Add SVD (#5895)

* begin model

* finish blocks

* add_embedding

* addition_time_embed_dim

* use TimestepEmbedding

* fix temporal res block

* fix time_pos_embed

* fix add_embedding

* add conversion script

* fix model

* up

* add new resnet blocks

* make forward work

* return sample in original shape

* fix temb shape in TemporalResnetBlock

* add spatio temporal transformers

* add vae blocks

* fix blocks

* update

* update

* fix shapes in Alphablender and add time activation in res blcok

* use new blocks

* style

* fix temb shape

* fix SpatioTemporalResBlock

* reuse TemporalBasicTransformerBlock

* fix TemporalBasicTransformerBlock

* use TransformerSpatioTemporalModel

* fix TransformerSpatioTemporalModel

* fix time_context dim

* clean up

* make temb optional

* add blocks

* rename model

* update conversion script

* remove UNetMidBlockSpatioTemporal

* add in init

* remove unused arg

* remove unused arg
...

PHILOSOPHY.md

@@ -82,7 +82,7 @@ Models are designed as configurable toolboxes that are natural extensions of [Py
 The following design principles are followed:
 - Models correspond to **a type of model architecture**. *E.g.* the [`UNet2DConditionModel`] class is used for all UNet variations that expect 2D image inputs and are conditioned on some context.
 - All models can be found in [`src/diffusers/models`](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and every model architecture shall be defined in its file, e.g. [`unet_2d_condition.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/unet_2d_condition.py), [`transformer_2d.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/transformer_2d.py), etc...
-- Models **do not** follow the single-file policy and should make use of smaller model building blocks, such as [`attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py), [`resnet.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py), [`embeddings.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/embeddings.py), etc... **Note**: This is in stark contrast to Transformers' modelling files and shows that models do not really follow the single-file policy.
+- Models **do not** follow the single-file policy and should make use of smaller model building blocks, such as [`attention.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py), [`resnet.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py), [`embeddings.py`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/embeddings.py), etc... **Note**: This is in stark contrast to Transformers' modeling files and shows that models do not really follow the single-file policy.
 - Models intend to expose complexity, just like PyTorch's `Module` class, and give clear error messages.
 - Models all inherit from `ModelMixin` and `ConfigMixin`.
 - Models can be optimized for performance when it doesn’t demand major code changes, keep backward compatibility, and give significant memory or compute gain.

docs/source/en/_toctree.yml

@@ -94,6 +94,8 @@
       title: Latent Consistency Model-LoRA
     - local: using-diffusers/inference_with_lcm
       title: Latent Consistency Model
+    - local: using-diffusers/svd
+      title: Stable Video Diffusion
     title: Specific pipeline examples
   - sections:
     - local: training/overview

docs/source/en/using-diffusers/svd.md

+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Stable Video Diffusion
+
+[[open-in-colab]]
+
+[Stable Video Diffusion](https://static1.squarespace.com/static/6213c340453c3f502425776e/t/655ce779b9d47d342a93c890/1700587395994/stable_video_diffusion.pdf) is a powerful image-to-video generation model that can generate high resolution (576x1024) 2-4 second videos conditioned on the input image.
+
+This guide will show you how to use SVD to short generate videos from images.
+
+Before you begin, make sure you have the following libraries installed:
+
+```py
+!pip install -q -U diffusers transformers accelerate 
+```
+
+## Image to Video Generation
+
+The are two variants of SVD. [SVD](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid) 
+and [SVD-XT](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt). The svd checkpoint is trained to generate 14 frames and the svd-xt checkpoint is further 
+finetuned to generate 25 frames.
+
+We will use the `svd-xt` checkpoint for this guide.
+
+```python
+import torch
+
+from diffusers import StableVideoDiffusionPipeline
+from diffusers.utils import load_image, export_to_video
+
+pipe = StableVideoDiffusionPipeline.from_pretrained(
+    "stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16"
+)
+pipe.enable_model_cpu_offload()
+
+# Load the conditioning image
+image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png?download=true")
+image = image.resize((1024, 576))
+
+generator = torch.manual_seed(42)
+frames = pipe(image, decode_chunk_size=8, generator=generator).frames[0]
+
+export_to_video(frames, "generated.mp4", fps=7)
+```
+
+<video width="1024" height="576" controls>
+  <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket_generated.mp4?download=true" type="video/mp4">
+</video>
+
+<Tip>
+Since generating videos is more memory intensive we can use the `decode_chunk_size` argument to control how many frames are decoded at once. This will reduce the memory usage. It's recommended to tweak this value based on your GPU memory.
+Setting `decode_chunk_size=1` will decode one frame at a time and will use the least amount of memory but the video might have some flickering.
+
+Additionally, we also use [model cpu offloading](../../optimization/memory#model-offloading) to reduce the memory usage.
+</Tip>
+
+
+### Torch.compile
+
+You can achieve a 20-25% speed-up at the expense of slightly increased memory by compiling the UNet as follows:
+
+```diff
+- pipe.enable_model_cpu_offload()
++ pipe.to("cuda")
++ pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+```
+
+### Low-memory
+
+Video generation is very memory intensive as we have to essentially generate `num_frames` all at once. The mechanism is very comparable to text-to-image generation with a high batch size. To reduce the memory requirement you have multiple options. The following options trade inference speed against lower memory requirement:
+- enable model offloading: Each component of the pipeline is offloaded to CPU once it's not needed anymore.
+- enable feed-forward chunking: The feed-forward layer runs in a loop instead of running with a single huge feed-forward batch size
+- reduce `decode_chunk_size`: This means that the VAE decodes frames in chunks instead of decoding them all together. **Note**: In addition to leading to a small slowdown, this method also slightly leads to video quality deterioration
+
+ You can enable them as follows:
+ ```diff
+-pipe.enable_model_cpu_offload()
+-frames = pipe(image, decode_chunk_size=8, generator=generator).frames[0]
++pipe.enable_model_cpu_offload()
++pipe.unet.enable_forward_chunking()
++frames = pipe(image, decode_chunk_size=2, generator=generator, num_frames=25).frames[0]
+```
+
+
+Including all these tricks should lower the memory requirement to less than 8GB VRAM.
+
+### Micro-conditioning
+
+Along with conditioning image Stable Diffusion Video also allows providing micro-conditioning that allows more control over the generated video.
+It accepts the following arguments:
+
+- `fps`: The frames per second of the generated video.
+- `motion_bucket_id`: The motion bucket id to use for the generated video. This can be used to control the motion of the generated video. Increasing the motion bucket id will increase the motion of the generated video.
+- `noise_aug_strength`: The amount of noise added to the conditioning image. The higher the values the less the video will resemble the conditioning image. Increasing this value will also increase the motion of the generated video.
+
+Here is an example of using micro-conditioning to generate a video with more motion.
+
+```python
+import torch
+
+from diffusers import StableVideoDiffusionPipeline
+from diffusers.utils import load_image, export_to_video
+
+pipe = StableVideoDiffusionPipeline.from_pretrained(
+    "stabilityai/stable-video-diffusion-img2vid-xt", torch_dtype=torch.float16, variant="fp16"
+)
+pipe.enable_model_cpu_offload()
+
+# Load the conditioning image
+image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png?download=true")
+image = image.resize((1024, 576))
+
+generator = torch.manual_seed(42)
+frames = pipe(image, decode_chunk_size=8, generator=generator, motion_bucket_id=180, noise_aug_strength=0.1).frames[0]
+export_to_video(frames, "generated.mp4", fps=7)
+```
+
+<video width="1024" height="576" controls>
+  <source src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket_generated_motion.mp4?download=true" type="video/mp4">
+</video>
+

src/diffusers/__init__.py

@@ -76,6 +76,7 @@ else:
         [
             "AsymmetricAutoencoderKL",
             "AutoencoderKL",
+            "AutoencoderKLTemporalDecoder",
             "AutoencoderTiny",
             "ConsistencyDecoderVAE",
             "ControlNetModel",
@@ -92,6 +93,7 @@ else:
             "UNet2DModel",
             "UNet3DConditionModel",
             "UNetMotionModel",
+            "UNetSpatioTemporalConditionModel",
             "VQModel",
         ]
     )
@@ -277,6 +279,7 @@ else:
             "StableDiffusionXLPipeline",
             "StableUnCLIPImg2ImgPipeline",
             "StableUnCLIPPipeline",
+            "StableVideoDiffusionPipeline",
             "TextToVideoSDPipeline",
             "TextToVideoZeroPipeline",
             "TextToVideoZeroSDXLPipeline",
@@ -447,6 +450,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .models import (
             AsymmetricAutoencoderKL,
             AutoencoderKL,
+            AutoencoderKLTemporalDecoder,
             AutoencoderTiny,
             ConsistencyDecoderVAE,
             ControlNetModel,
@@ -463,6 +467,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             UNet2DModel,
             UNet3DConditionModel,
             UNetMotionModel,
+            UNetSpatioTemporalConditionModel,
             VQModel,
         )
         from .optimization import (
@@ -627,6 +632,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             StableDiffusionXLPipeline,
             StableUnCLIPImg2ImgPipeline,
             StableUnCLIPPipeline,
+            StableVideoDiffusionPipeline,
             TextToVideoSDPipeline,
             TextToVideoZeroPipeline,
             TextToVideoZeroSDXLPipeline,

src/diffusers/models/__init__.py

@@ -14,7 +14,12 @@
 
 from typing import TYPE_CHECKING
 
-from ..utils import DIFFUSERS_SLOW_IMPORT, _LazyModule, is_flax_available, is_torch_available
+from ..utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    _LazyModule,
+    is_flax_available,
+    is_torch_available,
+)
 
 
 _import_structure = {}
@@ -23,6 +28,7 @@ if is_torch_available():
     _import_structure["adapter"] = ["MultiAdapter", "T2IAdapter"]
     _import_structure["autoencoder_asym_kl"] = ["AsymmetricAutoencoderKL"]
     _import_structure["autoencoder_kl"] = ["AutoencoderKL"]
+    _import_structure["autoencoder_kl_temporal_decoder"] = ["AutoencoderKLTemporalDecoder"]
     _import_structure["autoencoder_tiny"] = ["AutoencoderTiny"]
     _import_structure["consistency_decoder_vae"] = ["ConsistencyDecoderVAE"]
     _import_structure["controlnet"] = ["ControlNetModel"]
@@ -38,6 +44,7 @@ if is_torch_available():
     _import_structure["unet_3d_condition"] = ["UNet3DConditionModel"]
     _import_structure["unet_kandi3"] = ["Kandinsky3UNet"]
     _import_structure["unet_motion_model"] = ["MotionAdapter", "UNetMotionModel"]
+    _import_structure["unet_spatio_temporal_condition"] = ["UNetSpatioTemporalConditionModel"]
     _import_structure["vq_model"] = ["VQModel"]
 
 if is_flax_available():
@@ -51,6 +58,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .adapter import MultiAdapter, T2IAdapter
         from .autoencoder_asym_kl import AsymmetricAutoencoderKL
         from .autoencoder_kl import AutoencoderKL
+        from .autoencoder_kl_temporal_decoder import AutoencoderKLTemporalDecoder
         from .autoencoder_tiny import AutoencoderTiny
         from .consistency_decoder_vae import ConsistencyDecoderVAE
         from .controlnet import ControlNetModel
@@ -66,6 +74,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .unet_3d_condition import UNet3DConditionModel
         from .unet_kandi3 import Kandinsky3UNet
         from .unet_motion_model import MotionAdapter, UNetMotionModel
+        from .unet_spatio_temporal_condition import UNetSpatioTemporalConditionModel
         from .vq_model import VQModel
 
     if is_flax_available():

src/diffusers/models/attention.py

@@ -25,6 +25,31 @@ from .lora import LoRACompatibleLinear
 from .normalization import AdaLayerNorm, AdaLayerNormZero
 
 
+def _chunked_feed_forward(
+    ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int, lora_scale: Optional[float] = None
+):
+    # "feed_forward_chunk_size" can be used to save memory
+    if hidden_states.shape[chunk_dim] % chunk_size != 0:
+        raise ValueError(
+            f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]} has to be divisible by chunk size: {chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
+        )
+
+    num_chunks = hidden_states.shape[chunk_dim] // chunk_size
+    if lora_scale is None:
+        ff_output = torch.cat(
+            [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
+            dim=chunk_dim,
+        )
+    else:
+        # TOOD(Patrick): LoRA scale can be removed once PEFT refactor is complete
+        ff_output = torch.cat(
+            [ff(hid_slice, scale=lora_scale) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)],
+            dim=chunk_dim,
+        )
+
+    return ff_output
+
+
 @maybe_allow_in_graph
 class GatedSelfAttentionDense(nn.Module):
     r"""
@@ -194,7 +219,12 @@ class BasicTransformerBlock(nn.Module):
         if not self.use_ada_layer_norm_single:
             self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine, eps=norm_eps)
 
-        self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout)
+        self.ff = FeedForward(
+            dim,
+            dropout=dropout,
+            activation_fn=activation_fn,
+            final_dropout=final_dropout,
+        )
 
         # 4. Fuser
         if attention_type == "gated" or attention_type == "gated-text-image":
@@ -208,7 +238,7 @@ class BasicTransformerBlock(nn.Module):
         self._chunk_size = None
         self._chunk_dim = 0
 
-    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):
+    def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
         # Sets chunk feed-forward
         self._chunk_size = chunk_size
         self._chunk_dim = dim
@@ -311,18 +341,8 @@ class BasicTransformerBlock(nn.Module):
 
         if self._chunk_size is not None:
             # "feed_forward_chunk_size" can be used to save memory
-            if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
-                raise ValueError(
-                    f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
-                )
-
-            num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
-            ff_output = torch.cat(
-                [
-                    self.ff(hid_slice, scale=lora_scale)
-                    for hid_slice in norm_hidden_states.chunk(num_chunks, dim=self._chunk_dim)
-                ],
-                dim=self._chunk_dim,
+            ff_output = _chunked_feed_forward(
+                self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size, lora_scale=lora_scale
             )
         else:
             ff_output = self.ff(norm_hidden_states, scale=lora_scale)
@@ -339,6 +359,137 @@ class BasicTransformerBlock(nn.Module):
         return hidden_states
 
 
+@maybe_allow_in_graph
+class TemporalBasicTransformerBlock(nn.Module):
+    r"""
+    A basic Transformer block for video like data.
+
+    Parameters:
+        dim (`int`): The number of channels in the input and output.
+        time_mix_inner_dim (`int`): The number of channels for temporal attention.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        time_mix_inner_dim: int,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        cross_attention_dim: Optional[int] = None,
+    ):
+        super().__init__()
+        self.is_res = dim == time_mix_inner_dim
+
+        self.norm_in = nn.LayerNorm(dim)
+
+        # Define 3 blocks. Each block has its own normalization layer.
+        # 1. Self-Attn
+        self.norm_in = nn.LayerNorm(dim)
+        self.ff_in = FeedForward(
+            dim,
+            dim_out=time_mix_inner_dim,
+            activation_fn="geglu",
+        )
+
+        self.norm1 = nn.LayerNorm(time_mix_inner_dim)
+        self.attn1 = Attention(
+            query_dim=time_mix_inner_dim,
+            heads=num_attention_heads,
+            dim_head=attention_head_dim,
+            cross_attention_dim=None,
+        )
+
+        # 2. Cross-Attn
+        if cross_attention_dim is not None:
+            # We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
+            # I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
+            # the second cross attention block.
+            self.norm2 = nn.LayerNorm(time_mix_inner_dim)
+            self.attn2 = Attention(
+                query_dim=time_mix_inner_dim,
+                cross_attention_dim=cross_attention_dim,
+                heads=num_attention_heads,
+                dim_head=attention_head_dim,
+            )  # is self-attn if encoder_hidden_states is none
+        else:
+            self.norm2 = None
+            self.attn2 = None
+
+        # 3. Feed-forward
+        self.norm3 = nn.LayerNorm(time_mix_inner_dim)
+        self.ff = FeedForward(time_mix_inner_dim, activation_fn="geglu")
+
+        # let chunk size default to None
+        self._chunk_size = None
+        self._chunk_dim = None
+
+    def set_chunk_feed_forward(self, chunk_size: Optional[int], **kwargs):
+        # Sets chunk feed-forward
+        self._chunk_size = chunk_size
+        # chunk dim should be hardcoded to 1 to have better speed vs. memory trade-off
+        self._chunk_dim = 1
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        num_frames: int,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+    ) -> torch.FloatTensor:
+        # Notice that normalization is always applied before the real computation in the following blocks.
+        # 0. Self-Attention
+        batch_size = hidden_states.shape[0]
+
+        batch_frames, seq_length, channels = hidden_states.shape
+        batch_size = batch_frames // num_frames
+
+        hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, seq_length, channels)
+        hidden_states = hidden_states.permute(0, 2, 1, 3)
+        hidden_states = hidden_states.reshape(batch_size * seq_length, num_frames, channels)
+
+        residual = hidden_states
+        hidden_states = self.norm_in(hidden_states)
+
+        if self._chunk_size is not None:
+            hidden_states = _chunked_feed_forward(self.ff, hidden_states, self._chunk_dim, self._chunk_size)
+        else:
+            hidden_states = self.ff_in(hidden_states)
+
+        if self.is_res:
+            hidden_states = hidden_states + residual
+
+        norm_hidden_states = self.norm1(hidden_states)
+        attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None)
+        hidden_states = attn_output + hidden_states
+
+        # 3. Cross-Attention
+        if self.attn2 is not None:
+            norm_hidden_states = self.norm2(hidden_states)
+            attn_output = self.attn2(norm_hidden_states, encoder_hidden_states=encoder_hidden_states)
+            hidden_states = attn_output + hidden_states
+
+        # 4. Feed-forward
+        norm_hidden_states = self.norm3(hidden_states)
+
+        if self._chunk_size is not None:
+            ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size)
+        else:
+            ff_output = self.ff(norm_hidden_states)
+
+        if self.is_res:
+            hidden_states = ff_output + hidden_states
+        else:
+            hidden_states = ff_output
+
+        hidden_states = hidden_states[None, :].reshape(batch_size, seq_length, num_frames, channels)
+        hidden_states = hidden_states.permute(0, 2, 1, 3)
+        hidden_states = hidden_states.reshape(batch_size * num_frames, seq_length, channels)
+
+        return hidden_states
+
+
 class FeedForward(nn.Module):
     r"""
     A feed-forward layer.

src/diffusers/models/autoencoder_asym_kl.py

@@ -18,7 +18,7 @@ import torch.nn as nn
 
 from ..configuration_utils import ConfigMixin, register_to_config
 from ..utils.accelerate_utils import apply_forward_hook
-from .autoencoder_kl import AutoencoderKLOutput
+from .modeling_outputs import AutoencoderKLOutput
 from .modeling_utils import ModelMixin
 from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder, MaskConditionDecoder
 

src/diffusers/models/autoencoder_kl.py

@@ -11,7 +11,6 @@
 # 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.
-from dataclasses import dataclass
 from typing import Dict, Optional, Tuple, Union
 
 import torch
@@ -19,7 +18,6 @@ import torch.nn as nn
 
 from ..configuration_utils import ConfigMixin, register_to_config
 from ..loaders import FromOriginalVAEMixin
-from ..utils import BaseOutput
 from ..utils.accelerate_utils import apply_forward_hook
 from .attention_processor import (
     ADDED_KV_ATTENTION_PROCESSORS,
@@ -28,24 +26,11 @@ from .attention_processor import (
     AttnAddedKVProcessor,
     AttnProcessor,
 )
+from .modeling_outputs import AutoencoderKLOutput
 from .modeling_utils import ModelMixin
 from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
 
 
-@dataclass
-class AutoencoderKLOutput(BaseOutput):
-    """
-    Output of AutoencoderKL encoding method.
-
-    Args:
-        latent_dist (`DiagonalGaussianDistribution`):
-            Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
-            `DiagonalGaussianDistribution` allows for sampling latents from the distribution.
-    """
-
-    latent_dist: "DiagonalGaussianDistribution"
-
-
 class AutoencoderKL(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
     r"""
     A VAE model with KL loss for encoding images into latents and decoding latent representations into images.

src/diffusers/models/autoencoder_kl_temporal_decoder.py

+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Dict, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+
+from ..configuration_utils import ConfigMixin, register_to_config
+from ..loaders import FromOriginalVAEMixin
+from ..utils import is_torch_version
+from ..utils.accelerate_utils import apply_forward_hook
+from .attention_processor import CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnProcessor
+from .modeling_outputs import AutoencoderKLOutput
+from .modeling_utils import ModelMixin
+from .unet_3d_blocks import MidBlockTemporalDecoder, UpBlockTemporalDecoder
+from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder
+
+
+class TemporalDecoder(nn.Module):
+    def __init__(
+        self,
+        in_channels: int = 4,
+        out_channels: int = 3,
+        block_out_channels: Tuple[int] = (128, 256, 512, 512),
+        layers_per_block: int = 2,
+    ):
+        super().__init__()
+        self.layers_per_block = layers_per_block
+
+        self.conv_in = nn.Conv2d(in_channels, block_out_channels[-1], kernel_size=3, stride=1, padding=1)
+        self.mid_block = MidBlockTemporalDecoder(
+            num_layers=self.layers_per_block,
+            in_channels=block_out_channels[-1],
+            out_channels=block_out_channels[-1],
+            attention_head_dim=block_out_channels[-1],
+        )
+
+        # up
+        self.up_blocks = nn.ModuleList([])
+        reversed_block_out_channels = list(reversed(block_out_channels))
+        output_channel = reversed_block_out_channels[0]
+        for i in range(len(block_out_channels)):
+            prev_output_channel = output_channel
+            output_channel = reversed_block_out_channels[i]
+
+            is_final_block = i == len(block_out_channels) - 1
+            up_block = UpBlockTemporalDecoder(
+                num_layers=self.layers_per_block + 1,
+                in_channels=prev_output_channel,
+                out_channels=output_channel,
+                add_upsample=not is_final_block,
+            )
+            self.up_blocks.append(up_block)
+            prev_output_channel = output_channel
+
+        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=32, eps=1e-6)
+
+        self.conv_act = nn.SiLU()
+        self.conv_out = torch.nn.Conv2d(
+            in_channels=block_out_channels[0],
+            out_channels=out_channels,
+            kernel_size=3,
+            padding=1,
+        )
+
+        conv_out_kernel_size = (3, 1, 1)
+        padding = [int(k // 2) for k in conv_out_kernel_size]
+        self.time_conv_out = torch.nn.Conv3d(
+            in_channels=out_channels,
+            out_channels=out_channels,
+            kernel_size=conv_out_kernel_size,
+            padding=padding,
+        )
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        image_only_indicator: torch.FloatTensor,
+        num_frames: int = 1,
+    ) -> torch.FloatTensor:
+        r"""The forward method of the `Decoder` class."""
+
+        sample = self.conv_in(sample)
+
+        upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
+        if self.training and self.gradient_checkpointing:
+
+            def create_custom_forward(module):
+                def custom_forward(*inputs):
+                    return module(*inputs)
+
+                return custom_forward
+
+            if is_torch_version(">=", "1.11.0"):
+                # middle
+                sample = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(self.mid_block),
+                    sample,
+                    image_only_indicator,
+                    use_reentrant=False,
+                )
+                sample = sample.to(upscale_dtype)
+
+                # up
+                for up_block in self.up_blocks:
+                    sample = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(up_block),
+                        sample,
+                        image_only_indicator,
+                        use_reentrant=False,
+                    )
+            else:
+                # middle
+                sample = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(self.mid_block),
+                    sample,
+                    image_only_indicator,
+                )
+                sample = sample.to(upscale_dtype)
+
+                # up
+                for up_block in self.up_blocks:
+                    sample = torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(up_block),
+                        sample,
+                        image_only_indicator,
+                    )
+        else:
+            # middle
+            sample = self.mid_block(sample, image_only_indicator=image_only_indicator)
+            sample = sample.to(upscale_dtype)
+
+            # up
+            for up_block in self.up_blocks:
+                sample = up_block(sample, image_only_indicator=image_only_indicator)
+
+        # post-process
+        sample = self.conv_norm_out(sample)
+        sample = self.conv_act(sample)
+        sample = self.conv_out(sample)
+
+        batch_frames, channels, height, width = sample.shape
+        batch_size = batch_frames // num_frames
+        sample = sample[None, :].reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)
+        sample = self.time_conv_out(sample)
+
+        sample = sample.permute(0, 2, 1, 3, 4).reshape(batch_frames, channels, height, width)
+
+        return sample
+
+
+class AutoencoderKLTemporalDecoder(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
+    r"""
+    A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+    for all models (such as downloading or saving).
+
+    Parameters:
+        in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
+        out_channels (int,  *optional*, defaults to 3): Number of channels in the output.
+        down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
+            Tuple of downsample block types.
+        block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
+            Tuple of block output channels.
+        layers_per_block: (`int`, *optional*, defaults to 1): Number of layers per block.
+        latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
+        sample_size (`int`, *optional*, defaults to `32`): Sample input size.
+        scaling_factor (`float`, *optional*, defaults to 0.18215):
+            The component-wise standard deviation of the trained latent space computed using the first batch of the
+            training set. This is used to scale the latent space to have unit variance when training the diffusion
+            model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
+            diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
+            / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
+            Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
+        force_upcast (`bool`, *optional*, default to `True`):
+            If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
+            can be fine-tuned / trained to a lower range without loosing too much precision in which case
+            `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        down_block_types: Tuple[str] = ("DownEncoderBlock2D",),
+        block_out_channels: Tuple[int] = (64,),
+        layers_per_block: int = 1,
+        latent_channels: int = 4,
+        sample_size: int = 32,
+        scaling_factor: float = 0.18215,
+        force_upcast: float = True,
+    ):
+        super().__init__()
+
+        # pass init params to Encoder
+        self.encoder = Encoder(
+            in_channels=in_channels,
+            out_channels=latent_channels,
+            down_block_types=down_block_types,
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+            double_z=True,
+        )
+
+        # pass init params to Decoder
+        self.decoder = TemporalDecoder(
+            in_channels=latent_channels,
+            out_channels=out_channels,
+            block_out_channels=block_out_channels,
+            layers_per_block=layers_per_block,
+        )
+
+        self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
+
+        sample_size = (
+            self.config.sample_size[0]
+            if isinstance(self.config.sample_size, (list, tuple))
+            else self.config.sample_size
+        )
+        self.tile_latent_min_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))
+        self.tile_overlap_factor = 0.25
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, (Encoder, TemporalDecoder)):
+            module.gradient_checkpointing = value
+
+    @property
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True)
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(
+        self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False
+    ):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor, _remove_lora=_remove_lora)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora)
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def set_default_attn_processor(self):
+        """
+        Disables custom attention processors and sets the default attention implementation.
+        """
+        if all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
+            processor = AttnProcessor()
+        else:
+            raise ValueError(
+                f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}"
+            )
+
+        self.set_attn_processor(processor, _remove_lora=True)
+
+    @apply_forward_hook
+    def encode(
+        self, x: torch.FloatTensor, return_dict: bool = True
+    ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
+        """
+        Encode a batch of images into latents.
+
+        Args:
+            x (`torch.FloatTensor`): Input batch of images.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.
+
+        Returns:
+                The latent representations of the encoded images. If `return_dict` is True, a
+                [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
+        """
+        h = self.encoder(x)
+        moments = self.quant_conv(h)
+        posterior = DiagonalGaussianDistribution(moments)
+
+        if not return_dict:
+            return (posterior,)
+
+        return AutoencoderKLOutput(latent_dist=posterior)
+
+    @apply_forward_hook
+    def decode(
+        self,
+        z: torch.FloatTensor,
+        num_frames: int,
+        return_dict: bool = True,
+    ) -> Union[DecoderOutput, torch.FloatTensor]:
+        """
+        Decode a batch of images.
+
+        Args:
+            z (`torch.FloatTensor`): Input batch of latent vectors.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~models.vae.DecoderOutput`] or `tuple`:
+                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
+                returned.
+
+        """
+        batch_size = z.shape[0] // num_frames
+        image_only_indicator = torch.zeros(batch_size, num_frames, dtype=z.dtype, device=z.device)
+        decoded = self.decoder(z, num_frames=num_frames, image_only_indicator=image_only_indicator)
+
+        if not return_dict:
+            return (decoded,)
+
+        return DecoderOutput(sample=decoded)
+
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        sample_posterior: bool = False,
+        return_dict: bool = True,
+        generator: Optional[torch.Generator] = None,
+        num_frames: int = 1,
+    ) -> Union[DecoderOutput, torch.FloatTensor]:
+        r"""
+        Args:
+            sample (`torch.FloatTensor`): Input sample.
+            sample_posterior (`bool`, *optional*, defaults to `False`):
+                Whether to sample from the posterior.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
+        """
+        x = sample
+        posterior = self.encode(x).latent_dist
+        if sample_posterior:
+            z = posterior.sample(generator=generator)
+        else:
+            z = posterior.mode()
+
+        dec = self.decode(z, num_frames=num_frames).sample
+
+        if not return_dict:
+            return (dec,)
+
+        return DecoderOutput(sample=dec)

src/diffusers/models/modeling_outputs.py

+from dataclasses import dataclass
+
+from ..utils import BaseOutput
+
+
+@dataclass
+class AutoencoderKLOutput(BaseOutput):
+    """
+    Output of AutoencoderKL encoding method.
+
+    Args:
+        latent_dist (`DiagonalGaussianDistribution`):
+            Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
+            `DiagonalGaussianDistribution` allows for sampling latents from the distribution.
+    """
+
+    latent_dist: "DiagonalGaussianDistribution"  # noqa: F821

src/diffusers/models/resnet.py

@@ -165,7 +165,10 @@ class Upsample2D(nn.Module):
             self.Conv2d_0 = conv
 
     def forward(
-        self, hidden_states: torch.FloatTensor, output_size: Optional[int] = None, scale: float = 1.0
+        self,
+        hidden_states: torch.FloatTensor,
+        output_size: Optional[int] = None,
+        scale: float = 1.0,
     ) -> torch.FloatTensor:
         assert hidden_states.shape[1] == self.channels
 
@@ -379,7 +382,11 @@ class FirUpsample2D(nn.Module):
             weight = torch.reshape(weight, (num_groups * inC, -1, convH, convW))
 
             inverse_conv = F.conv_transpose2d(
-                hidden_states, weight, stride=stride, output_padding=output_padding, padding=0
+                hidden_states,
+                weight,
+                stride=stride,
+                output_padding=output_padding,
+                padding=0,
             )
 
             output = upfirdn2d_native(
@@ -530,7 +537,14 @@ class KDownsample2D(nn.Module):
 
     def forward(self, inputs: torch.Tensor) -> torch.Tensor:
         inputs = F.pad(inputs, (self.pad,) * 4, self.pad_mode)
-        weight = inputs.new_zeros([inputs.shape[1], inputs.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
+        weight = inputs.new_zeros(
+            [
+                inputs.shape[1],
+                inputs.shape[1],
+                self.kernel.shape[0],
+                self.kernel.shape[1],
+            ]
+        )
         indices = torch.arange(inputs.shape[1], device=inputs.device)
         kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)
         weight[indices, indices] = kernel
@@ -553,7 +567,14 @@ class KUpsample2D(nn.Module):
 
     def forward(self, inputs: torch.Tensor) -> torch.Tensor:
         inputs = F.pad(inputs, ((self.pad + 1) // 2,) * 4, self.pad_mode)
-        weight = inputs.new_zeros([inputs.shape[1], inputs.shape[1], self.kernel.shape[0], self.kernel.shape[1]])
+        weight = inputs.new_zeros(
+            [
+                inputs.shape[1],
+                inputs.shape[1],
+                self.kernel.shape[0],
+                self.kernel.shape[1],
+            ]
+        )
         indices = torch.arange(inputs.shape[1], device=inputs.device)
         kernel = self.kernel.to(weight)[None, :].expand(inputs.shape[1], -1, -1)
         weight[indices, indices] = kernel
@@ -690,11 +711,19 @@ class ResnetBlock2D(nn.Module):
         self.conv_shortcut = None
         if self.use_in_shortcut:
             self.conv_shortcut = conv_cls(
-                in_channels, conv_2d_out_channels, kernel_size=1, stride=1, padding=0, bias=conv_shortcut_bias
+                in_channels,
+                conv_2d_out_channels,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias=conv_shortcut_bias,
             )
 
     def forward(
-        self, input_tensor: torch.FloatTensor, temb: torch.FloatTensor, scale: float = 1.0
+        self,
+        input_tensor: torch.FloatTensor,
+        temb: torch.FloatTensor,
+        scale: float = 1.0,
     ) -> torch.FloatTensor:
         hidden_states = input_tensor
 
@@ -866,7 +895,10 @@ class ResidualTemporalBlock1D(nn.Module):
 
 
 def upsample_2d(
-    hidden_states: torch.FloatTensor, kernel: Optional[torch.FloatTensor] = None, factor: int = 2, gain: float = 1
+    hidden_states: torch.FloatTensor,
+    kernel: Optional[torch.FloatTensor] = None,
+    factor: int = 2,
+    gain: float = 1,
 ) -> torch.FloatTensor:
     r"""Upsample2D a batch of 2D images with the given filter.
     Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and upsamples each image with the given
@@ -910,7 +942,10 @@ def upsample_2d(
 
 
 def downsample_2d(
-    hidden_states: torch.FloatTensor, kernel: Optional[torch.FloatTensor] = None, factor: int = 2, gain: float = 1
+    hidden_states: torch.FloatTensor,
+    kernel: Optional[torch.FloatTensor] = None,
+    factor: int = 2,
+    gain: float = 1,
 ) -> torch.FloatTensor:
     r"""Downsample2D a batch of 2D images with the given filter.
     Accepts a batch of 2D images of the shape `[N, C, H, W]` or `[N, H, W, C]` and downsamples each image with the
@@ -946,13 +981,20 @@ def downsample_2d(
     kernel = kernel * gain
     pad_value = kernel.shape[0] - factor
     output = upfirdn2d_native(
-        hidden_states, kernel.to(device=hidden_states.device), down=factor, pad=((pad_value + 1) // 2, pad_value // 2)
+        hidden_states,
+        kernel.to(device=hidden_states.device),
+        down=factor,
+        pad=((pad_value + 1) // 2, pad_value // 2),
     )
     return output
 
 
 def upfirdn2d_native(
-    tensor: torch.Tensor, kernel: torch.Tensor, up: int = 1, down: int = 1, pad: Tuple[int, int] = (0, 0)
+    tensor: torch.Tensor,
+    kernel: torch.Tensor,
+    up: int = 1,
+    down: int = 1,
+    pad: Tuple[int, int] = (0, 0),
 ) -> torch.Tensor:
     up_x = up_y = up
     down_x = down_y = down
@@ -1008,7 +1050,13 @@ class TemporalConvLayer(nn.Module):
         dropout (`float`, *optional*, defaults to `0.0`): The dropout probability to use.
     """
 
-    def __init__(self, in_dim: int, out_dim: Optional[int] = None, dropout: float = 0.0, norm_num_groups: int = 32):
+    def __init__(
+        self,
+        in_dim: int,
+        out_dim: Optional[int] = None,
+        dropout: float = 0.0,
+        norm_num_groups: int = 32,
+    ):
         super().__init__()
         out_dim = out_dim or in_dim
         self.in_dim = in_dim
@@ -1016,7 +1064,9 @@ class TemporalConvLayer(nn.Module):
 
         # conv layers
         self.conv1 = nn.Sequential(
-            nn.GroupNorm(norm_num_groups, in_dim), nn.SiLU(), nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding=(1, 0, 0))
+            nn.GroupNorm(norm_num_groups, in_dim),
+            nn.SiLU(),
+            nn.Conv3d(in_dim, out_dim, (3, 1, 1), padding=(1, 0, 0)),
         )
         self.conv2 = nn.Sequential(
             nn.GroupNorm(norm_num_groups, out_dim),
@@ -1058,3 +1108,261 @@ class TemporalConvLayer(nn.Module):
             (hidden_states.shape[0] * hidden_states.shape[2], -1) + hidden_states.shape[3:]
         )
         return hidden_states
+
+
+class TemporalResnetBlock(nn.Module):
+    r"""
+    A Resnet block.
+
+    Parameters:
+        in_channels (`int`): The number of channels in the input.
+        out_channels (`int`, *optional*, default to be `None`):
+            The number of output channels for the first conv2d layer. If None, same as `in_channels`.
+        temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
+        eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the normalization.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: Optional[int] = None,
+        temb_channels: int = 512,
+        eps: float = 1e-6,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+
+        kernel_size = (3, 1, 1)
+        padding = [k // 2 for k in kernel_size]
+
+        self.norm1 = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=eps, affine=True)
+        self.conv1 = nn.Conv3d(
+            in_channels,
+            out_channels,
+            kernel_size=kernel_size,
+            stride=1,
+            padding=padding,
+        )
+
+        if temb_channels is not None:
+            self.time_emb_proj = nn.Linear(temb_channels, out_channels)
+        else:
+            self.time_emb_proj = None
+
+        self.norm2 = torch.nn.GroupNorm(num_groups=32, num_channels=out_channels, eps=eps, affine=True)
+
+        self.dropout = torch.nn.Dropout(0.0)
+        self.conv2 = nn.Conv3d(
+            out_channels,
+            out_channels,
+            kernel_size=kernel_size,
+            stride=1,
+            padding=padding,
+        )
+
+        self.nonlinearity = get_activation("silu")
+
+        self.use_in_shortcut = self.in_channels != out_channels
+
+        self.conv_shortcut = None
+        if self.use_in_shortcut:
+            self.conv_shortcut = nn.Conv3d(
+                in_channels,
+                out_channels,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            )
+
+    def forward(self, input_tensor: torch.FloatTensor, temb: torch.FloatTensor) -> torch.FloatTensor:
+        hidden_states = input_tensor
+
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.conv1(hidden_states)
+
+        if self.time_emb_proj is not None:
+            temb = self.nonlinearity(temb)
+            temb = self.time_emb_proj(temb)[:, :, :, None, None]
+            temb = temb.permute(0, 2, 1, 3, 4)
+            hidden_states = hidden_states + temb
+
+        hidden_states = self.norm2(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+
+        if self.conv_shortcut is not None:
+            input_tensor = self.conv_shortcut(input_tensor)
+
+        output_tensor = input_tensor + hidden_states
+
+        return output_tensor
+
+
+# VideoResBlock
+class SpatioTemporalResBlock(nn.Module):
+    r"""
+    A SpatioTemporal Resnet block.
+
+    Parameters:
+        in_channels (`int`): The number of channels in the input.
+        out_channels (`int`, *optional*, default to be `None`):
+            The number of output channels for the first conv2d layer. If None, same as `in_channels`.
+        temb_channels (`int`, *optional*, default to `512`): the number of channels in timestep embedding.
+        eps (`float`, *optional*, defaults to `1e-6`): The epsilon to use for the spatial resenet.
+        temporal_eps (`float`, *optional*, defaults to `eps`): The epsilon to use for the temporal resnet.
+        merge_factor (`float`, *optional*, defaults to `0.5`): The merge factor to use for the temporal mixing.
+        merge_strategy (`str`, *optional*, defaults to `learned_with_images`):
+            The merge strategy to use for the temporal mixing.
+        switch_spatial_to_temporal_mix (`bool`, *optional*, defaults to `False`):
+            If `True`, switch the spatial and temporal mixing.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: Optional[int] = None,
+        temb_channels: int = 512,
+        eps: float = 1e-6,
+        temporal_eps: Optional[float] = None,
+        merge_factor: float = 0.5,
+        merge_strategy="learned_with_images",
+        switch_spatial_to_temporal_mix: bool = False,
+    ):
+        super().__init__()
+
+        self.spatial_res_block = ResnetBlock2D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            temb_channels=temb_channels,
+            eps=eps,
+        )
+
+        self.temporal_res_block = TemporalResnetBlock(
+            in_channels=out_channels if out_channels is not None else in_channels,
+            out_channels=out_channels if out_channels is not None else in_channels,
+            temb_channels=temb_channels,
+            eps=temporal_eps if temporal_eps is not None else eps,
+        )
+
+        self.time_mixer = AlphaBlender(
+            alpha=merge_factor,
+            merge_strategy=merge_strategy,
+            switch_spatial_to_temporal_mix=switch_spatial_to_temporal_mix,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        temb: Optional[torch.FloatTensor] = None,
+        image_only_indicator: Optional[torch.Tensor] = None,
+    ):
+        num_frames = image_only_indicator.shape[-1]
+        hidden_states = self.spatial_res_block(hidden_states, temb)
+
+        batch_frames, channels, height, width = hidden_states.shape
+        batch_size = batch_frames // num_frames
+
+        hidden_states_mix = (
+            hidden_states[None, :].reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)
+        )
+        hidden_states = (
+            hidden_states[None, :].reshape(batch_size, num_frames, channels, height, width).permute(0, 2, 1, 3, 4)
+        )
+
+        if temb is not None:
+            temb = temb.reshape(batch_size, num_frames, -1)
+
+        hidden_states = self.temporal_res_block(hidden_states, temb)
+        hidden_states = self.time_mixer(
+            x_spatial=hidden_states_mix,
+            x_temporal=hidden_states,
+            image_only_indicator=image_only_indicator,
+        )
+
+        hidden_states = hidden_states.permute(0, 2, 1, 3, 4).reshape(batch_frames, channels, height, width)
+        return hidden_states
+
+
+class AlphaBlender(nn.Module):
+    r"""
+    A module to blend spatial and temporal features.
+
+    Parameters:
+        alpha (`float`): The initial value of the blending factor.
+        merge_strategy (`str`, *optional*, defaults to `learned_with_images`):
+            The merge strategy to use for the temporal mixing.
+        switch_spatial_to_temporal_mix (`bool`, *optional*, defaults to `False`):
+            If `True`, switch the spatial and temporal mixing.
+    """
+
+    strategies = ["learned", "fixed", "learned_with_images"]
+
+    def __init__(
+        self,
+        alpha: float,
+        merge_strategy: str = "learned_with_images",
+        switch_spatial_to_temporal_mix: bool = False,
+    ):
+        super().__init__()
+        self.merge_strategy = merge_strategy
+        self.switch_spatial_to_temporal_mix = switch_spatial_to_temporal_mix  # For TemporalVAE
+
+        if merge_strategy not in self.strategies:
+            raise ValueError(f"merge_strategy needs to be in {self.strategies}")
+
+        if self.merge_strategy == "fixed":
+            self.register_buffer("mix_factor", torch.Tensor([alpha]))
+        elif self.merge_strategy == "learned" or self.merge_strategy == "learned_with_images":
+            self.register_parameter("mix_factor", torch.nn.Parameter(torch.Tensor([alpha])))
+        else:
+            raise ValueError(f"Unknown merge strategy {self.merge_strategy}")
+
+    def get_alpha(self, image_only_indicator: torch.Tensor, ndims: int) -> torch.Tensor:
+        if self.merge_strategy == "fixed":
+            alpha = self.mix_factor
+
+        elif self.merge_strategy == "learned":
+            alpha = torch.sigmoid(self.mix_factor)
+
+        elif self.merge_strategy == "learned_with_images":
+            if image_only_indicator is None:
+                raise ValueError("Please provide image_only_indicator to use learned_with_images merge strategy")
+
+            alpha = torch.where(
+                image_only_indicator.bool(),
+                torch.ones(1, 1, device=image_only_indicator.device),
+                torch.sigmoid(self.mix_factor)[..., None],
+            )
+
+            # (batch, channel, frames, height, width)
+            if ndims == 5:
+                alpha = alpha[:, None, :, None, None]
+            # (batch*frames, height*width, channels)
+            elif ndims == 3:
+                alpha = alpha.reshape(-1)[:, None, None]
+            else:
+                raise ValueError(f"Unexpected ndims {ndims}. Dimensions should be 3 or 5")
+
+        else:
+            raise NotImplementedError
+
+        return alpha
+
+    def forward(
+        self,
+        x_spatial: torch.Tensor,
+        x_temporal: torch.Tensor,
+        image_only_indicator: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        alpha = self.get_alpha(image_only_indicator, x_spatial.ndim)
+        alpha = alpha.to(x_spatial.dtype)
+
+        if self.switch_spatial_to_temporal_mix:
+            alpha = 1.0 - alpha
+
+        x = alpha * x_spatial + (1.0 - alpha) * x_temporal
+        return x

src/diffusers/models/transformer_temporal.py

@@ -19,8 +19,10 @@ from torch import nn
 
 from ..configuration_utils import ConfigMixin, register_to_config
 from ..utils import BaseOutput
-from .attention import BasicTransformerBlock
+from .attention import BasicTransformerBlock, TemporalBasicTransformerBlock
+from .embeddings import TimestepEmbedding, Timesteps
 from .modeling_utils import ModelMixin
+from .resnet import AlphaBlender
 
 
 @dataclass
@@ -195,3 +197,183 @@ class TransformerTemporalModel(ModelMixin, ConfigMixin):
             return (output,)
 
         return TransformerTemporalModelOutput(sample=output)
+
+
+class TransformerSpatioTemporalModel(nn.Module):
+    """
+    A Transformer model for video-like data.
+
+    Parameters:
+        num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
+        in_channels (`int`, *optional*):
+            The number of channels in the input and output (specify if the input is **continuous**).
+        out_channels (`int`, *optional*):
+            The number of channels in the output (specify if the input is **continuous**).
+        num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
+        cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
+    """
+
+    def __init__(
+        self,
+        num_attention_heads: int = 16,
+        attention_head_dim: int = 88,
+        in_channels: int = 320,
+        out_channels: Optional[int] = None,
+        num_layers: int = 1,
+        cross_attention_dim: Optional[int] = None,
+    ):
+        super().__init__()
+        self.num_attention_heads = num_attention_heads
+        self.attention_head_dim = attention_head_dim
+
+        inner_dim = num_attention_heads * attention_head_dim
+        self.inner_dim = inner_dim
+
+        # 2. Define input layers
+        self.in_channels = in_channels
+        self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6)
+        self.proj_in = nn.Linear(in_channels, inner_dim)
+
+        # 3. Define transformers blocks
+        self.transformer_blocks = nn.ModuleList(
+            [
+                BasicTransformerBlock(
+                    inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    cross_attention_dim=cross_attention_dim,
+                )
+                for d in range(num_layers)
+            ]
+        )
+
+        time_mix_inner_dim = inner_dim
+        self.temporal_transformer_blocks = nn.ModuleList(
+            [
+                TemporalBasicTransformerBlock(
+                    inner_dim,
+                    time_mix_inner_dim,
+                    num_attention_heads,
+                    attention_head_dim,
+                    cross_attention_dim=cross_attention_dim,
+                )
+                for _ in range(num_layers)
+            ]
+        )
+
+        time_embed_dim = in_channels * 4
+        self.time_pos_embed = TimestepEmbedding(in_channels, time_embed_dim, out_dim=in_channels)
+        self.time_proj = Timesteps(in_channels, True, 0)
+        self.time_mixer = AlphaBlender(alpha=0.5, merge_strategy="learned_with_images")
+
+        # 4. Define output layers
+        self.out_channels = in_channels if out_channels is None else out_channels
+        # TODO: should use out_channels for continuous projections
+        self.proj_out = nn.Linear(inner_dim, in_channels)
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        image_only_indicator: Optional[torch.Tensor] = None,
+        return_dict: bool = True,
+    ):
+        """
+        Args:
+            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
+                Input hidden_states.
+            num_frames (`int`):
+                The number of frames to be processed per batch. This is used to reshape the hidden states.
+            encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
+                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
+                self-attention.
+            image_only_indicator (`torch.LongTensor` of shape `(batch size, num_frames)`, *optional*):
+                A tensor indicating whether the input contains only images. 1 indicates that the input contains only
+                images, 0 indicates that the input contains video frames.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.transformer_temporal.TransformerTemporalModelOutput`] instead of a plain
+                tuple.
+
+        Returns:
+            [`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`:
+                If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is
+                returned, otherwise a `tuple` where the first element is the sample tensor.
+        """
+        # 1. Input
+        batch_frames, _, height, width = hidden_states.shape
+        num_frames = image_only_indicator.shape[-1]
+        batch_size = batch_frames // num_frames
+
+        time_context = encoder_hidden_states
+        time_context_first_timestep = time_context[None, :].reshape(
+            batch_size, num_frames, -1, time_context.shape[-1]
+        )[:, 0]
+        time_context = time_context_first_timestep[None, :].broadcast_to(
+            height * width, batch_size, 1, time_context.shape[-1]
+        )
+        time_context = time_context.reshape(height * width * batch_size, 1, time_context.shape[-1])
+
+        residual = hidden_states
+
+        hidden_states = self.norm(hidden_states)
+        inner_dim = hidden_states.shape[1]
+        hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_frames, height * width, inner_dim)
+        hidden_states = self.proj_in(hidden_states)
+
+        num_frames_emb = torch.arange(num_frames, device=hidden_states.device)
+        num_frames_emb = num_frames_emb.repeat(batch_size, 1)
+        num_frames_emb = num_frames_emb.reshape(-1)
+        t_emb = self.time_proj(num_frames_emb)
+
+        # `Timesteps` does not contain any weights and will always return f32 tensors
+        # but time_embedding might actually be running in fp16. so we need to cast here.
+        # there might be better ways to encapsulate this.
+        t_emb = t_emb.to(dtype=hidden_states.dtype)
+
+        emb = self.time_pos_embed(t_emb)
+        emb = emb[:, None, :]
+
+        # 2. Blocks
+        for block, temporal_block in zip(self.transformer_blocks, self.temporal_transformer_blocks):
+            if self.training and self.gradient_checkpointing:
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    block,
+                    hidden_states,
+                    None,
+                    encoder_hidden_states,
+                    None,
+                    use_reentrant=False,
+                )
+            else:
+                hidden_states = block(
+                    hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                )
+
+            hidden_states_mix = hidden_states
+            hidden_states_mix = hidden_states_mix + emb
+
+            hidden_states_mix = temporal_block(
+                hidden_states_mix,
+                num_frames=num_frames,
+                encoder_hidden_states=time_context,
+            )
+            hidden_states = self.time_mixer(
+                x_spatial=hidden_states,
+                x_temporal=hidden_states_mix,
+                image_only_indicator=image_only_indicator,
+            )
+
+        # 3. Output
+        hidden_states = self.proj_out(hidden_states)
+        hidden_states = hidden_states.reshape(batch_frames, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
+
+        output = hidden_states + residual
+
+        if not return_dict:
+            return (output,)
+
+        return TransformerTemporalModelOutput(sample=output)

src/diffusers/models/vae.py

@@ -22,7 +22,12 @@ from ..utils import BaseOutput, is_torch_version
 from ..utils.torch_utils import randn_tensor
 from .activations import get_activation
 from .attention_processor import SpatialNorm
-from .unet_2d_blocks import AutoencoderTinyBlock, UNetMidBlock2D, get_down_block, get_up_block
+from .unet_2d_blocks import (
+    AutoencoderTinyBlock,
+    UNetMidBlock2D,
+    get_down_block,
+    get_up_block,
+)
 
 
 @dataclass
@@ -274,7 +279,9 @@ class Decoder(nn.Module):
         self.gradient_checkpointing = False
 
     def forward(
-        self, sample: torch.FloatTensor, latent_embeds: Optional[torch.FloatTensor] = None
+        self,
+        sample: torch.FloatTensor,
+        latent_embeds: Optional[torch.FloatTensor] = None,
     ) -> torch.FloatTensor:
         r"""The forward method of the `Decoder` class."""
 
@@ -292,14 +299,20 @@ class Decoder(nn.Module):
             if is_torch_version(">=", "1.11.0"):
                 # middle
                 sample = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(self.mid_block), sample, latent_embeds, use_reentrant=False
+                    create_custom_forward(self.mid_block),
+                    sample,
+                    latent_embeds,
+                    use_reentrant=False,
                 )
                 sample = sample.to(upscale_dtype)
 
                 # up
                 for up_block in self.up_blocks:
                     sample = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(up_block), sample, latent_embeds, use_reentrant=False
+                        create_custom_forward(up_block),
+                        sample,
+                        latent_embeds,
+                        use_reentrant=False,
                     )
             else:
                 # middle
@@ -540,7 +553,10 @@ class MaskConditionDecoder(nn.Module):
             if is_torch_version(">=", "1.11.0"):
                 # middle
                 sample = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(self.mid_block), sample, latent_embeds, use_reentrant=False
+                    create_custom_forward(self.mid_block),
+                    sample,
+                    latent_embeds,
+                    use_reentrant=False,
                 )
                 sample = sample.to(upscale_dtype)
 
@@ -548,7 +564,10 @@ class MaskConditionDecoder(nn.Module):
                 if image is not None and mask is not None:
                     masked_image = (1 - mask) * image
                     im_x = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(self.condition_encoder), masked_image, mask, use_reentrant=False
+                        create_custom_forward(self.condition_encoder),
+                        masked_image,
+                        mask,
+                        use_reentrant=False,
                     )
 
                 # up
@@ -558,7 +577,10 @@ class MaskConditionDecoder(nn.Module):
                         mask_ = nn.functional.interpolate(mask, size=sample.shape[-2:], mode="nearest")
                         sample = sample * mask_ + sample_ * (1 - mask_)
                     sample = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(up_block), sample, latent_embeds, use_reentrant=False
+                        create_custom_forward(up_block),
+                        sample,
+                        latent_embeds,
+                        use_reentrant=False,
                     )
                 if image is not None and mask is not None:
                     sample = sample * mask + im_x[str(tuple(sample.shape))] * (1 - mask)
@@ -573,7 +595,9 @@ class MaskConditionDecoder(nn.Module):
                 if image is not None and mask is not None:
                     masked_image = (1 - mask) * image
                     im_x = torch.utils.checkpoint.checkpoint(
-                        create_custom_forward(self.condition_encoder), masked_image, mask
+                        create_custom_forward(self.condition_encoder),
+                        masked_image,
+                        mask,
                     )
 
                 # up
@@ -754,7 +778,10 @@ class DiagonalGaussianDistribution(object):
     def sample(self, generator: Optional[torch.Generator] = None) -> torch.FloatTensor:
         # make sure sample is on the same device as the parameters and has same dtype
         sample = randn_tensor(
-            self.mean.shape, generator=generator, device=self.parameters.device, dtype=self.parameters.dtype
+            self.mean.shape,
+            generator=generator,
+            device=self.parameters.device,
+            dtype=self.parameters.dtype,
         )
         x = self.mean + self.std * sample
         return x
@@ -764,7 +791,10 @@ class DiagonalGaussianDistribution(object):
             return torch.Tensor([0.0])
         else:
             if other is None:
-                return 0.5 * torch.sum(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=[1, 2, 3])
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
+                    dim=[1, 2, 3],
+                )
             else:
                 return 0.5 * torch.sum(
                     torch.pow(self.mean - other.mean, 2) / other.var
@@ -779,7 +809,10 @@ class DiagonalGaussianDistribution(object):
         if self.deterministic:
             return torch.Tensor([0.0])
         logtwopi = np.log(2.0 * np.pi)
-        return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims)
+        return 0.5 * torch.sum(
+            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+            dim=dims,
+        )
 
     def mode(self) -> torch.Tensor:
         return self.mean
@@ -820,7 +853,16 @@ class EncoderTiny(nn.Module):
             if i == 0:
                 layers.append(nn.Conv2d(in_channels, num_channels, kernel_size=3, padding=1))
             else:
-                layers.append(nn.Conv2d(num_channels, num_channels, kernel_size=3, padding=1, stride=2, bias=False))
+                layers.append(
+                    nn.Conv2d(
+                        num_channels,
+                        num_channels,
+                        kernel_size=3,
+                        padding=1,
+                        stride=2,
+                        bias=False,
+                    )
+                )
 
             for _ in range(num_block):
                 layers.append(AutoencoderTinyBlock(num_channels, num_channels, act_fn))
@@ -899,7 +941,15 @@ class DecoderTiny(nn.Module):
                 layers.append(nn.Upsample(scale_factor=upsampling_scaling_factor))
 
             conv_out_channel = num_channels if not is_final_block else out_channels
-            layers.append(nn.Conv2d(num_channels, conv_out_channel, kernel_size=3, padding=1, bias=is_final_block))
+            layers.append(
+                nn.Conv2d(
+                    num_channels,
+                    conv_out_channel,
+                    kernel_size=3,
+                    padding=1,
+                    bias=is_final_block,
+                )
+            )
 
         self.layers = nn.Sequential(*layers)
         self.gradient_checkpointing = False

src/diffusers/pipelines/__init__.py

@@ -17,7 +17,12 @@ from ..utils import (
 
 # These modules contain pipelines from multiple libraries/frameworks
 _dummy_objects = {}
-_import_structure = {"stable_diffusion": [], "stable_diffusion_xl": [], "latent_diffusion": [], "controlnet": []}
+_import_structure = {
+    "controlnet": [],
+    "latent_diffusion": [],
+    "stable_diffusion": [],
+    "stable_diffusion_xl": [],
+}
 
 try:
     if not is_torch_available():
@@ -39,7 +44,11 @@ else:
     _import_structure["dit"] = ["DiTPipeline"]
     _import_structure["latent_diffusion"].extend(["LDMSuperResolutionPipeline"])
     _import_structure["latent_diffusion_uncond"] = ["LDMPipeline"]
-    _import_structure["pipeline_utils"] = ["AudioPipelineOutput", "DiffusionPipeline", "ImagePipelineOutput"]
+    _import_structure["pipeline_utils"] = [
+        "AudioPipelineOutput",
+        "DiffusionPipeline",
+        "ImagePipelineOutput",
+    ]
     _import_structure["pndm"] = ["PNDMPipeline"]
     _import_structure["repaint"] = ["RePaintPipeline"]
     _import_structure["score_sde_ve"] = ["ScoreSdeVePipeline"]
@@ -61,7 +70,10 @@ except OptionalDependencyNotAvailable:
 
     _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
 else:
-    _import_structure["alt_diffusion"] = ["AltDiffusionImg2ImgPipeline", "AltDiffusionPipeline"]
+    _import_structure["alt_diffusion"] = [
+        "AltDiffusionImg2ImgPipeline",
+        "AltDiffusionPipeline",
+    ]
     _import_structure["animatediff"] = ["AnimateDiffPipeline"]
     _import_structure["audioldm"] = ["AudioLDMPipeline"]
     _import_structure["audioldm2"] = [
@@ -110,7 +122,10 @@ else:
         "KandinskyV22PriorEmb2EmbPipeline",
         "KandinskyV22PriorPipeline",
     ]
-    _import_structure["kandinsky3"] = ["Kandinsky3Img2ImgPipeline", "Kandinsky3Pipeline"]
+    _import_structure["kandinsky3"] = [
+        "Kandinsky3Img2ImgPipeline",
+        "Kandinsky3Pipeline",
+    ]
     _import_structure["latent_consistency_models"] = [
         "LatentConsistencyModelImg2ImgPipeline",
         "LatentConsistencyModelPipeline",
@@ -150,6 +165,7 @@ else:
         ]
     )
     _import_structure["stable_diffusion_safe"] = ["StableDiffusionPipelineSafe"]
+    _import_structure["stable_video_diffusion"] = ["StableVideoDiffusionPipeline"]
     _import_structure["stable_diffusion_xl"].extend(
         [
             "StableDiffusionXLImg2ImgPipeline",
@@ -158,7 +174,10 @@ else:
             "StableDiffusionXLPipeline",
         ]
     )
-    _import_structure["t2i_adapter"] = ["StableDiffusionAdapterPipeline", "StableDiffusionXLAdapterPipeline"]
+    _import_structure["t2i_adapter"] = [
+        "StableDiffusionAdapterPipeline",
+        "StableDiffusionXLAdapterPipeline",
+    ]
     _import_structure["text_to_video_synthesis"] = [
         "TextToVideoSDPipeline",
         "TextToVideoZeroPipeline",
@@ -216,7 +235,9 @@ try:
     if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
         raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
-    from ..utils import dummy_torch_and_transformers_and_k_diffusion_objects  # noqa F403
+    from ..utils import (
+        dummy_torch_and_transformers_and_k_diffusion_objects,
+    )
 
     _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_and_k_diffusion_objects))
 else:
@@ -259,7 +280,10 @@ except OptionalDependencyNotAvailable:
 
     _dummy_objects.update(get_objects_from_module(dummy_transformers_and_torch_and_note_seq_objects))
 else:
-    _import_structure["spectrogram_diffusion"] = ["MidiProcessor", "SpectrogramDiffusionPipeline"]
+    _import_structure["spectrogram_diffusion"] = [
+        "MidiProcessor",
+        "SpectrogramDiffusionPipeline",
+    ]
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     try:
@@ -269,7 +293,11 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from ..utils.dummy_pt_objects import *  # noqa F403
 
     else:
-        from .auto_pipeline import AutoPipelineForImage2Image, AutoPipelineForInpainting, AutoPipelineForText2Image
+        from .auto_pipeline import (
+            AutoPipelineForImage2Image,
+            AutoPipelineForInpainting,
+            AutoPipelineForText2Image,
+        )
         from .consistency_models import ConsistencyModelPipeline
         from .dance_diffusion import DanceDiffusionPipeline
         from .ddim import DDIMPipeline
@@ -277,7 +305,11 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .dit import DiTPipeline
         from .latent_diffusion import LDMSuperResolutionPipeline
         from .latent_diffusion_uncond import LDMPipeline
-        from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
+        from .pipeline_utils import (
+            AudioPipelineOutput,
+            DiffusionPipeline,
+            ImagePipelineOutput,
+        )
         from .pndm import PNDMPipeline
         from .repaint import RePaintPipeline
         from .score_sde_ve import ScoreSdeVePipeline
@@ -300,7 +332,11 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from .alt_diffusion import AltDiffusionImg2ImgPipeline, AltDiffusionPipeline
         from .animatediff import AnimateDiffPipeline
         from .audioldm import AudioLDMPipeline
-        from .audioldm2 import AudioLDM2Pipeline, AudioLDM2ProjectionModel, AudioLDM2UNet2DConditionModel
+        from .audioldm2 import (
+            AudioLDM2Pipeline,
+            AudioLDM2ProjectionModel,
+            AudioLDM2UNet2DConditionModel,
+        )
         from .blip_diffusion import BlipDiffusionPipeline
         from .controlnet import (
             BlipDiffusionControlNetPipeline,
@@ -344,7 +380,10 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             Kandinsky3Img2ImgPipeline,
             Kandinsky3Pipeline,
         )
-        from .latent_consistency_models import LatentConsistencyModelImg2ImgPipeline, LatentConsistencyModelPipeline
+        from .latent_consistency_models import (
+            LatentConsistencyModelImg2ImgPipeline,
+            LatentConsistencyModelPipeline,
+        )
         from .latent_diffusion import LDMTextToImagePipeline
         from .musicldm import MusicLDMPipeline
         from .paint_by_example import PaintByExamplePipeline
@@ -383,7 +422,11 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             StableDiffusionXLInstructPix2PixPipeline,
             StableDiffusionXLPipeline,
         )
-        from .t2i_adapter import StableDiffusionAdapterPipeline, StableDiffusionXLAdapterPipeline
+        from .stable_video_diffusion import StableVideoDiffusionPipeline
+        from .t2i_adapter import (
+            StableDiffusionAdapterPipeline,
+            StableDiffusionXLAdapterPipeline,
+        )
         from .text_to_video_synthesis import (
             TextToVideoSDPipeline,
             TextToVideoZeroPipeline,
@@ -473,7 +516,10 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
 
         else:
-            from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline
+            from .spectrogram_diffusion import (
+                MidiProcessor,
+                SpectrogramDiffusionPipeline,
+            )
 
 else:
     import sys

src/diffusers/pipelines/stable_diffusion/__init__.py

@@ -55,7 +55,9 @@ try:
     if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
         raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
-    from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
+    from ...utils.dummy_torch_and_transformers_objects import (
+        StableDiffusionImageVariationPipeline,
+    )
 
     _dummy_objects.update({"StableDiffusionImageVariationPipeline": StableDiffusionImageVariationPipeline})
 else:
@@ -90,7 +92,9 @@ try:
     ):
         raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
-    from ...utils import dummy_torch_and_transformers_and_k_diffusion_objects  # noqa F403
+    from ...utils import (
+        dummy_torch_and_transformers_and_k_diffusion_objects,
+    )
 
     _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_and_k_diffusion_objects))
 else:
@@ -137,18 +141,32 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             StableDiffusionPipelineOutput,
             StableDiffusionSafetyChecker,
         )
-        from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
+        from .pipeline_stable_diffusion_attend_and_excite import (
+            StableDiffusionAttendAndExcitePipeline,
+        )
         from .pipeline_stable_diffusion_gligen import StableDiffusionGLIGENPipeline
-        from .pipeline_stable_diffusion_gligen_text_image import StableDiffusionGLIGENTextImagePipeline
+        from .pipeline_stable_diffusion_gligen_text_image import (
+            StableDiffusionGLIGENTextImagePipeline,
+        )
         from .pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline
         from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
-        from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
-        from .pipeline_stable_diffusion_instruct_pix2pix import StableDiffusionInstructPix2PixPipeline
-        from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
+        from .pipeline_stable_diffusion_inpaint_legacy import (
+            StableDiffusionInpaintPipelineLegacy,
+        )
+        from .pipeline_stable_diffusion_instruct_pix2pix import (
+            StableDiffusionInstructPix2PixPipeline,
+        )
+        from .pipeline_stable_diffusion_latent_upscale import (
+            StableDiffusionLatentUpscalePipeline,
+        )
         from .pipeline_stable_diffusion_ldm3d import StableDiffusionLDM3DPipeline
-        from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
+        from .pipeline_stable_diffusion_model_editing import (
+            StableDiffusionModelEditingPipeline,
+        )
         from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
-        from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
+        from .pipeline_stable_diffusion_paradigms import (
+            StableDiffusionParadigmsPipeline,
+        )
         from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
         from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
         from .pipeline_stable_unclip import StableUnCLIPPipeline
@@ -160,9 +178,13 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
             raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
-        from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
+        from ...utils.dummy_torch_and_transformers_objects import (
+            StableDiffusionImageVariationPipeline,
+        )
     else:
-        from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline
+        from .pipeline_stable_diffusion_image_variation import (
+            StableDiffusionImageVariationPipeline,
+        )
 
     try:
         if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.26.0")):
@@ -174,9 +196,13 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             StableDiffusionPix2PixZeroPipeline,
         )
     else:
-        from .pipeline_stable_diffusion_depth2img import StableDiffusionDepth2ImgPipeline
+        from .pipeline_stable_diffusion_depth2img import (
+            StableDiffusionDepth2ImgPipeline,
+        )
         from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
-        from .pipeline_stable_diffusion_pix2pix_zero import StableDiffusionPix2PixZeroPipeline
+        from .pipeline_stable_diffusion_pix2pix_zero import (
+            StableDiffusionPix2PixZeroPipeline,
+        )
 
     try:
         if not (
@@ -189,7 +215,9 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     except OptionalDependencyNotAvailable:
         from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *
     else:
-        from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
+        from .pipeline_stable_diffusion_k_diffusion import (
+            StableDiffusionKDiffusionPipeline,
+        )
 
     try:
         if not (is_transformers_available() and is_onnx_available()):
@@ -197,11 +225,22 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
     except OptionalDependencyNotAvailable:
         from ...utils.dummy_onnx_objects import *
     else:
-        from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
-        from .pipeline_onnx_stable_diffusion_img2img import OnnxStableDiffusionImg2ImgPipeline
-        from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
-        from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
-        from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline
+        from .pipeline_onnx_stable_diffusion import (
+            OnnxStableDiffusionPipeline,
+            StableDiffusionOnnxPipeline,
+        )
+        from .pipeline_onnx_stable_diffusion_img2img import (
+            OnnxStableDiffusionImg2ImgPipeline,
+        )
+        from .pipeline_onnx_stable_diffusion_inpaint import (
+            OnnxStableDiffusionInpaintPipeline,
+        )
+        from .pipeline_onnx_stable_diffusion_inpaint_legacy import (
+            OnnxStableDiffusionInpaintPipelineLegacy,
+        )
+        from .pipeline_onnx_stable_diffusion_upscale import (
+            OnnxStableDiffusionUpscalePipeline,
+        )
 
     try:
         if not (is_transformers_available() and is_flax_available()):
@@ -210,8 +249,12 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
         from ...utils.dummy_flax_objects import *
     else:
         from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
-        from .pipeline_flax_stable_diffusion_img2img import FlaxStableDiffusionImg2ImgPipeline
-        from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
+        from .pipeline_flax_stable_diffusion_img2img import (
+            FlaxStableDiffusionImg2ImgPipeline,
+        )
+        from .pipeline_flax_stable_diffusion_inpaint import (
+            FlaxStableDiffusionInpaintPipeline,
+        )
         from .pipeline_output import FlaxStableDiffusionPipelineOutput
         from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
 

src/diffusers/pipelines/stable_video_diffusion/__init__.py

+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    BaseOutput,
+    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
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure.update(
+        {
+            "pipeline_stable_video_diffusion": [
+                "StableVideoDiffusionPipeline",
+                "StableVideoDiffusionPipelineOutput",
+            ],
+        }
+    )
+
+
+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_stable_video_diffusion import (
+            StableVideoDiffusionPipeline,
+            StableVideoDiffusionPipelineOutput,
+        )
+
+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)