from functools import partial from abc import abstractmethod import torch import torch.nn as nn from einops import rearrange import torch.nn.functional as F from core.models.utils_diffusion import timestep_embedding from core.common import gradient_checkpoint from core.basics import zero_module, conv_nd, linear, avg_pool_nd, normalization from core.modules.attention import SpatialTransformer, TemporalTransformer TASK_IDX_IMAGE = 0 TASK_IDX_RAY = 1 class TimestepBlock(nn.Module): """ Any module where forward() takes timestep embeddings as a second argument. """ @abstractmethod def forward(self, x, emb): """ Apply the module to `x` given `emb` timestep embeddings. """ class TimestepEmbedSequential(nn.Sequential, TimestepBlock): """ A sequential module that passes timestep embeddings to the children that support it as an extra input. """ def forward( self, x, emb, context=None, batch_size=None, with_lora=False, time_steps=None ): for layer in self: if isinstance(layer, TimestepBlock): x = layer(x, emb, batch_size=batch_size) elif isinstance(layer, SpatialTransformer): x = layer(x, context, with_lora=with_lora) elif isinstance(layer, TemporalTransformer): x = rearrange(x, "(b f) c h w -> b c f h w", b=batch_size) x = layer(x, context, with_lora=with_lora, time_steps=time_steps) x = rearrange(x, "b c f h w -> (b f) c h w") else: x = layer(x) return x class Downsample(nn.Module): """ A downsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then downsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims stride = 2 if dims != 3 else (1, 2, 2) if use_conv: self.op = conv_nd( dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, ) else: assert self.channels == self.out_channels self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) def forward(self, x): assert x.shape[1] == self.channels return self.op(x) class Upsample(nn.Module): """ An upsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determining if a convolution is applied. :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then upsampling occurs in the inner-two dimensions. """ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): super().__init__() self.channels = channels self.out_channels = out_channels or channels self.use_conv = use_conv self.dims = dims if use_conv: self.conv = conv_nd( dims, self.channels, self.out_channels, 3, padding=padding ) def forward(self, x): assert x.shape[1] == self.channels if self.dims == 3: x = F.interpolate( x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest" ) else: x = F.interpolate(x, scale_factor=2, mode="nearest") if self.use_conv: x = self.conv(x) return x class ResBlock(TimestepBlock): """ A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: the number of timestep embedding channels. :param dropout: the rate of dropout. :param out_channels: if specified, the number of out channels. :param use_conv: if True and out_channels is specified, use a spatial convolution instead of a smaller 1x1 convolution to change the channels in the skip connection. :param dims: determines if the signal is 1D, 2D, or 3D. :param up: if True, use this block for upsampling. :param down: if True, use this block for downsampling. :param use_temporal_conv: if True, use the temporal convolution. :param use_image_dataset: if True, the temporal parameters will not be optimized. """ def __init__( self, channels, emb_channels, dropout, out_channels=None, use_scale_shift_norm=False, dims=2, use_checkpoint=False, use_conv=False, up=False, down=False, use_temporal_conv=False, tempspatial_aware=False, ): super().__init__() self.channels = channels self.emb_channels = emb_channels self.dropout = dropout self.out_channels = out_channels or channels self.use_conv = use_conv self.use_checkpoint = use_checkpoint self.use_scale_shift_norm = use_scale_shift_norm self.use_temporal_conv = use_temporal_conv self.in_layers = nn.Sequential( normalization(channels), nn.SiLU(), conv_nd(dims, channels, self.out_channels, 3, padding=1), ) self.updown = up or down if up: self.h_upd = Upsample(channels, False, dims) self.x_upd = Upsample(channels, False, dims) elif down: self.h_upd = Downsample(channels, False, dims) self.x_upd = Downsample(channels, False, dims) else: self.h_upd = self.x_upd = nn.Identity() self.emb_layers = nn.Sequential( nn.SiLU(), nn.Linear( emb_channels, 2 * self.out_channels if use_scale_shift_norm else self.out_channels, ), ) self.out_layers = nn.Sequential( normalization(self.out_channels), nn.SiLU(), nn.Dropout(p=dropout), zero_module(nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1)), ) if self.out_channels == channels: self.skip_connection = nn.Identity() elif use_conv: self.skip_connection = conv_nd( dims, channels, self.out_channels, 3, padding=1 ) else: self.skip_connection = conv_nd(dims, channels, self.out_channels, 1) if self.use_temporal_conv: self.temopral_conv = TemporalConvBlock( self.out_channels, self.out_channels, dropout=0.1, spatial_aware=tempspatial_aware, ) def forward(self, x, emb, batch_size=None): """ Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb: an [N x emb_channels] Tensor of timestep embeddings. :return: an [N x C x ...] Tensor of outputs. """ input_tuple = (x, emb) if batch_size: forward_batchsize = partial(self._forward, batch_size=batch_size) return gradient_checkpoint( forward_batchsize, input_tuple, self.parameters(), self.use_checkpoint ) return gradient_checkpoint( self._forward, input_tuple, self.parameters(), self.use_checkpoint ) def _forward(self, x, emb, batch_size=None): if self.updown: in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] h = in_rest(x) h = self.h_upd(h) x = self.x_upd(x) h = in_conv(h) else: h = self.in_layers(x) emb_out = self.emb_layers(emb).type(h.dtype) while len(emb_out.shape) < len(h.shape): emb_out = emb_out[..., None] if self.use_scale_shift_norm: out_norm, out_rest = self.out_layers[0], self.out_layers[1:] scale, shift = torch.chunk(emb_out, 2, dim=1) h = out_norm(h) * (1 + scale) + shift h = out_rest(h) else: h = h + emb_out h = self.out_layers(h) h = self.skip_connection(x) + h if self.use_temporal_conv and batch_size: h = rearrange(h, "(b t) c h w -> b c t h w", b=batch_size) h = self.temopral_conv(h) h = rearrange(h, "b c t h w -> (b t) c h w") return h class TemporalConvBlock(nn.Module): def __init__( self, in_channels, out_channels=None, dropout=0.0, spatial_aware=False ): super(TemporalConvBlock, self).__init__() if out_channels is None: out_channels = in_channels self.in_channels = in_channels self.out_channels = out_channels th_kernel_shape = (3, 1, 1) if not spatial_aware else (3, 3, 1) th_padding_shape = (1, 0, 0) if not spatial_aware else (1, 1, 0) tw_kernel_shape = (3, 1, 1) if not spatial_aware else (3, 1, 3) tw_padding_shape = (1, 0, 0) if not spatial_aware else (1, 0, 1) # conv layers self.conv1 = nn.Sequential( nn.GroupNorm(32, in_channels), nn.SiLU(), nn.Conv3d( in_channels, out_channels, th_kernel_shape, padding=th_padding_shape ), ) self.conv2 = nn.Sequential( nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout), nn.Conv3d( out_channels, in_channels, tw_kernel_shape, padding=tw_padding_shape ), ) self.conv3 = nn.Sequential( nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout), nn.Conv3d( out_channels, in_channels, th_kernel_shape, padding=th_padding_shape ), ) self.conv4 = nn.Sequential( nn.GroupNorm(32, out_channels), nn.SiLU(), nn.Dropout(dropout), nn.Conv3d( out_channels, in_channels, tw_kernel_shape, padding=tw_padding_shape ), ) # zero out the last layer params,so the conv block is identity nn.init.zeros_(self.conv4[-1].weight) nn.init.zeros_(self.conv4[-1].bias) def forward(self, x): identity = x x = self.conv1(x) x = self.conv2(x) x = self.conv3(x) x = self.conv4(x) return identity + x class UNetModel(nn.Module): """ The full UNet model with attention and timestep embedding. :param in_channels: in_channels in the input Tensor. :param model_channels: base channel count for the model. :param out_channels: channels in the output Tensor. :param num_res_blocks: number of residual blocks per downsample. :param attention_resolutions: a collection of downsample rates at which attention will take place. May be a set, list, or tuple. For example, if this contains 4, then at 4x downsampling, attention will be used. :param dropout: the dropout probability. :param channel_mult: channel multiplier for each level of the UNet. :param conv_resample: if True, use learned convolutions for upsampling and downsampling. :param dims: determines if the signal is 1D, 2D, or 3D. :param num_classes: if specified (as an int), then this model will be class-conditional with `num_classes` classes. :param use_checkpoint: use gradient checkpointing to reduce memory usage. :param num_heads: the number of attention heads in each attention layer. :param num_heads_channels: if specified, ignore num_heads and instead use a fixed channel width per attention head. :param num_heads_upsample: works with num_heads to set a different number of heads for upsampling. Deprecated. :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. :param resblock_updown: use residual blocks for up/downsampling. :param use_new_attention_order: use a different attention pattern for potentially increased efficiency. """ def __init__( self, in_channels, model_channels, out_channels, num_res_blocks, attention_resolutions, dropout=0.0, channel_mult=(1, 2, 4, 8), conv_resample=True, dims=2, context_dim=None, use_scale_shift_norm=False, resblock_updown=False, num_heads=-1, num_head_channels=-1, transformer_depth=1, use_linear=False, use_checkpoint=False, temporal_conv=False, tempspatial_aware=False, temporal_attention=True, use_relative_position=True, use_causal_attention=False, temporal_length=None, use_fp16=False, addition_attention=False, temporal_selfatt_only=True, image_cross_attention=False, image_cross_attention_scale_learnable=False, default_fs=4, fs_condition=False, use_spatial_temporal_attention=False, # >>> Extra Ray Options use_addition_ray_output_head=False, ray_channels=6, use_lora_for_rays_in_output_blocks=False, use_task_embedding=False, use_ray_decoder=False, use_ray_decoder_residual=False, full_spatial_temporal_attention=False, enhance_multi_view_correspondence=False, camera_pose_condition=False, use_feature_alignment=False, ): super(UNetModel, self).__init__() if num_heads == -1: assert ( num_head_channels != -1 ), "Either num_heads or num_head_channels has to be set" if num_head_channels == -1: assert ( num_heads != -1 ), "Either num_heads or num_head_channels has to be set" self.in_channels = in_channels self.model_channels = model_channels self.out_channels = out_channels self.num_res_blocks = num_res_blocks self.attention_resolutions = attention_resolutions self.dropout = dropout self.channel_mult = channel_mult self.conv_resample = conv_resample self.temporal_attention = temporal_attention time_embed_dim = model_channels * 4 self.use_checkpoint = use_checkpoint self.dtype = torch.float16 if use_fp16 else torch.float32 temporal_self_att_only = True self.addition_attention = addition_attention self.temporal_length = temporal_length self.image_cross_attention = image_cross_attention self.image_cross_attention_scale_learnable = ( image_cross_attention_scale_learnable ) self.default_fs = default_fs self.fs_condition = fs_condition self.use_spatial_temporal_attention = use_spatial_temporal_attention # >>> Extra Ray Options self.use_addition_ray_output_head = use_addition_ray_output_head self.use_lora_for_rays_in_output_blocks = use_lora_for_rays_in_output_blocks if self.use_lora_for_rays_in_output_blocks: assert ( use_addition_ray_output_head ), "`use_addition_ray_output_head` is required to be True when using LoRA for rays in output blocks." assert ( not use_task_embedding ), "`use_task_embedding` cannot be True when `use_lora_for_rays_in_output_blocks` is enabled." if self.use_addition_ray_output_head: print("Using additional ray output head...") assert (self.out_channels == 4) or ( 4 + ray_channels == self.out_channels ), f"`out_channels`={out_channels} is invalid." self.out_channels = 4 out_channels = 4 self.ray_channels = ray_channels self.use_ray_decoder = use_ray_decoder if use_ray_decoder: assert ( not use_task_embedding ), "`use_task_embedding` cannot be True when `use_ray_decoder_layers` is enabled." assert ( use_addition_ray_output_head ), "`use_addition_ray_output_head` must be True when `use_ray_decoder_layers` is enabled." self.use_ray_decoder_residual = use_ray_decoder_residual # >>> Time/Task Embedding Blocks self.time_embed = nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) if fs_condition: self.fps_embedding = nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) nn.init.zeros_(self.fps_embedding[-1].weight) nn.init.zeros_(self.fps_embedding[-1].bias) if camera_pose_condition: self.camera_pose_condition = True self.camera_pose_embedding = nn.Sequential( linear(12, model_channels), nn.SiLU(), linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) nn.init.zeros_(self.camera_pose_embedding[-1].weight) nn.init.zeros_(self.camera_pose_embedding[-1].bias) self.use_task_embedding = use_task_embedding if use_task_embedding: assert ( not use_lora_for_rays_in_output_blocks ), "`use_lora_for_rays_in_output_blocks` and `use_task_embedding` cannot be True at the same time." assert ( use_addition_ray_output_head ), "`use_addition_ray_output_head` is required to be True when `use_task_embedding` is enabled." self.task_embedding = nn.Sequential( linear(model_channels, time_embed_dim), nn.SiLU(), linear(time_embed_dim, time_embed_dim), ) nn.init.zeros_(self.task_embedding[-1].weight) nn.init.zeros_(self.task_embedding[-1].bias) self.task_parameters = nn.ParameterList( [ nn.Parameter( torch.zeros(size=[model_channels], requires_grad=True) ), nn.Parameter( torch.zeros(size=[model_channels], requires_grad=True) ), ] ) # >>> Input Block self.input_blocks = nn.ModuleList( [ TimestepEmbedSequential( conv_nd(dims, in_channels, model_channels, 3, padding=1) ) ] ) if self.addition_attention: self.init_attn = TimestepEmbedSequential( TemporalTransformer( model_channels, n_heads=8, d_head=num_head_channels, depth=transformer_depth, context_dim=context_dim, use_checkpoint=use_checkpoint, only_self_att=temporal_selfatt_only, causal_attention=False, relative_position=use_relative_position, temporal_length=temporal_length, ) ) input_block_chans = [model_channels] ch = model_channels ds = 1 for level, mult in enumerate(channel_mult): for _ in range(num_res_blocks): layers = [ ResBlock( ch, time_embed_dim, dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, use_temporal_conv=temporal_conv, ) ] ch = mult * model_channels if ds in attention_resolutions: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels layers.append( SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, use_checkpoint=use_checkpoint, disable_self_attn=False, video_length=temporal_length, image_cross_attention=self.image_cross_attention, image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable, ) ) if self.temporal_attention: layers.append( TemporalTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only, causal_attention=use_causal_attention, relative_position=use_relative_position, temporal_length=temporal_length, ) ) self.input_blocks.append(TimestepEmbedSequential(*layers)) input_block_chans.append(ch) if level != len(channel_mult) - 1: out_ch = ch self.input_blocks.append( TimestepEmbedSequential( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, down=True, ) if resblock_updown else Downsample( ch, conv_resample, dims=dims, out_channels=out_ch ) ) ) ch = out_ch input_block_chans.append(ch) ds *= 2 if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels layers = [ ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, use_temporal_conv=temporal_conv, ), SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, use_checkpoint=use_checkpoint, disable_self_attn=False, video_length=temporal_length, image_cross_attention=self.image_cross_attention, image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable, ), ] if self.temporal_attention: layers.append( TemporalTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only, causal_attention=use_causal_attention, relative_position=use_relative_position, temporal_length=temporal_length, ) ) layers.append( ResBlock( ch, time_embed_dim, dropout, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, use_temporal_conv=temporal_conv, ) ) # >>> Middle Block self.middle_block = TimestepEmbedSequential(*layers) # >>> Ray Decoder if use_ray_decoder: self.ray_decoder_blocks = nn.ModuleList([]) # >>> Output Block is_first_layer = True self.output_blocks = nn.ModuleList([]) for level, mult in list(enumerate(channel_mult))[::-1]: for i in range(num_res_blocks + 1): ich = input_block_chans.pop() layers = [ ResBlock( ch + ich, time_embed_dim, dropout, out_channels=mult * model_channels, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, use_temporal_conv=temporal_conv, ) ] if use_ray_decoder: if self.use_ray_decoder_residual: ray_residual_ch = ich else: ray_residual_ch = 0 ray_decoder_layers = [ ResBlock( (ch if is_first_layer else (ch // 10)) + ray_residual_ch, time_embed_dim, dropout, out_channels=mult * model_channels // 10, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, tempspatial_aware=tempspatial_aware, use_temporal_conv=True, ) ] is_first_layer = False ch = model_channels * mult if ds in attention_resolutions: if num_head_channels == -1: dim_head = ch // num_heads else: num_heads = ch // num_head_channels dim_head = num_head_channels layers.append( SpatialTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, use_checkpoint=use_checkpoint, disable_self_attn=False, video_length=temporal_length, image_cross_attention=self.image_cross_attention, image_cross_attention_scale_learnable=self.image_cross_attention_scale_learnable, enable_lora=self.use_lora_for_rays_in_output_blocks, ) ) if self.temporal_attention: layers.append( TemporalTransformer( ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim, use_linear=use_linear, use_checkpoint=use_checkpoint, only_self_att=temporal_self_att_only, causal_attention=use_causal_attention, relative_position=use_relative_position, temporal_length=temporal_length, use_extra_spatial_temporal_self_attention=use_spatial_temporal_attention, enable_lora=self.use_lora_for_rays_in_output_blocks, full_spatial_temporal_attention=full_spatial_temporal_attention, enhance_multi_view_correspondence=enhance_multi_view_correspondence, ) ) if level and i == num_res_blocks: out_ch = ch # out_ray_ch = ray_ch layers.append( ResBlock( ch, time_embed_dim, dropout, out_channels=out_ch, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, up=True, ) if resblock_updown else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch) ) if use_ray_decoder: ray_decoder_layers.append( ResBlock( ch // 10, time_embed_dim, dropout, out_channels=out_ch // 10, dims=dims, use_checkpoint=use_checkpoint, use_scale_shift_norm=use_scale_shift_norm, up=True, ) if resblock_updown else Upsample( ch // 10, conv_resample, dims=dims, out_channels=out_ch // 10, ) ) ds //= 2 self.output_blocks.append(TimestepEmbedSequential(*layers)) if use_ray_decoder: self.ray_decoder_blocks.append( TimestepEmbedSequential(*ray_decoder_layers) ) self.out = nn.Sequential( normalization(ch), nn.SiLU(), zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)), ) if self.use_addition_ray_output_head: ray_model_channels = model_channels // 10 self.ray_output_head = nn.Sequential( normalization(ray_model_channels), nn.SiLU(), conv_nd(dims, ray_model_channels, ray_model_channels, 3, padding=1), nn.SiLU(), conv_nd(dims, ray_model_channels, ray_model_channels, 3, padding=1), nn.SiLU(), zero_module( conv_nd(dims, ray_model_channels, self.ray_channels, 3, padding=1) ), ) self.use_feature_alignment = use_feature_alignment if self.use_feature_alignment: self.feature_alignment_adapter = FeatureAlignmentAdapter( time_embed_dim=time_embed_dim, use_checkpoint=use_checkpoint ) def forward( self, x, time_steps, context=None, features_adapter=None, fs=None, task_idx=None, camera_poses=None, return_input_block_features=False, return_middle_feature=False, return_output_block_features=False, **kwargs, ): intermediate_features = {} if return_input_block_features: intermediate_features["input"] = [] if return_output_block_features: intermediate_features["output"] = [] b, t, _, _, _ = x.shape t_emb = timestep_embedding( time_steps, self.model_channels, repeat_only=False ).type(x.dtype) emb = self.time_embed(t_emb) # repeat t times for context [(b t) 77 768] & time embedding # check if we use per-frame image conditioning _, l_context, _ = context.shape if l_context == 77 + t * 16: # !!! HARD CODE here context_text, context_img = context[:, :77, :], context[:, 77:, :] context_text = context_text.repeat_interleave(repeats=t, dim=0) context_img = rearrange(context_img, "b (t l) c -> (b t) l c", t=t) context = torch.cat([context_text, context_img], dim=1) else: context = context.repeat_interleave(repeats=t, dim=0) emb = emb.repeat_interleave(repeats=t, dim=0) # always in shape (b t) c h w, except for temporal layer x = rearrange(x, "b t c h w -> (b t) c h w") # combine emb if self.fs_condition: if fs is None: fs = torch.tensor( [self.default_fs] * b, dtype=torch.long, device=x.device ) fs_emb = timestep_embedding( fs, self.model_channels, repeat_only=False ).type(x.dtype) fs_embed = self.fps_embedding(fs_emb) fs_embed = fs_embed.repeat_interleave(repeats=t, dim=0) emb = emb + fs_embed if self.camera_pose_condition: # camera_poses: (b, t, 12) camera_poses = rearrange(camera_poses, "b t x y -> (b t) (x y)") # x=3, y=4 camera_poses_embed = self.camera_pose_embedding(camera_poses) emb = emb + camera_poses_embed if self.use_task_embedding: assert ( task_idx is not None ), "`task_idx` should not be None when `use_task_embedding` is enabled." task_embed = self.task_embedding( self.task_parameters[task_idx] .reshape(1, self.model_channels) .repeat(b, 1) ) task_embed = task_embed.repeat_interleave(repeats=t, dim=0) emb = emb + task_embed h = x.type(self.dtype) adapter_idx = 0 hs = [] for _id, module in enumerate(self.input_blocks): h = module(h, emb, context=context, batch_size=b) if _id == 0 and self.addition_attention: h = self.init_attn(h, emb, context=context, batch_size=b) # plug-in adapter features if ((_id + 1) % 3 == 0) and features_adapter is not None: h = h + features_adapter[adapter_idx] adapter_idx += 1 hs.append(h) if return_input_block_features: intermediate_features["input"].append(h) if features_adapter is not None: assert len(features_adapter) == adapter_idx, "Wrong features_adapter" h = self.middle_block(h, emb, context=context, batch_size=b) if return_middle_feature: intermediate_features["middle"] = h if self.use_feature_alignment: feature_alignment_output = self.feature_alignment_adapter( hs[2], hs[5], hs[8], emb=emb ) # >>> Output Blocks Forward if self.use_ray_decoder: h_original = h h_ray = h for original_module, ray_module in zip( self.output_blocks, self.ray_decoder_blocks ): cur_hs = hs.pop() h_original = torch.cat([h_original, cur_hs], dim=1) h_original = original_module( h_original, emb, context=context, batch_size=b, time_steps=time_steps, ) if self.use_ray_decoder_residual: h_ray = torch.cat([h_ray, cur_hs], dim=1) h_ray = ray_module(h_ray, emb, context=context, batch_size=b) if return_output_block_features: print( "return_output_block_features: h_original.shape=", h_original.shape, ) intermediate_features["output"].append(h_original.detach()) h_original = h_original.type(x.dtype) h_ray = h_ray.type(x.dtype) y_original = self.out(h_original) y_ray = self.ray_output_head(h_ray) y = torch.cat([y_original, y_ray], dim=1) else: if self.use_lora_for_rays_in_output_blocks: middle_h = h h_original = middle_h h_lora = middle_h for output_idx, module in enumerate(self.output_blocks): cur_hs = hs.pop() h_original = torch.cat([h_original, cur_hs], dim=1) h_original = module( h_original, emb, context=context, batch_size=b, with_lora=False ) h_lora = torch.cat([h_lora, cur_hs], dim=1) h_lora = module( h_lora, emb, context=context, batch_size=b, with_lora=True ) h_original = h_original.type(x.dtype) h_lora = h_lora.type(x.dtype) y_original = self.out(h_original) y_lora = self.ray_output_head(h_lora) y = torch.cat([y_original, y_lora], dim=1) else: for module in self.output_blocks: h = torch.cat([h, hs.pop()], dim=1) h = module(h, emb, context=context, batch_size=b) h = h.type(x.dtype) if self.use_task_embedding: # Seperated Input (Branch Control in CPU) # Serial Execution (GPU Vectorization Pending) if task_idx == TASK_IDX_IMAGE: y = self.out(h) elif task_idx == TASK_IDX_RAY: y = self.ray_output_head(h) else: raise NotImplementedError(f"Unsupported `task_idx`: {task_idx}") else: # Output ray and images at the same forward y = self.out(h) if self.use_addition_ray_output_head: y_ray = self.ray_output_head(h) y = torch.cat([y, y_ray], dim=1) # reshape back to (b c t h w) y = rearrange(y, "(b t) c h w -> b t c h w", b=b) if ( return_input_block_features or return_output_block_features or return_middle_feature ): return y, intermediate_features # Assume intermediate features are only request during non-training scenarios (e.g., feature visualization) if self.use_feature_alignment: return y, feature_alignment_output return y class FeatureAlignmentAdapter(torch.nn.Module): def __init__(self, time_embed_dim, use_checkpoint, dropout=0.0, *args, **kwargs): super().__init__(*args, **kwargs) self.channel_adapter_conv_16 = torch.nn.Conv2d( in_channels=1280, out_channels=320, kernel_size=1 ) self.channel_adapter_conv_32 = torch.nn.Conv2d( in_channels=640, out_channels=320, kernel_size=1 ) self.upsampler_x2 = torch.nn.UpsamplingBilinear2d(scale_factor=2) self.upsampler_x4 = torch.nn.UpsamplingBilinear2d(scale_factor=4) self.res_block = ResBlock( 320 * 3, time_embed_dim, dropout, out_channels=32 * 3, dims=2, use_checkpoint=use_checkpoint, use_scale_shift_norm=False, ) self.final_conv = conv_nd( dims=2, in_channels=32 * 3, out_channels=6, kernel_size=1 ) def forward(self, feature_64, feature_32, feature_16, emb): feature_16_adapted = self.channel_adapter_conv_16(feature_16) feature_32_adapted = self.channel_adapter_conv_32(feature_32) feature_16_upsampled = self.upsampler_x4(feature_16_adapted) feature_32_upsampled = self.upsampler_x2(feature_32_adapted) feature_all = torch.concat( [feature_16_upsampled, feature_32_upsampled, feature_64], dim=1 ) # bt, 3, h, w return self.final_conv(self.res_block(feature_all, emb=emb))