing_utils import torch_device
+
+from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS, TEXT_TO_IMAGE_PARAMS
+from ..test_pipelines_common import (
+    IPAdapterTesterMixin,
+    PipelineTesterMixin,
+    SDFunctionTesterMixin,
+    SDXLOptionalComponentsTesterMixin,
+)
+
+
+def to_np(tensor):
+    if isinstance(tensor, torch.Tensor):
+        tensor = tensor.detach().cpu().numpy()
+
+    return tensor
+
+
+class AnimateDiffPipelineSDXLFastTests(
+    IPAdapterTesterMixin,
+    SDFunctionTesterMixin,
+    PipelineTesterMixin,
+    SDXLOptionalComponentsTesterMixin,
+    unittest.TestCase,
+):
+    pipeline_class = AnimateDiffSDXLPipeline
+    params = TEXT_TO_IMAGE_PARAMS
+    batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
+    required_optional_params = frozenset(
+        [
+            "num_inference_steps",
+            "generator",
+            "latents",
+            "return_dict",
+            "callback_on_step_end",
+            "callback_on_step_end_tensor_inputs",
+        ]
+    )
+    callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union({"add_text_embeds", "add_time_ids"})
+
+    def get_dummy_components(self, time_cond_proj_dim=None):
+        torch.manual_seed(0)
+        unet = UNet2DConditionModel(
+            block_out_channels=(32, 64, 128),
+            layers_per_block=2,
+            time_cond_proj_dim=time_cond_proj_dim,
+            sample_size=32,
+            in_channels=4,
+            out_channels=4,
+            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),
+            up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"),
+            # SD2-specific config below
+            attention_head_dim=(2, 4, 8),
+            use_linear_projection=True,
+            addition_embed_type="text_time",
+            addition_time_embed_dim=8,
+            transformer_layers_per_block=(1, 2, 4),
+            projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
+            cross_attention_dim=64,
+            norm_num_groups=1,
+        )
+        scheduler = DDIMScheduler(
+            beta_start=0.00085,
+            beta_end=0.012,
+            beta_schedule="linear",
+            clip_sample=False,
+        )
+        torch.manual_seed(0)
+        vae = AutoencoderKL(
+            block_out_channels=[32, 64],
+            in_channels=3,
+            out_channels=3,
+            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
+            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
+            latent_channels=4,
+            sample_size=128,
+        )
+        torch.manual_seed(0)
+        text_encoder_config = CLIPTextConfig(
+            bos_token_id=0,
+            eos_token_id=2,
+            hidden_size=32,
+            intermediate_size=37,
+            layer_norm_eps=1e-05,
+            num_attention_heads=4,
+            num_hidden_layers=5,
+            pad_token_id=1,
+            vocab_size=1000,
+            # SD2-specific config below
+            hidden_act="gelu",
+            projection_dim=32,
+        )
+        text_encoder = CLIPTextModel(text_encoder_config)
+        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+        text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
+        tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
+        motion_adapter = MotionAdapter(
+            block_out_channels=(32, 64, 128),
+            motion_layers_per_block=2,
+            motion_norm_num_groups=2,
+            motion_num_attention_heads=4,
+            use_motion_mid_block=False,
+        )
+
+        components = {
+            "unet": unet,
+            "scheduler": scheduler,
+            "vae": vae,
+            "motion_adapter": motion_adapter,
+            "text_encoder": text_encoder,
+            "tokenizer": tokenizer,
+            "text_encoder_2": text_encoder_2,
+            "tokenizer_2": tokenizer_2,
+            "feature_extractor": None,
+            "image_encoder": None,
+        }
+        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)
+
+        inputs = {
+            "prompt": "A painting of a squirrel eating a burger",
+            "generator": generator,
+            "num_inference_steps": 2,
+            "guidance_scale": 7.5,
+            "output_type": "np",
+        }
+        return inputs
+
+    def test_motion_unet_loading(self):
+        components = self.get_dummy_components()
+        pipe = AnimateDiffSDXLPipeline(**components)
+
+        assert isinstance(pipe.unet, UNetMotionModel)
+
+    @unittest.skip("Attention slicing is not enabled in this pipeline")
+    def test_attention_slicing_forward_pass(self):
+        pass
+
+    def test_inference_batch_single_identical(
+        self,
+        batch_size=2,
+        expected_max_diff=1e-4,
+        additional_params_copy_to_batched_inputs=["num_inference_steps"],
+    ):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        for components in pipe.components.values():
+            if hasattr(components, "set_default_attn_processor"):
+                components.set_default_attn_processor()
+
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+        inputs = self.get_dummy_inputs(torch_device)
+        # Reset generator in case it is has been used in self.get_dummy_inputs
+        inputs["generator"] = self.get_generator(0)
+
+        logger = logging.get_logger(pipe.__module__)
+        logger.setLevel(level=diffusers.logging.FATAL)
+
+        # batchify inputs
+        batched_inputs = {}
+        batched_inputs.update(inputs)
+
+        for name in self.batch_params:
+            if name not in inputs:
+                continue
+
+            value = inputs[name]
+            if name == "prompt":
+                len_prompt = len(value)
+                batched_inputs[name] = [value[: len_prompt // i] for i in range(1, batch_size + 1)]
+                batched_inputs[name][-1] = 100 * "very long"
+
+            else:
+                batched_inputs[name] = batch_size * [value]
+
+        if "generator" in inputs:
+            batched_inputs["generator"] = [self.get_generator(i) for i in range(batch_size)]
+
+        if "batch_size" in inputs:
+            batched_inputs["batch_size"] = batch_size
+
+        for arg in additional_params_copy_to_batched_inputs:
+            batched_inputs[arg] = inputs[arg]
+
+        output = pipe(**inputs)
+        output_batch = pipe(**batched_inputs)
+
+        assert output_batch[0].shape[0] == batch_size
+
+        max_diff = np.abs(to_np(output_batch[0][0]) - to_np(output[0][0])).max()
+        assert max_diff < expected_max_diff
+
+    @unittest.skipIf(torch_device != "cuda", reason="CUDA and CPU are required to switch devices")
+    def test_to_device(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.set_progress_bar_config(disable=None)
+
+        pipe.to("cpu")
+        # pipeline creates a new motion UNet under the hood. So we need to check the device from pipe.components
+        model_devices = [
+            component.device.type for component in pipe.components.values() if hasattr(component, "device")
+        ]
+        self.assertTrue(all(device == "cpu" for device in model_devices))
+
+        output_cpu = pipe(**self.get_dummy_inputs("cpu"))[0]
+        self.assertTrue(np.isnan(output_cpu).sum() == 0)
+
+        pipe.to("cuda")
+        model_devices = [
+            component.device.type for component in pipe.components.values() if hasattr(component, "device")
+        ]
+        self.assertTrue(all(device == "cuda" for device in model_devices))
+
+        output_cuda = pipe(**self.get_dummy_inputs("cuda"))[0]
+        self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0)
+
+    def test_to_dtype(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.set_progress_bar_config(disable=None)
+
+        # pipeline creates a new motion UNet under the hood. So we need to check the dtype from pipe.components
+        model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, "dtype")]
+        self.assertTrue(all(dtype == torch.float32 for dtype in model_dtypes))
+
+        pipe.to(dtype=torch.float16)
+        model_dtypes = [component.dtype for component in pipe.components.values() if hasattr(component, "dtype")]
+        self.assertTrue(all(dtype == torch.float16 for dtype in model_dtypes))
+
+    def test_prompt_embeds(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        pipe.set_progress_bar_config(disable=None)
+        pipe.to(torch_device)
+
+        inputs = self.get_dummy_inputs(torch_device)
+        prompt = inputs.pop("prompt")
+
+        (
+            prompt_embeds,
+            negative_prompt_embeds,
+            pooled_prompt_embeds,
+            negative_pooled_prompt_embeds,
+        ) = pipe.encode_prompt(prompt)
+
+        pipe(
+            **inputs,
+            prompt_embeds=prompt_embeds,
+            negative_prompt_embeds=negative_prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
+        )
+
+    def test_save_load_optional_components(self):
+        self._test_save_load_optional_components()
+
+    @unittest.skipIf(
+        torch_device != "cuda" or not is_xformers_available(),
+        reason="XFormers attention is only available with CUDA and `xformers` installed",
+    )
+    def test_xformers_attention_forwardGenerator_pass(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        for component in pipe.components.values():
+            if hasattr(component, "set_default_attn_processor"):
+                component.set_default_attn_processor()
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+        output_without_offload = pipe(**inputs).frames[0]
+        output_without_offload = (
+            output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload
+        )
+
+        pipe.enable_xformers_memory_efficient_attention()
+        inputs = self.get_dummy_inputs(torch_device)
+        output_with_offload = pipe(**inputs).frames[0]
+        output_with_offload = (
+            output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload
+        )
+
+        max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()
+        self.assertLess(max_diff, 1e-4, "XFormers attention should not affect the inference results")