931 lines
31 KiB
Python
931 lines
31 KiB
Python
# Copyright 2023 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import torch.utils.checkpoint as checkpoint
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from torch import nn
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from diffusers.models.resnet import Downsample2D, ResnetBlock2D, TemporalConvLayer, Upsample2D
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# from diffusers.models.transformer_2d import Transformer2DModel
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from .transformer_2d import Transformer2DModel
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# from diffusers.models.transformer_temporal import TransformerTemporalModel
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from .transformer_temporal import TransformerTemporalModel
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# Assign gradient checkpoint function to simple variable for readability.
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g_c = checkpoint.checkpoint
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def is_video(num_frames, only_video=True):
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if num_frames == 1 and not only_video:
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return False
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return num_frames > 1
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def custom_checkpoint(module, mode=None):
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if mode == None:
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raise ValueError('Mode for gradient checkpointing cannot be none.')
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custom_forward = None
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if mode == 'resnet':
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def custom_forward(hidden_states, temb):
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inputs = module(hidden_states, temb)
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return inputs
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if mode == 'attn':
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def custom_forward(
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hidden_states,
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encoder_hidden_states=None,
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cross_attention_kwargs=None,
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attention_mask=None,
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):
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inputs = module(
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hidden_states,
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encoder_hidden_states,
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cross_attention_kwargs,
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attention_mask
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)
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return inputs.sample
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if mode == 'temp':
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# If inputs are not None, we can assume that this was a single image.
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# Otherwise, do temporal convolutions / attention.
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def custom_forward(hidden_states, num_frames=None):
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if not is_video(num_frames):
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return hidden_states
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else:
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inputs = module(
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hidden_states,
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num_frames=num_frames
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)
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if isinstance(module, TransformerTemporalModel):
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return inputs.sample
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else:
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return inputs
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return custom_forward
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def transformer_g_c(transformer, sample, num_frames):
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sample = g_c(custom_checkpoint(transformer, mode='temp'),
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sample, num_frames, use_reentrant=False,
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)
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return sample
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def cross_attn_g_c(
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attn,
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temp_attn,
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resnet,
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temp_conv,
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hidden_states,
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encoder_hidden_states,
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cross_attention_kwargs,
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temb,
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num_frames,
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inverse_temp=False,
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attention_mask=None,
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):
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def ordered_g_c(idx):
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# Self and CrossAttention
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if idx == 0:
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return g_c(custom_checkpoint(attn, mode='attn'),
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hidden_states,
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encoder_hidden_states,
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cross_attention_kwargs,
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attention_mask,
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use_reentrant=False
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)
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# Temporal Self and CrossAttention
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if idx == 1:
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return g_c(custom_checkpoint(temp_attn, mode='temp'),
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hidden_states,
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num_frames,
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use_reentrant=False
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)
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# Resnets
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if idx == 2:
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return g_c(custom_checkpoint(resnet, mode='resnet'),
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hidden_states,
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temb,
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use_reentrant=False
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)
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# Temporal Convolutions
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if idx == 3:
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return g_c(custom_checkpoint(temp_conv, mode='temp'),
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hidden_states,
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num_frames,
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use_reentrant=False
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)
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# Here we call the function depending on the order in which they are called.
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# For some layers, the orders are different, so we access the appropriate one by index.
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if not inverse_temp:
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for idx in [0, 1, 2, 3]:
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hidden_states = ordered_g_c(idx)
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else:
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for idx in [2, 3, 0, 1]:
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hidden_states = ordered_g_c(idx)
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return hidden_states
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def up_down_g_c(resnet, temp_conv, hidden_states, temb, num_frames):
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hidden_states = g_c(custom_checkpoint(resnet, mode='resnet'),
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hidden_states,
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temb,
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use_reentrant=False
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)
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hidden_states = g_c(custom_checkpoint(temp_conv, mode='temp'),
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hidden_states,
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num_frames,
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use_reentrant=False
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)
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return hidden_states
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def get_down_block(
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down_block_type,
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num_layers,
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in_channels,
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out_channels,
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temb_channels,
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add_downsample,
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resnet_eps,
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resnet_act_fn,
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attn_num_head_channels,
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resnet_groups=None,
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cross_attention_dim=None,
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downsample_padding=None,
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dual_cross_attention=False,
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use_linear_projection=True,
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only_cross_attention=False,
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upcast_attention=False,
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resnet_time_scale_shift="default",
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use_image_embedding=False,
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unet_params=None,
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):
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if down_block_type == "DownBlock3D":
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return DownBlock3D(
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num_layers=num_layers,
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in_channels=in_channels,
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out_channels=out_channels,
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temb_channels=temb_channels,
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add_downsample=add_downsample,
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resnet_eps=resnet_eps,
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resnet_act_fn=resnet_act_fn,
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resnet_groups=resnet_groups,
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downsample_padding=downsample_padding,
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resnet_time_scale_shift=resnet_time_scale_shift,
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)
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elif down_block_type == "CrossAttnDownBlock3D":
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if cross_attention_dim is None:
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raise ValueError(
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"cross_attention_dim must be specified for CrossAttnDownBlock3D")
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return CrossAttnDownBlock3D(
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num_layers=num_layers,
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in_channels=in_channels,
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out_channels=out_channels,
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temb_channels=temb_channels,
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add_downsample=add_downsample,
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resnet_eps=resnet_eps,
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resnet_act_fn=resnet_act_fn,
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resnet_groups=resnet_groups,
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downsample_padding=downsample_padding,
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cross_attention_dim=cross_attention_dim,
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attn_num_head_channels=attn_num_head_channels,
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dual_cross_attention=dual_cross_attention,
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use_linear_projection=use_linear_projection,
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only_cross_attention=only_cross_attention,
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upcast_attention=upcast_attention,
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resnet_time_scale_shift=resnet_time_scale_shift,
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use_image_embedding=use_image_embedding,
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unet_params=unet_params,
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)
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raise ValueError(f"{down_block_type} does not exist.")
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def get_up_block(
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up_block_type,
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num_layers,
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in_channels,
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out_channels,
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prev_output_channel,
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temb_channels,
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add_upsample,
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resnet_eps,
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resnet_act_fn,
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attn_num_head_channels,
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resnet_groups=None,
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cross_attention_dim=None,
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dual_cross_attention=False,
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use_linear_projection=True,
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only_cross_attention=False,
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upcast_attention=False,
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resnet_time_scale_shift="default",
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use_image_embedding=False,
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unet_params=None,
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):
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if up_block_type == "UpBlock3D":
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return UpBlock3D(
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num_layers=num_layers,
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in_channels=in_channels,
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out_channels=out_channels,
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prev_output_channel=prev_output_channel,
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temb_channels=temb_channels,
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add_upsample=add_upsample,
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resnet_eps=resnet_eps,
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resnet_act_fn=resnet_act_fn,
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resnet_groups=resnet_groups,
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resnet_time_scale_shift=resnet_time_scale_shift,
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)
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elif up_block_type == "CrossAttnUpBlock3D":
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if cross_attention_dim is None:
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raise ValueError(
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"cross_attention_dim must be specified for CrossAttnUpBlock3D")
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return CrossAttnUpBlock3D(
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num_layers=num_layers,
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in_channels=in_channels,
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out_channels=out_channels,
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prev_output_channel=prev_output_channel,
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temb_channels=temb_channels,
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add_upsample=add_upsample,
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resnet_eps=resnet_eps,
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resnet_act_fn=resnet_act_fn,
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resnet_groups=resnet_groups,
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cross_attention_dim=cross_attention_dim,
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attn_num_head_channels=attn_num_head_channels,
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dual_cross_attention=dual_cross_attention,
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use_linear_projection=use_linear_projection,
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only_cross_attention=only_cross_attention,
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upcast_attention=upcast_attention,
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resnet_time_scale_shift=resnet_time_scale_shift,
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use_image_embedding=use_image_embedding,
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unet_params=unet_params,
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)
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raise ValueError(f"{up_block_type} does not exist.")
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class UNetMidBlock3DCrossAttn(nn.Module):
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def __init__(
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self,
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in_channels: int,
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temb_channels: int,
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dropout: float = 0.0,
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num_layers: int = 1,
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resnet_eps: float = 1e-6,
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resnet_time_scale_shift: str = "default",
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resnet_act_fn: str = "swish",
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resnet_groups: int = 32,
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resnet_pre_norm: bool = True,
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attn_num_head_channels=1,
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output_scale_factor=1.0,
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cross_attention_dim=1280,
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dual_cross_attention=False,
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use_linear_projection=True,
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upcast_attention=False,
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use_image_embedding=False,
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unet_params=None,
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):
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super().__init__()
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self.gradient_checkpointing = False
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self.has_cross_attention = True
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self.attn_num_head_channels = attn_num_head_channels
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resnet_groups = resnet_groups if resnet_groups is not None else min(
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in_channels // 4, 32)
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# there is always at least one resnet
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resnets = [
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ResnetBlock2D(
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in_channels=in_channels,
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out_channels=in_channels,
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temb_channels=temb_channels,
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eps=resnet_eps,
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groups=resnet_groups,
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dropout=dropout,
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time_embedding_norm=resnet_time_scale_shift,
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non_linearity=resnet_act_fn,
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output_scale_factor=output_scale_factor,
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pre_norm=resnet_pre_norm,
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)
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]
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temp_convs = [
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TemporalConvLayer(
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in_channels,
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in_channels,
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dropout=0.1
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)
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]
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attentions = []
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temp_attentions = []
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for _ in range(num_layers):
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attentions.append(
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Transformer2DModel(
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in_channels // attn_num_head_channels,
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attn_num_head_channels,
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in_channels=in_channels,
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num_layers=1,
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cross_attention_dim=cross_attention_dim,
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norm_num_groups=resnet_groups,
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use_linear_projection=use_linear_projection,
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upcast_attention=upcast_attention,
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use_image_embedding=use_image_embedding,
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unet_params=unet_params,
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)
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)
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temp_attentions.append(
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TransformerTemporalModel(
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in_channels // attn_num_head_channels,
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attn_num_head_channels,
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in_channels=in_channels,
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num_layers=1,
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cross_attention_dim=cross_attention_dim,
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norm_num_groups=resnet_groups,
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)
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)
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resnets.append(
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ResnetBlock2D(
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in_channels=in_channels,
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out_channels=in_channels,
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temb_channels=temb_channels,
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eps=resnet_eps,
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groups=resnet_groups,
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dropout=dropout,
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time_embedding_norm=resnet_time_scale_shift,
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non_linearity=resnet_act_fn,
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output_scale_factor=output_scale_factor,
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pre_norm=resnet_pre_norm,
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)
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)
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temp_convs.append(
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TemporalConvLayer(
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in_channels,
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in_channels,
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dropout=0.1
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)
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)
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self.resnets = nn.ModuleList(resnets)
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self.temp_convs = nn.ModuleList(temp_convs)
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self.attentions = nn.ModuleList(attentions)
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self.temp_attentions = nn.ModuleList(temp_attentions)
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def forward(
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self,
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hidden_states,
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temb=None,
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encoder_hidden_states=None,
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attention_mask=None,
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num_frames=1,
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cross_attention_kwargs=None,
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):
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if self.gradient_checkpointing:
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hidden_states = up_down_g_c(
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self.resnets[0],
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self.temp_convs[0],
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hidden_states,
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temb,
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num_frames
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)
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else:
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hidden_states = self.resnets[0](hidden_states, temb)
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hidden_states = self.temp_convs[0](
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hidden_states, num_frames=num_frames)
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for attn, temp_attn, resnet, temp_conv in zip(
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self.attentions, self.temp_attentions, self.resnets[1:], self.temp_convs[1:]
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):
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if self.gradient_checkpointing:
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hidden_states = cross_attn_g_c(
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attn,
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temp_attn,
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resnet,
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temp_conv,
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hidden_states,
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encoder_hidden_states,
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cross_attention_kwargs,
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temb,
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num_frames
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)
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else:
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hidden_states = attn(
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hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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cross_attention_kwargs=cross_attention_kwargs,
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).sample
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if num_frames > 1:
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hidden_states = temp_attn(
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hidden_states, num_frames=num_frames, attention_mask=attention_mask,
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).sample
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hidden_states = resnet(hidden_states, temb)
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if num_frames > 1:
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hidden_states = temp_conv(
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hidden_states, num_frames=num_frames)
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return hidden_states
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class CrossAttnDownBlock3D(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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temb_channels: int,
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dropout: float = 0.0,
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num_layers: int = 1,
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resnet_eps: float = 1e-6,
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resnet_time_scale_shift: str = "default",
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resnet_act_fn: str = "swish",
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resnet_groups: int = 32,
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resnet_pre_norm: bool = True,
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attn_num_head_channels=1,
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cross_attention_dim=1280,
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output_scale_factor=1.0,
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downsample_padding=1,
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add_downsample=True,
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dual_cross_attention=False,
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use_linear_projection=False,
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only_cross_attention=False,
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upcast_attention=False,
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use_image_embedding=False,
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unet_params=None,
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):
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super().__init__()
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resnets = []
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attentions = []
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temp_attentions = []
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temp_convs = []
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self.gradient_checkpointing = False
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self.has_cross_attention = True
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self.attn_num_head_channels = attn_num_head_channels
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for i in range(num_layers):
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in_channels = in_channels if i == 0 else out_channels
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resnets.append(
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ResnetBlock2D(
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in_channels=in_channels,
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out_channels=out_channels,
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temb_channels=temb_channels,
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eps=resnet_eps,
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groups=resnet_groups,
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dropout=dropout,
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time_embedding_norm=resnet_time_scale_shift,
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non_linearity=resnet_act_fn,
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output_scale_factor=output_scale_factor,
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pre_norm=resnet_pre_norm,
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)
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)
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temp_convs.append(
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TemporalConvLayer(
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out_channels,
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out_channels,
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dropout=0.1
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)
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)
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attentions.append(
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Transformer2DModel(
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out_channels // attn_num_head_channels,
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attn_num_head_channels,
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in_channels=out_channels,
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num_layers=1,
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cross_attention_dim=cross_attention_dim,
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norm_num_groups=resnet_groups,
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use_linear_projection=use_linear_projection,
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only_cross_attention=only_cross_attention,
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upcast_attention=upcast_attention,
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use_image_embedding=use_image_embedding,
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unet_params=unet_params,
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)
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)
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temp_attentions.append(
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TransformerTemporalModel(
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out_channels // attn_num_head_channels,
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attn_num_head_channels,
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in_channels=out_channels,
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num_layers=1,
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cross_attention_dim=cross_attention_dim,
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norm_num_groups=resnet_groups,
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)
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)
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self.resnets = nn.ModuleList(resnets)
|
|
self.temp_convs = nn.ModuleList(temp_convs)
|
|
self.attentions = nn.ModuleList(attentions)
|
|
self.temp_attentions = nn.ModuleList(temp_attentions)
|
|
|
|
if add_downsample:
|
|
self.downsamplers = nn.ModuleList(
|
|
[
|
|
Downsample2D(
|
|
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
|
|
)
|
|
]
|
|
)
|
|
else:
|
|
self.downsamplers = None
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
temb=None,
|
|
encoder_hidden_states=None,
|
|
attention_mask=None,
|
|
num_frames=1,
|
|
cross_attention_kwargs=None,
|
|
):
|
|
# TODO(Patrick, William) - attention mask is not used
|
|
output_states = ()
|
|
layer_idx = 0
|
|
|
|
for resnet, temp_conv, attn, temp_attn in zip(
|
|
self.resnets, self.temp_convs, self.attentions, self.temp_attentions
|
|
):
|
|
if self.gradient_checkpointing:
|
|
hidden_states = cross_attn_g_c(
|
|
attn,
|
|
temp_attn,
|
|
resnet,
|
|
temp_conv,
|
|
hidden_states,
|
|
encoder_hidden_states,
|
|
cross_attention_kwargs,
|
|
temb,
|
|
num_frames,
|
|
inverse_temp=True
|
|
)
|
|
else:
|
|
hidden_states = resnet(hidden_states, temb)
|
|
if num_frames > 1:
|
|
hidden_states = temp_conv(
|
|
hidden_states, num_frames=num_frames)
|
|
|
|
hidden_states = attn(
|
|
hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
).sample
|
|
if num_frames > 1:
|
|
hidden_states = temp_attn(
|
|
hidden_states, num_frames=num_frames, attention_mask=attention_mask,
|
|
).sample
|
|
layer_idx += 1
|
|
output_states += (hidden_states,)
|
|
|
|
if self.downsamplers is not None:
|
|
for downsampler in self.downsamplers:
|
|
hidden_states = downsampler(hidden_states)
|
|
|
|
output_states += (hidden_states,)
|
|
|
|
return hidden_states, output_states
|
|
|
|
|
|
class DownBlock3D(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_channels: int,
|
|
temb_channels: int,
|
|
dropout: float = 0.0,
|
|
num_layers: int = 1,
|
|
resnet_eps: float = 1e-6,
|
|
resnet_time_scale_shift: str = "default",
|
|
resnet_act_fn: str = "swish",
|
|
resnet_groups: int = 32,
|
|
resnet_pre_norm: bool = True,
|
|
output_scale_factor=1.0,
|
|
add_downsample=True,
|
|
downsample_padding=1,
|
|
):
|
|
super().__init__()
|
|
resnets = []
|
|
temp_convs = []
|
|
|
|
self.gradient_checkpointing = False
|
|
for i in range(num_layers):
|
|
in_channels = in_channels if i == 0 else out_channels
|
|
resnets.append(
|
|
ResnetBlock2D(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
temb_channels=temb_channels,
|
|
eps=resnet_eps,
|
|
groups=resnet_groups,
|
|
dropout=dropout,
|
|
time_embedding_norm=resnet_time_scale_shift,
|
|
non_linearity=resnet_act_fn,
|
|
output_scale_factor=output_scale_factor,
|
|
pre_norm=resnet_pre_norm,
|
|
)
|
|
)
|
|
temp_convs.append(
|
|
TemporalConvLayer(
|
|
out_channels,
|
|
out_channels,
|
|
dropout=0.1
|
|
)
|
|
)
|
|
|
|
self.resnets = nn.ModuleList(resnets)
|
|
self.temp_convs = nn.ModuleList(temp_convs)
|
|
|
|
if add_downsample:
|
|
self.downsamplers = nn.ModuleList(
|
|
[
|
|
Downsample2D(
|
|
out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op"
|
|
)
|
|
]
|
|
)
|
|
else:
|
|
self.downsamplers = None
|
|
|
|
def forward(self, hidden_states, temb=None, num_frames=1):
|
|
output_states = ()
|
|
|
|
for resnet, temp_conv in zip(self.resnets, self.temp_convs):
|
|
if self.gradient_checkpointing:
|
|
hidden_states = up_down_g_c(
|
|
resnet, temp_conv, hidden_states, temb, num_frames)
|
|
else:
|
|
hidden_states = resnet(hidden_states, temb)
|
|
|
|
if num_frames > 1:
|
|
hidden_states = temp_conv(
|
|
hidden_states, num_frames=num_frames)
|
|
|
|
output_states += (hidden_states,)
|
|
|
|
if self.downsamplers is not None:
|
|
for downsampler in self.downsamplers:
|
|
hidden_states = downsampler(hidden_states)
|
|
|
|
output_states += (hidden_states,)
|
|
|
|
return hidden_states, output_states
|
|
|
|
|
|
class CrossAttnUpBlock3D(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
out_channels: int,
|
|
prev_output_channel: int,
|
|
temb_channels: int,
|
|
dropout: float = 0.0,
|
|
num_layers: int = 1,
|
|
resnet_eps: float = 1e-6,
|
|
resnet_time_scale_shift: str = "default",
|
|
resnet_act_fn: str = "swish",
|
|
resnet_groups: int = 32,
|
|
resnet_pre_norm: bool = True,
|
|
attn_num_head_channels=1,
|
|
cross_attention_dim=1280,
|
|
output_scale_factor=1.0,
|
|
add_upsample=True,
|
|
dual_cross_attention=False,
|
|
use_linear_projection=False,
|
|
only_cross_attention=False,
|
|
upcast_attention=False,
|
|
use_image_embedding=False,
|
|
unet_params=None,
|
|
):
|
|
super().__init__()
|
|
resnets = []
|
|
temp_convs = []
|
|
attentions = []
|
|
temp_attentions = []
|
|
|
|
self.gradient_checkpointing = False
|
|
self.has_cross_attention = True
|
|
self.attn_num_head_channels = attn_num_head_channels
|
|
|
|
for i in range(num_layers):
|
|
res_skip_channels = in_channels if (
|
|
i == num_layers - 1) else out_channels
|
|
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
|
|
|
resnets.append(
|
|
ResnetBlock2D(
|
|
in_channels=resnet_in_channels + res_skip_channels,
|
|
out_channels=out_channels,
|
|
temb_channels=temb_channels,
|
|
eps=resnet_eps,
|
|
groups=resnet_groups,
|
|
dropout=dropout,
|
|
time_embedding_norm=resnet_time_scale_shift,
|
|
non_linearity=resnet_act_fn,
|
|
output_scale_factor=output_scale_factor,
|
|
pre_norm=resnet_pre_norm,
|
|
)
|
|
)
|
|
temp_convs.append(
|
|
TemporalConvLayer(
|
|
out_channels,
|
|
out_channels,
|
|
dropout=0.1
|
|
)
|
|
)
|
|
attentions.append(
|
|
Transformer2DModel(
|
|
out_channels // attn_num_head_channels,
|
|
attn_num_head_channels,
|
|
in_channels=out_channels,
|
|
num_layers=1,
|
|
cross_attention_dim=cross_attention_dim,
|
|
norm_num_groups=resnet_groups,
|
|
use_linear_projection=use_linear_projection,
|
|
only_cross_attention=only_cross_attention,
|
|
upcast_attention=upcast_attention,
|
|
use_image_embedding=use_image_embedding,
|
|
unet_params=unet_params,
|
|
)
|
|
)
|
|
temp_attentions.append(
|
|
TransformerTemporalModel(
|
|
out_channels // attn_num_head_channels,
|
|
attn_num_head_channels,
|
|
in_channels=out_channels,
|
|
num_layers=1,
|
|
cross_attention_dim=cross_attention_dim,
|
|
norm_num_groups=resnet_groups,
|
|
)
|
|
)
|
|
self.resnets = nn.ModuleList(resnets)
|
|
self.temp_convs = nn.ModuleList(temp_convs)
|
|
self.attentions = nn.ModuleList(attentions)
|
|
self.temp_attentions = nn.ModuleList(temp_attentions)
|
|
|
|
if add_upsample:
|
|
self.upsamplers = nn.ModuleList(
|
|
[Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
|
|
else:
|
|
self.upsamplers = None
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
res_hidden_states_tuple,
|
|
temb=None,
|
|
encoder_hidden_states=None,
|
|
upsample_size=None,
|
|
attention_mask=None,
|
|
num_frames=1,
|
|
cross_attention_kwargs=None,
|
|
):
|
|
# TODO(Patrick, William) - attention mask is not used
|
|
output_states = ()
|
|
for resnet, temp_conv, attn, temp_attn in zip(
|
|
self.resnets, self.temp_convs, self.attentions, self.temp_attentions
|
|
):
|
|
# pop res hidden states
|
|
res_hidden_states = res_hidden_states_tuple[-1]
|
|
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
|
hidden_states = torch.cat(
|
|
[hidden_states, res_hidden_states], dim=1)
|
|
|
|
if self.gradient_checkpointing:
|
|
hidden_states = cross_attn_g_c(
|
|
attn,
|
|
temp_attn,
|
|
resnet,
|
|
temp_conv,
|
|
hidden_states,
|
|
encoder_hidden_states,
|
|
cross_attention_kwargs,
|
|
temb,
|
|
num_frames,
|
|
inverse_temp=True
|
|
)
|
|
else:
|
|
hidden_states = resnet(hidden_states, temb)
|
|
|
|
if num_frames > 1:
|
|
hidden_states = temp_conv(
|
|
hidden_states, num_frames=num_frames)
|
|
|
|
hidden_states = attn(
|
|
hidden_states,
|
|
encoder_hidden_states=encoder_hidden_states,
|
|
cross_attention_kwargs=cross_attention_kwargs,
|
|
).sample
|
|
|
|
if num_frames > 1:
|
|
hidden_states = temp_attn(
|
|
hidden_states, num_frames=num_frames,
|
|
attention_mask=attention_mask,
|
|
).sample
|
|
output_states += (hidden_states,)
|
|
if self.upsamplers is not None:
|
|
for upsampler in self.upsamplers:
|
|
hidden_states = upsampler(hidden_states, upsample_size)
|
|
output_states += (hidden_states,)
|
|
|
|
return hidden_states, output_states
|
|
|
|
|
|
class UpBlock3D(nn.Module):
|
|
def __init__(
|
|
self,
|
|
in_channels: int,
|
|
prev_output_channel: int,
|
|
out_channels: int,
|
|
temb_channels: int,
|
|
dropout: float = 0.0,
|
|
num_layers: int = 1,
|
|
resnet_eps: float = 1e-6,
|
|
resnet_time_scale_shift: str = "default",
|
|
resnet_act_fn: str = "swish",
|
|
resnet_groups: int = 32,
|
|
resnet_pre_norm: bool = True,
|
|
output_scale_factor=1.0,
|
|
add_upsample=True,
|
|
):
|
|
super().__init__()
|
|
resnets = []
|
|
temp_convs = []
|
|
self.gradient_checkpointing = False
|
|
for i in range(num_layers):
|
|
res_skip_channels = in_channels if (
|
|
i == num_layers - 1) else out_channels
|
|
resnet_in_channels = prev_output_channel if i == 0 else out_channels
|
|
|
|
resnets.append(
|
|
ResnetBlock2D(
|
|
in_channels=resnet_in_channels + res_skip_channels,
|
|
out_channels=out_channels,
|
|
temb_channels=temb_channels,
|
|
eps=resnet_eps,
|
|
groups=resnet_groups,
|
|
dropout=dropout,
|
|
time_embedding_norm=resnet_time_scale_shift,
|
|
non_linearity=resnet_act_fn,
|
|
output_scale_factor=output_scale_factor,
|
|
pre_norm=resnet_pre_norm,
|
|
)
|
|
)
|
|
temp_convs.append(
|
|
TemporalConvLayer(
|
|
out_channels,
|
|
out_channels,
|
|
dropout=0.1
|
|
)
|
|
)
|
|
|
|
self.resnets = nn.ModuleList(resnets)
|
|
self.temp_convs = nn.ModuleList(temp_convs)
|
|
|
|
if add_upsample:
|
|
self.upsamplers = nn.ModuleList(
|
|
[Upsample2D(out_channels, use_conv=True, out_channels=out_channels)])
|
|
else:
|
|
self.upsamplers = None
|
|
|
|
def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None, num_frames=1):
|
|
output_states = ()
|
|
for resnet, temp_conv in zip(self.resnets, self.temp_convs):
|
|
# pop res hidden states
|
|
res_hidden_states = res_hidden_states_tuple[-1]
|
|
res_hidden_states_tuple = res_hidden_states_tuple[:-1]
|
|
hidden_states = torch.cat(
|
|
[hidden_states, res_hidden_states], dim=1)
|
|
|
|
if self.gradient_checkpointing:
|
|
hidden_states = up_down_g_c(
|
|
resnet, temp_conv, hidden_states, temb, num_frames)
|
|
else:
|
|
hidden_states = resnet(hidden_states, temb)
|
|
|
|
if num_frames > 1:
|
|
hidden_states = temp_conv(
|
|
hidden_states, num_frames=num_frames)
|
|
output_states += (hidden_states,)
|
|
if self.upsamplers is not None:
|
|
for upsampler in self.upsamplers:
|
|
hidden_states = upsampler(hidden_states, upsample_size)
|
|
output_states += (hidden_states,)
|
|
|
|
return hidden_states, output_states
|