Delete unet_3d_blocks.py

unet_3d_blocks.py

@@ -1,842 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import torch
-import torch.utils.checkpoint as checkpoint
-from torch import nn
-from diffusers.models.resnet import Downsample2D, ResnetBlock2D, TemporalConvLayer, Upsample2D
-from diffusers.models.transformer_2d import Transformer2DModel
-from diffusers.models.transformer_temporal import TransformerTemporalModel
-
-# Assign gradient checkpoint function to simple variable for readability.
-g_c = checkpoint.checkpoint
-
-def use_temporal(module, num_frames, x):
-    if num_frames == 1:
-        if isinstance(module, TransformerTemporalModel):
-            return {"sample": x}
-        else:
-            return x
-
-def custom_checkpoint(module, mode=None):
-    if mode == None: raise ValueError('Mode for gradient checkpointing cannot be none.')
-    custom_forward = None
-
-    if mode == 'resnet':
-        def custom_forward(hidden_states, temb):
-            inputs = module(hidden_states, temb)
-            return inputs
-
-    if mode == 'attn':
-        def custom_forward(
-            hidden_states, 
-            encoder_hidden_states=None, 
-            cross_attention_kwargs=None
-        ):
-            inputs = module(
-                hidden_states,
-                encoder_hidden_states,
-                cross_attention_kwargs
-            )
-            return inputs
-
-    if mode == 'temp':
-         def custom_forward(hidden_states, num_frames=None):
-            inputs = use_temporal(module, num_frames, hidden_states)
-            if inputs is None: inputs = module(
-                hidden_states, 
-                num_frames=num_frames
-            )
-            return inputs
-
-    return custom_forward
-
-def transformer_g_c(transformer, sample, num_frames):
-    sample = g_c(custom_checkpoint(transformer, mode='temp'), 
-        sample, num_frames, use_reentrant=False
-    )['sample']
-
-    return sample
-
-def cross_attn_g_c(
-        attn, 
-        temp_attn, 
-        resnet, 
-        temp_conv, 
-        hidden_states, 
-        encoder_hidden_states, 
-        cross_attention_kwargs, 
-        temb, 
-        num_frames,
-        inverse_temp=False
-    ):
-    
-    def ordered_g_c(idx):
-
-        # Self and CrossAttention
-        if idx == 0: return g_c(custom_checkpoint(attn, mode='attn'),
-            hidden_states, encoder_hidden_states,cross_attention_kwargs, use_reentrant=False
-        )['sample']
-
-        # Temporal Self and CrossAttention
-        if idx == 1: return g_c(custom_checkpoint(temp_attn, mode='temp'), 
-            hidden_states, num_frames, use_reentrant=False)['sample']
-
-        # Resnets
-        if idx == 2: return g_c(custom_checkpoint(resnet, mode='resnet'), 
-            hidden_states, temb, use_reentrant=False)
-        
-        # Temporal Convolutions
-        if idx == 3: return g_c(custom_checkpoint(temp_conv, mode='temp'), 
-            hidden_states, num_frames, use_reentrant=False
-    )
-
-    # Here we call the function depending on the order in which they are called. 
-    # For some layers, the orders are different, so we access the appropriate one by index.
-    
-    if not inverse_temp:
-        for idx in [0,1,2,3]: hidden_states = ordered_g_c(idx) 
-    else:
-        for idx in [2,3,0,1]: hidden_states = ordered_g_c(idx)
-
-    return hidden_states
-
-def up_down_g_c(resnet, temp_conv, hidden_states, temb, num_frames):
-    hidden_states = g_c(custom_checkpoint(resnet, mode='resnet'), hidden_states, temb, use_reentrant=False)
-    hidden_states = g_c(custom_checkpoint(temp_conv, mode='temp'), 
-        hidden_states, num_frames,  use_reentrant=False
-    )
-    return hidden_states
-
-def get_down_block(
-    down_block_type,
-    num_layers,
-    in_channels,
-    out_channels,
-    temb_channels,
-    add_downsample,
-    resnet_eps,
-    resnet_act_fn,
-    attn_num_head_channels,
-    resnet_groups=None,
-    cross_attention_dim=None,
-    downsample_padding=None,
-    dual_cross_attention=False,
-    use_linear_projection=True,
-    only_cross_attention=False,
-    upcast_attention=False,
-    resnet_time_scale_shift="default",
-):
-    if down_block_type == "DownBlock3D":
-        return DownBlock3D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    elif down_block_type == "CrossAttnDownBlock3D":
-        if cross_attention_dim is None:
-            raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D")
-        return CrossAttnDownBlock3D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            temb_channels=temb_channels,
-            add_downsample=add_downsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            downsample_padding=downsample_padding,
-            cross_attention_dim=cross_attention_dim,
-            attn_num_head_channels=attn_num_head_channels,
-            dual_cross_attention=dual_cross_attention,
-            use_linear_projection=use_linear_projection,
-            only_cross_attention=only_cross_attention,
-            upcast_attention=upcast_attention,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    raise ValueError(f"{down_block_type} does not exist.")
-
-
-def get_up_block(
-    up_block_type,
-    num_layers,
-    in_channels,
-    out_channels,
-    prev_output_channel,
-    temb_channels,
-    add_upsample,
-    resnet_eps,
-    resnet_act_fn,
-    attn_num_head_channels,
-    resnet_groups=None,
-    cross_attention_dim=None,
-    dual_cross_attention=False,
-    use_linear_projection=True,
-    only_cross_attention=False,
-    upcast_attention=False,
-    resnet_time_scale_shift="default",
-):
-    if up_block_type == "UpBlock3D":
-        return UpBlock3D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    elif up_block_type == "CrossAttnUpBlock3D":
-        if cross_attention_dim is None:
-            raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D")
-        return CrossAttnUpBlock3D(
-            num_layers=num_layers,
-            in_channels=in_channels,
-            out_channels=out_channels,
-            prev_output_channel=prev_output_channel,
-            temb_channels=temb_channels,
-            add_upsample=add_upsample,
-            resnet_eps=resnet_eps,
-            resnet_act_fn=resnet_act_fn,
-            resnet_groups=resnet_groups,
-            cross_attention_dim=cross_attention_dim,
-            attn_num_head_channels=attn_num_head_channels,
-            dual_cross_attention=dual_cross_attention,
-            use_linear_projection=use_linear_projection,
-            only_cross_attention=only_cross_attention,
-            upcast_attention=upcast_attention,
-            resnet_time_scale_shift=resnet_time_scale_shift,
-        )
-    raise ValueError(f"{up_block_type} does not exist.")
-
-
-class UNetMidBlock3DCrossAttn(nn.Module):
-    def __init__(
-        self,
-        in_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,
-        attn_num_head_channels=1,
-        output_scale_factor=1.0,
-        cross_attention_dim=1280,
-        dual_cross_attention=False,
-        use_linear_projection=True,
-        upcast_attention=False,
-    ):
-        super().__init__()
-
-        self.gradient_checkpointing = False
-        self.has_cross_attention = True
-        self.attn_num_head_channels = attn_num_head_channels
-        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
-
-        # there is always at least one resnet
-        resnets = [
-            ResnetBlock2D(
-                in_channels=in_channels,
-                out_channels=in_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 = [
-            TemporalConvLayer(
-                in_channels,
-                in_channels,
-                dropout=0.1
-            )
-        ]
-        attentions = []
-        temp_attentions = []
-
-        for _ in range(num_layers):
-            attentions.append(
-                Transformer2DModel(
-                    in_channels // attn_num_head_channels,
-                    attn_num_head_channels,
-                    in_channels=in_channels,
-                    num_layers=1,
-                    cross_attention_dim=cross_attention_dim,
-                    norm_num_groups=resnet_groups,
-                    use_linear_projection=use_linear_projection,
-                    upcast_attention=upcast_attention,
-                )
-            )
-            temp_attentions.append(
-                TransformerTemporalModel(
-                    in_channels // attn_num_head_channels,
-                    attn_num_head_channels,
-                    in_channels=in_channels,
-                    num_layers=1,
-                    cross_attention_dim=cross_attention_dim,
-                    norm_num_groups=resnet_groups,
-                )
-            )
-            resnets.append(
-                ResnetBlock2D(
-                    in_channels=in_channels,
-                    out_channels=in_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(
-                    in_channels,
-                    in_channels,
-                    dropout=0.1
-                )
-            )
-
-        self.resnets = nn.ModuleList(resnets)
-        self.temp_convs = nn.ModuleList(temp_convs)
-        self.attentions = nn.ModuleList(attentions)
-        self.temp_attentions = nn.ModuleList(temp_attentions)
-
-    def forward(
-        self,
-        hidden_states,
-        temb=None,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        num_frames=1,
-        cross_attention_kwargs=None,
-    ):
-        if self.gradient_checkpointing:
-            hidden_states = up_down_g_c(
-                    self.resnets[0], 
-                    self.temp_convs[0], 
-                    hidden_states, 
-                    temb, 
-                    num_frames
-                )
-        else:
-            hidden_states = self.resnets[0](hidden_states, temb)
-            hidden_states = self.temp_convs[0](hidden_states, num_frames=num_frames)
-            
-        for attn, temp_attn, resnet, temp_conv in zip(
-            self.attentions, self.temp_attentions, self.resnets[1:], self.temp_convs[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
-                    )
-            else:
-                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).sample
-
-                hidden_states = resnet(hidden_states, temb)
-
-                if num_frames > 1:
-                    hidden_states = temp_conv(hidden_states, num_frames=num_frames)
-
-        return hidden_states
-
-
-class CrossAttnDownBlock3D(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,
-        attn_num_head_channels=1,
-        cross_attention_dim=1280,
-        output_scale_factor=1.0,
-        downsample_padding=1,
-        add_downsample=True,
-        dual_cross_attention=False,
-        use_linear_projection=False,
-        only_cross_attention=False,
-        upcast_attention=False,
-    ):
-        super().__init__()
-        resnets = []
-        attentions = []
-        temp_attentions = []
-        temp_convs = []
-
-        self.gradient_checkpointing = False
-        self.has_cross_attention = True
-        self.attn_num_head_channels = attn_num_head_channels
-
-        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
-                )
-            )
-            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,
-                )
-            )
-            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_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 = ()
-
-        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).sample
-
-            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,
-    ):
-        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,
-                )
-            )
-            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
-        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).sample
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states, upsample_size)
-
-        return hidden_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):
-        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)
-
-        if self.upsamplers is not None:
-            for upsampler in self.upsamplers:
-                hidden_states = upsampler(hidden_states, upsample_size)
-
-        return hidden_states