# Copyright 2024 Alibaba DAMO-VILAB and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.utils.checkpoint
from ...configuration_utils import ConfigMixin, register_to_config
from ...loaders import UNet2DConditionLoadersMixin
from ...utils import logging
from ..activations import get_activation
from ..attention import Attention, FeedForward
from ..attention_processor import (
ADDED_KV_ATTENTION_PROCESSORS,
CROSS_ATTENTION_PROCESSORS,
AttentionProcessor,
AttnAddedKVProcessor,
AttnProcessor,
FusedAttnProcessor2_0,
)
from ..embeddings import TimestepEmbedding, Timesteps
from ..modeling_utils import ModelMixin
from ..transformers.transformer_temporal import TransformerTemporalModel
from .unet_3d_blocks import (
CrossAttnDownBlock3D,
CrossAttnUpBlock3D,
DownBlock3D,
UNetMidBlock3DCrossAttn,
UpBlock3D,
get_down_block,
get_up_block,
)
from .unet_3d_condition import UNet3DConditionOutput
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class I2VGenXLTransformerTemporalEncoder(nn.Module):
def __init__(
self,
dim: int,
num_attention_heads: int,
attention_head_dim: int,
activation_fn: str = "geglu",
upcast_attention: bool = False,
ff_inner_dim: Optional[int] = None,
dropout: int = 0.0,
):
super().__init__()
self.norm1 = nn.LayerNorm(dim, elementwise_affine=True, eps=1e-5)
self.attn1 = Attention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=False,
upcast_attention=upcast_attention,
out_bias=True,
)
self.ff = FeedForward(
dim,
dropout=dropout,
activation_fn=activation_fn,
final_dropout=False,
inner_dim=ff_inner_dim,
bias=True,
)
def forward(
self,
hidden_states: torch.Tensor,
) -> torch.Tensor:
norm_hidden_states = self.norm1(hidden_states)
attn_output = self.attn1(norm_hidden_states, encoder_hidden_states=None)
hidden_states = attn_output + hidden_states
if hidden_states.ndim == 4:
hidden_states = hidden_states.squeeze(1)
ff_output = self.ff(hidden_states)
hidden_states = ff_output + hidden_states
if hidden_states.ndim == 4:
hidden_states = hidden_states.squeeze(1)
return hidden_states
class I2VGenXLUNet(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
r"""
I2VGenXL UNet. It is a conditional 3D UNet model that takes a noisy sample, conditional state, and a timestep and
returns a sample-shaped output.
This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).
Parameters:
sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`):
Height and width of input/output sample.
in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample.
out_channels (`int`, *optional*, defaults to 4): The number of channels in the output.
down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`):
The tuple of downsample blocks to use.
up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`):
The tuple of upsample blocks to use.
block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`):
The tuple of output channels for each block.
layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block.
norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization.
If `None`, normalization and activation layers is skipped in post-processing.
cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features.
attention_head_dim (`int`, *optional*, defaults to 64): Attention head dim.
num_attention_heads (`int`, *optional*): The number of attention heads.
"""
_supports_gradient_checkpointing = False
@register_to_config
def __init__(
self,
sample_size: Optional[int] = None,
in_channels: int = 4,
out_channels: int = 4,
down_block_types: Tuple[str, ...] = (
"CrossAttnDownBlock3D",
"CrossAttnDownBlock3D",
"CrossAttnDownBlock3D",
"DownBlock3D",
),
up_block_types: Tuple[str, ...] = (
"UpBlock3D",
"CrossAttnUpBlock3D",
"CrossAttnUpBlock3D",
"CrossAttnUpBlock3D",
),
block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280),
layers_per_block: int = 2,
norm_num_groups: Optional[int] = 32,
cross_attention_dim: int = 1024,
attention_head_dim: Union[int, Tuple[int]] = 64,
num_attention_heads: Optional[Union[int, Tuple[int]]] = None,
):
super().__init__()
# When we first integrated the UNet into the library, we didn't have `attention_head_dim`. As a consequence
# of that, we used `num_attention_heads` for arguments that actually denote attention head dimension. This
# is why we ignore `num_attention_heads` and calculate it from `attention_head_dims` below.
# This is still an incorrect way of calculating `num_attention_heads` but we need to stick to it
# without running proper depcrecation cycles for the {down,mid,up} blocks which are a
# part of the public API.
num_attention_heads = attention_head_dim
# Check inputs
if len(down_block_types) != len(up_block_types):
raise ValueError(
f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."
)
if len(block_out_channels) != len(down_block_types):
raise ValueError(
f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."
)
if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types):
raise ValueError(
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}."
)
# input
self.conv_in = nn.Conv2d(in_channels + in_channels, block_out_channels[0], kernel_size=3, padding=1)
self.transformer_in = TransformerTemporalModel(
num_attention_heads=8,
attention_head_dim=num_attention_heads,
in_channels=block_out_channels[0],
num_layers=1,
norm_num_groups=norm_num_groups,
)
# image embedding
self.image_latents_proj_in = nn.Sequential(
nn.Conv2d(4, in_channels * 4, 3, padding=1),
nn.SiLU(),
nn.Conv2d(in_channels * 4, in_channels * 4, 3, stride=1, padding=1),
nn.SiLU(),
nn.Conv2d(in_channels * 4, in_channels, 3, stride=1, padding=1),
)
self.image_latents_temporal_encoder = I2VGenXLTransformerTemporalEncoder(
dim=in_channels,
num_attention_heads=2,
ff_inner_dim=in_channels * 4,
attention_head_dim=in_channels,
activation_fn="gelu",
)
self.image_latents_context_embedding = nn.Sequential(
nn.Conv2d(4, in_channels * 8, 3, padding=1),
nn.SiLU(),
nn.AdaptiveAvgPool2d((32, 32)),
nn.Conv2d(in_channels * 8, in_channels * 16, 3, stride=2, padding=1),
nn.SiLU(),
nn.Conv2d(in_channels * 16, cross_attention_dim, 3, stride=2, padding=1),
)
# other embeddings -- time, context, fps, etc.
time_embed_dim = block_out_channels[0] * 4
self.time_proj = Timesteps(block_out_channels[0], True, 0)
timestep_input_dim = block_out_channels[0]
self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn="silu")
self.context_embedding = nn.Sequential(
nn.Linear(cross_attention_dim, time_embed_dim),
nn.SiLU(),
nn.Linear(time_embed_dim, cross_attention_dim * in_channels),
)
self.fps_embedding = nn.Sequential(
nn.Linear(timestep_input_dim, time_embed_dim), nn.SiLU(), nn.Linear(time_embed_dim, time_embed_dim)
)
# blocks
self.down_blocks = nn.ModuleList([])
self.up_blocks = nn.ModuleList([])
if isinstance(num_attention_heads, int):
num_attention_heads = (num_attention_heads,) * len(down_block_types)
# down
output_channel = block_out_channels[0]
for i, down_block_type in enumerate(down_block_types):
input_channel = output_channel
output_channel = block_out_channels[i]
is_final_block = i == len(block_out_channels) - 1
down_block = get_down_block(
down_block_type,
num_layers=layers_per_block,
in_channels=input_channel,
out_channels=output_channel,
temb_channels=time_embed_dim,
add_downsample=not is_final_block,
resnet_eps=1e-05,
resnet_act_fn="silu",
resnet_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads[i],
downsample_padding=1,
dual_cross_attention=False,
)
self.down_blocks.append(down_block)
# mid
self.mid_block = UNetMidBlock3DCrossAttn(
in_channels=block_out_channels[-1],
temb_channels=time_embed_dim,
resnet_eps=1e-05,
resnet_act_fn="silu",
output_scale_factor=1,
cross_attention_dim=cross_attention_dim,
num_attention_heads=num_attention_heads[-1],
resnet_groups=norm_num_groups,
dual_cross_attention=False,
)
# count how many layers upsample the images
self.num_upsamplers = 0
# up
reversed_block_out_channels = list(reversed(block_out_channels))
reversed_num_attention_heads = list(reversed(num_attention_heads))
output_channel = reversed_block_out_channels[0]
for i, up_block_type in enumerate(up_block_types):
is_final_block = i == len(block_out_channels) - 1
prev_output_channel = output_channel
output_channel = reversed_block_out_channels[i]
input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)]
# add upsample block for all BUT final layer
if not is_final_block:
add_upsample = True
self.num_upsamplers += 1
else:
add_upsample = False
up_block = get_up_block(
up_block_type,
num_layers=layers_per_block + 1,
in_channels=input_channel,
out_channels=output_channel,
prev_output_channel=prev_output_channel,
temb_channels=time_embed_dim,
add_upsample=add_upsample,
resnet_eps=1e-05,
resnet_act_fn="silu",
resnet_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
num_attention_heads=reversed_num_attention_heads[i],
dual_cross_attention=False,
resolution_idx=i,
)
self.up_blocks.append(up_block)
prev_output_channel = output_channel
# out
self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-05)
self.conv_act = get_activation("silu")
self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1)
@property
# Copied from diffusers.models.unets.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()
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.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]):
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)
else:
module.set_processor(processor.pop(f"{name}.processor"))
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)
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking
def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None:
"""
Sets the attention processor to use [feed forward
chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers).
Parameters:
chunk_size (`int`, *optional*):
The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually
over each tensor of dim=`dim`.
dim (`int`, *optional*, defaults to `0`):
The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch)
or dim=1 (sequence length).
"""
if dim not in [0, 1]:
raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}")
# By default chunk size is 1
chunk_size = chunk_size or 1
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
if hasattr(module, "set_chunk_feed_forward"):
module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
for child in module.children():
fn_recursive_feed_forward(child, chunk_size, dim)
for module in self.children():
fn_recursive_feed_forward(module, chunk_size, dim)
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking
def disable_forward_chunking(self):
def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int):
if hasattr(module, "set_chunk_feed_forward"):
module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim)
for child in module.children():
fn_recursive_feed_forward(child, chunk_size, dim)
for module in self.children():
fn_recursive_feed_forward(module, None, 0)
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()):
processor = AttnAddedKVProcessor()
elif 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)
# Copied from diffusers.models.unets.unet_3d_condition.UNet3DConditionModel._set_gradient_checkpointing
def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)):
module.gradient_checkpointing = value
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.enable_freeu
def enable_freeu(self, s1, s2, b1, b2):
r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497.
The suffixes after the scaling factors represent the stage blocks where they are being applied.
Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that
are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.
Args:
s1 (`float`):
Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to
mitigate the "oversmoothing effect" in the enhanced denoising process.
s2 (`float`):
Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to
mitigate the "oversmoothing effect" in the enhanced denoising process.
b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features.
b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
"""
for i, upsample_block in enumerate(self.up_blocks):
setattr(upsample_block, "s1", s1)
setattr(upsample_block, "s2", s2)
setattr(upsample_block, "b1", b1)
setattr(upsample_block, "b2", b2)
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.disable_freeu
def disable_freeu(self):
"""Disables the FreeU mechanism."""
freeu_keys = {"s1", "s2", "b1", "b2"}
for i, upsample_block in enumerate(self.up_blocks):
for k in freeu_keys:
if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None:
setattr(upsample_block, k, None)
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.fuse_qkv_projections
def fuse_qkv_projections(self):
"""
Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value)
are fused. For cross-attention modules, key and value projection matrices are fused.
<Tip warning={true}>
This API is 🧪 experimental.
</Tip>
"""
self.original_attn_processors = None
for _, attn_processor in self.attn_processors.items():
if "Added" in str(attn_processor.__class__.__name__):
raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.")
self.original_attn_processors = self.attn_processors
for module in self.modules():
if isinstance(module, Attention):
module.fuse_projections(fuse=True)
self.set_attn_processor(FusedAttnProcessor2_0())
# Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
def unfuse_qkv_projections(self):
"""Disables the fused QKV projection if enabled.
<Tip warning={true}>
This API is 🧪 experimental.
</Tip>
"""
if self.original_attn_processors is not None:
self.set_attn_processor(self.original_attn_processors)
def forward(
self,
sample: torch.Tensor,
timestep: Union[torch.Tensor, float, int],
fps: torch.Tensor,
image_latents: torch.Tensor,
image_embeddings: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
timestep_cond: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
return_dict: bool = True,
) -> Union[UNet3DConditionOutput, Tuple[torch.Tensor]]:
r"""
The [`I2VGenXLUNet`] forward method.
Args:
sample (`torch.Tensor`):
The noisy input tensor with the following shape `(batch, num_frames, channel, height, width`.
timestep (`torch.Tensor` or `float` or `int`): The number of timesteps to denoise an input.
fps (`torch.Tensor`): Frames per second for the video being generated. Used as a "micro-condition".
image_latents (`torch.Tensor`): Image encodings from the VAE.
image_embeddings (`torch.Tensor`):
Projection embeddings of the conditioning image computed with a vision encoder.
encoder_hidden_states (`torch.Tensor`):
The encoder hidden states with shape `(batch, sequence_length, feature_dim)`.
cross_attention_kwargs (`dict`, *optional*):
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
`self.processor` in
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] instead of a plain
tuple.
Returns:
[`~models.unets.unet_3d_condition.UNet3DConditionOutput`] or `tuple`:
If `return_dict` is True, an [`~models.unets.unet_3d_condition.UNet3DConditionOutput`] is returned,
otherwise a `tuple` is returned where the first element is the sample tensor.
"""
batch_size, channels, num_frames, height, width = sample.shape
# By default samples have to be AT least a multiple of the overall upsampling factor.
# The overall upsampling factor is equal to 2 ** (# num of upsampling layears).
# However, the upsampling interpolation output size can be forced to fit any upsampling size
# on the fly if necessary.
default_overall_up_factor = 2**self.num_upsamplers
# upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor`
forward_upsample_size = False
upsample_size = None
if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
logger.info("Forward upsample size to force interpolation output size.")
forward_upsample_size = True
# 1. time
timesteps = timestep
if not torch.is_tensor(timesteps):
# TODO: this requires sync between CPU and GPU. So try to pass `timesteps` as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
is_mps = sample.device.type == "mps"
if isinstance(timesteps, float):
dtype = torch.float32 if is_mps else torch.float64
else:
dtype = torch.int32 if is_mps else torch.int64
timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device)
elif len(timesteps.shape) == 0:
timesteps = timesteps[None].to(sample.device)
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
timesteps = timesteps.expand(sample.shape[0])
t_emb = self.time_proj(timesteps)
# 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=self.dtype)
t_emb = self.time_embedding(t_emb, timestep_cond)
# 2. FPS
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
fps = fps.expand(fps.shape[0])
fps_emb = self.fps_embedding(self.time_proj(fps).to(dtype=self.dtype))
# 3. time + FPS embeddings.
emb = t_emb + fps_emb
emb = emb.repeat_interleave(repeats=num_frames, dim=0)
# 4. context embeddings.
# The context embeddings consist of both text embeddings from the input prompt
# AND the image embeddings from the input image. For images, both VAE encodings
# and the CLIP image embeddings are incorporated.
# So the final `context_embeddings` becomes the query for cross-attention.
context_emb = sample.new_zeros(batch_size, 0, self.config.cross_attention_dim)
context_emb = torch.cat([context_emb, encoder_hidden_states], dim=1)
image_latents_for_context_embds = image_latents[:, :, :1, :]
image_latents_context_embs = image_latents_for_context_embds.permute(0, 2, 1, 3, 4).reshape(
image_latents_for_context_embds.shape[0] * image_latents_for_context_embds.shape[2],
image_latents_for_context_embds.shape[1],
image_latents_for_context_embds.shape[3],
image_latents_for_context_embds.shape[4],
)
image_latents_context_embs = self.image_latents_context_embedding(image_latents_context_embs)
_batch_size, _channels, _height, _width = image_latents_context_embs.shape
image_latents_context_embs = image_latents_context_embs.permute(0, 2, 3, 1).reshape(
_batch_size, _height * _width, _channels
)
context_emb = torch.cat([context_emb, image_latents_context_embs], dim=1)
image_emb = self.context_embedding(image_embeddings)
image_emb = image_emb.view(-1, self.config.in_channels, self.config.cross_attention_dim)
context_emb = torch.cat([context_emb, image_emb], dim=1)
context_emb = context_emb.repeat_interleave(repeats=num_frames, dim=0)
image_latents = image_latents.permute(0, 2, 1, 3, 4).reshape(
image_latents.shape[0] * image_latents.shape[2],
image_latents.shape[1],
image_latents.shape[3],
image_latents.shape[4],
)
image_latents = self.image_latents_proj_in(image_latents)
image_latents = (
image_latents[None, :]
.reshape(batch_size, num_frames, channels, height, width)
.permute(0, 3, 4, 1, 2)
.reshape(batch_size * height * width, num_frames, channels)
)
image_latents = self.image_latents_temporal_encoder(image_latents)
image_latents = image_latents.reshape(batch_size, height, width, num_frames, channels).permute(0, 4, 3, 1, 2)
# 5. pre-process
sample = torch.cat([sample, image_latents], dim=1)
sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:])
sample = self.conv_in(sample)
sample = self.transformer_in(
sample,
num_frames=num_frames,
cross_attention_kwargs=cross_attention_kwargs,
return_dict=False,
)[0]
# 6. down
down_block_res_samples = (sample,)
for downsample_block in self.down_blocks:
if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention:
sample, res_samples = downsample_block(
hidden_states=sample,
temb=emb,
encoder_hidden_states=context_emb,
num_frames=num_frames,
cross_attention_kwargs=cross_attention_kwargs,
)
else:
sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames)
down_block_res_samples += res_samples
# 7. mid
if self.mid_block is not None:
sample = self.mid_block(
sample,
emb,
encoder_hidden_states=context_emb,
num_frames=num_frames,
cross_attention_kwargs=cross_attention_kwargs,
)
# 8. up
for i, upsample_block in enumerate(self.up_blocks):
is_final_block = i == len(self.up_blocks) - 1
res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
# if we have not reached the final block and need to forward the
# upsample size, we do it here
if not is_final_block and forward_upsample_size:
upsample_size = down_block_res_samples[-1].shape[2:]
if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
encoder_hidden_states=context_emb,
upsample_size=upsample_size,
num_frames=num_frames,
cross_attention_kwargs=cross_attention_kwargs,
)
else:
sample = upsample_block(
hidden_states=sample,
temb=emb,
res_hidden_states_tuple=res_samples,
upsample_size=upsample_size,
num_frames=num_frames,
)
# 9. post-process
sample = self.conv_norm_out(sample)
sample = self.conv_act(sample)
sample = self.conv_out(sample)
# reshape to (batch, channel, framerate, width, height)
sample = sample[None, :].reshape((-1, num_frames) + sample.shape[1:]).permute(0, 2, 1, 3, 4)
if not return_dict:
return (sample,)
return UNet3DConditionOutput(sample=sample)