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| # Copyright 2023 Alibaba DAMO-VILAB and The HuggingFace Team. All rights reserved. | |
| # Copyright 2023 The ModelScope Team. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from dataclasses import dataclass | |
| from typing import Any, Dict, List, Optional, Tuple, Union | |
| import torch | |
| import torch.nn as nn | |
| import torch.utils.checkpoint | |
| from diffusers.configuration_utils import ConfigMixin, register_to_config | |
| from diffusers.utils import BaseOutput, logging | |
| from diffusers.models.embeddings import TimestepEmbedding, Timesteps | |
| from diffusers.models.modeling_utils import ModelMixin | |
| from diffusers.models.transformer_temporal import TransformerTemporalModel | |
| from .unet_3d_blocks import ( | |
| CrossAttnDownBlock3D, | |
| CrossAttnUpBlock3D, | |
| DownBlock3D, | |
| UNetMidBlock3DCrossAttn, | |
| UpBlock3D, | |
| get_down_block, | |
| get_up_block, | |
| transformer_g_c | |
| ) | |
| logger = logging.get_logger(__name__) # pylint: disable=invalid-name | |
| class UNet3DConditionOutput(BaseOutput): | |
| """ | |
| Args: | |
| sample (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`): | |
| Hidden states conditioned on `encoder_hidden_states` input. Output of last layer of model. | |
| """ | |
| sample: torch.FloatTensor | |
| class UNet3DConditionModel(ModelMixin, ConfigMixin): | |
| r""" | |
| UNet3DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep | |
| and returns sample shaped output. | |
| This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library | |
| implements for all the models (such as downloading or saving, etc.) | |
| 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. | |
| downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. | |
| mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. | |
| act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. | |
| norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. | |
| If `None`, it will skip the normalization and activation layers in post-processing | |
| norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. | |
| cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features. | |
| attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. | |
| """ | |
| _supports_gradient_checkpointing = True | |
| 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, | |
| downsample_padding: int = 1, | |
| mid_block_scale_factor: float = 1, | |
| act_fn: str = "silu", | |
| norm_num_groups: Optional[int] = 32, | |
| norm_eps: float = 1e-5, | |
| cross_attention_dim: int = 1024, | |
| attention_head_dim: Union[int, Tuple[int]] = 64, | |
| ): | |
| super().__init__() | |
| self.sample_size = sample_size | |
| self.gradient_checkpointing = False | |
| # 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(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types): | |
| raise ValueError( | |
| f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}." | |
| ) | |
| # input | |
| conv_in_kernel = 3 | |
| conv_out_kernel = 3 | |
| conv_in_padding = (conv_in_kernel - 1) // 2 | |
| self.conv_in = nn.Conv2d( | |
| in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding | |
| ) | |
| # time | |
| 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=act_fn, | |
| ) | |
| self.transformer_in = TransformerTemporalModel( | |
| num_attention_heads=8, | |
| attention_head_dim=attention_head_dim, | |
| in_channels=block_out_channels[0], | |
| num_layers=1, | |
| ) | |
| # class embedding | |
| self.down_blocks = nn.ModuleList([]) | |
| self.up_blocks = nn.ModuleList([]) | |
| if isinstance(attention_head_dim, int): | |
| attention_head_dim = (attention_head_dim,) * 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=norm_eps, | |
| resnet_act_fn=act_fn, | |
| resnet_groups=norm_num_groups, | |
| cross_attention_dim=cross_attention_dim, | |
| attn_num_head_channels=attention_head_dim[i], | |
| downsample_padding=downsample_padding, | |
| 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=norm_eps, | |
| resnet_act_fn=act_fn, | |
| output_scale_factor=mid_block_scale_factor, | |
| cross_attention_dim=cross_attention_dim, | |
| attn_num_head_channels=attention_head_dim[-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_attention_head_dim = list(reversed(attention_head_dim)) | |
| 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=norm_eps, | |
| resnet_act_fn=act_fn, | |
| resnet_groups=norm_num_groups, | |
| cross_attention_dim=cross_attention_dim, | |
| attn_num_head_channels=reversed_attention_head_dim[i], | |
| dual_cross_attention=False, | |
| ) | |
| self.up_blocks.append(up_block) | |
| prev_output_channel = output_channel | |
| # out | |
| if norm_num_groups is not None: | |
| self.conv_norm_out = nn.GroupNorm( | |
| num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps | |
| ) | |
| self.conv_act = nn.SiLU() | |
| else: | |
| self.conv_norm_out = None | |
| self.conv_act = None | |
| conv_out_padding = (conv_out_kernel - 1) // 2 | |
| self.conv_out = nn.Conv2d( | |
| block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding | |
| ) | |
| def set_attention_slice(self, slice_size): | |
| r""" | |
| Enable sliced attention computation. | |
| When this option is enabled, the attention module will split the input tensor in slices, to compute attention | |
| in several steps. This is useful to save some memory in exchange for a small speed decrease. | |
| Args: | |
| slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): | |
| When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If | |
| `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is | |
| provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` | |
| must be a multiple of `slice_size`. | |
| """ | |
| sliceable_head_dims = [] | |
| def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module): | |
| if hasattr(module, "set_attention_slice"): | |
| sliceable_head_dims.append(module.sliceable_head_dim) | |
| for child in module.children(): | |
| fn_recursive_retrieve_slicable_dims(child) | |
| # retrieve number of attention layers | |
| for module in self.children(): | |
| fn_recursive_retrieve_slicable_dims(module) | |
| num_slicable_layers = len(sliceable_head_dims) | |
| if slice_size == "auto": | |
| # half the attention head size is usually a good trade-off between | |
| # speed and memory | |
| slice_size = [dim // 2 for dim in sliceable_head_dims] | |
| elif slice_size == "max": | |
| # make smallest slice possible | |
| slice_size = num_slicable_layers * [1] | |
| slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size | |
| if len(slice_size) != len(sliceable_head_dims): | |
| raise ValueError( | |
| f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" | |
| f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." | |
| ) | |
| for i in range(len(slice_size)): | |
| size = slice_size[i] | |
| dim = sliceable_head_dims[i] | |
| if size is not None and size > dim: | |
| raise ValueError(f"size {size} has to be smaller or equal to {dim}.") | |
| # Recursively walk through all the children. | |
| # Any children which exposes the set_attention_slice method | |
| # gets the message | |
| def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): | |
| if hasattr(module, "set_attention_slice"): | |
| module.set_attention_slice(slice_size.pop()) | |
| for child in module.children(): | |
| fn_recursive_set_attention_slice(child, slice_size) | |
| reversed_slice_size = list(reversed(slice_size)) | |
| for module in self.children(): | |
| fn_recursive_set_attention_slice(module, reversed_slice_size) | |
| def _set_gradient_checkpointing(self, value=False): | |
| self.gradient_checkpointing = value | |
| self.mid_block.gradient_checkpointing = value | |
| for module in self.down_blocks + self.up_blocks: | |
| if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)): | |
| module.gradient_checkpointing = value | |
| def forward( | |
| self, | |
| sample: torch.FloatTensor, | |
| timestep: Union[torch.Tensor, float, int], | |
| encoder_hidden_states: torch.Tensor, | |
| class_labels: Optional[torch.Tensor] = None, | |
| timestep_cond: Optional[torch.Tensor] = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| cross_attention_kwargs: Optional[Dict[str, Any]] = None, | |
| down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, | |
| mid_block_additional_residual: Optional[torch.Tensor] = None, | |
| return_dict: bool = True, | |
| ) -> Union[UNet3DConditionOutput, Tuple]: | |
| r""" | |
| Args: | |
| sample (`torch.FloatTensor`): (batch, num_frames, channel, height, width) noisy inputs tensor | |
| timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps | |
| encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states | |
| return_dict (`bool`, *optional*, defaults to `True`): | |
| Whether or not to return a [`models.unet_2d_condition.UNet3DConditionOutput`] instead of a plain tuple. | |
| cross_attention_kwargs (`dict`, *optional*): | |
| A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under | |
| `self.processor` in | |
| [diffusers.cross_attention](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py). | |
| Returns: | |
| [`~models.unet_2d_condition.UNet3DConditionOutput`] or `tuple`: | |
| [`~models.unet_2d_condition.UNet3DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When | |
| returning a tuple, the first element is the sample tensor. | |
| """ | |
| # 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 | |
| # prepare attention_mask | |
| if attention_mask is not None: | |
| attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 | |
| attention_mask = attention_mask.unsqueeze(1) | |
| # 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(timestep, 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 | |
| num_frames = sample.shape[2] | |
| 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) | |
| emb = self.time_embedding(t_emb, timestep_cond) | |
| emb = emb.repeat_interleave(repeats=num_frames, dim=0) | |
| encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0) | |
| # 2. pre-process | |
| sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:]) | |
| sample = self.conv_in(sample) | |
| if self.gradient_checkpointing: | |
| sample = transformer_g_c(self.transformer_in, sample, num_frames) | |
| else: | |
| sample = self.transformer_in(sample, num_frames=num_frames).sample | |
| # 3. 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=encoder_hidden_states, | |
| attention_mask=attention_mask, | |
| 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 | |
| if down_block_additional_residuals is not None: | |
| new_down_block_res_samples = () | |
| for down_block_res_sample, down_block_additional_residual in zip( | |
| down_block_res_samples, down_block_additional_residuals | |
| ): | |
| down_block_res_sample = down_block_res_sample + down_block_additional_residual | |
| new_down_block_res_samples += (down_block_res_sample,) | |
| down_block_res_samples = new_down_block_res_samples | |
| # 4. mid | |
| if self.mid_block is not None: | |
| sample = self.mid_block( | |
| sample, | |
| emb, | |
| encoder_hidden_states=encoder_hidden_states, | |
| attention_mask=attention_mask, | |
| num_frames=num_frames, | |
| cross_attention_kwargs=cross_attention_kwargs, | |
| ) | |
| if mid_block_additional_residual is not None: | |
| sample = sample + mid_block_additional_residual | |
| # 5. 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=encoder_hidden_states, | |
| upsample_size=upsample_size, | |
| attention_mask=attention_mask, | |
| 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, | |
| ) | |
| # 6. post-process | |
| if self.conv_norm_out: | |
| 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) | |