from typing import List, Optional, Tuple, Union import torch from dataclasses import dataclass from typing import Optional, Tuple, Union import torch import torch.nn as nn from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.utils import BaseOutput from diffusers.models.embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps from diffusers.models.modeling_utils import ModelMixin from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2D, get_down_block, get_up_block @dataclass class UNet2DOutput(BaseOutput): """ The output of [`UNet2DModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): The hidden states output from the last layer of the model. """ sample: torch.FloatTensor class UNet2DModel(ModelMixin, ConfigMixin): r""" A 2D UNet model that takes a noisy sample and a timestep and returns a sample shaped output. This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented for all models (such as downloading or saving). Parameters: sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): Height and width of input/output sample. Dimensions must be a multiple of `2 ** (len(block_out_channels) - 1)`. in_channels (`int`, *optional*, defaults to 3): Number of channels in the input sample. out_channels (`int`, *optional*, defaults to 3): Number of channels in the output. center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. time_embedding_type (`str`, *optional*, defaults to `"positional"`): Type of time embedding to use. freq_shift (`int`, *optional*, defaults to 0): Frequency shift for Fourier time embedding. flip_sin_to_cos (`bool`, *optional*, defaults to `True`): Whether to flip sin to cos for Fourier time embedding. down_block_types (`Tuple[str]`, *optional*, defaults to `("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")`): Tuple of downsample block types. mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2D"`): Block type for middle of UNet, it can be either `UNetMidBlock2D` or `UnCLIPUNetMidBlock2D`. up_block_types (`Tuple[str]`, *optional*, defaults to `("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")`): Tuple of upsample block types. block_out_channels (`Tuple[int]`, *optional*, defaults to `(224, 448, 672, 896)`): Tuple of block output channels. layers_per_block (`int`, *optional*, defaults to `2`): The number of layers per block. mid_block_scale_factor (`float`, *optional*, defaults to `1`): The scale factor for the mid block. downsample_padding (`int`, *optional*, defaults to `1`): The padding for the downsample convolution. downsample_type (`str`, *optional*, defaults to `conv`): The downsample type for downsampling layers. Choose between "conv" and "resnet" upsample_type (`str`, *optional*, defaults to `conv`): The upsample type for upsampling layers. Choose between "conv" and "resnet" dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension. norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for normalization. attn_norm_num_groups (`int`, *optional*, defaults to `None`): If set to an integer, a group norm layer will be created in the mid block's [`Attention`] layer with the given number of groups. If left as `None`, the group norm layer will only be created if `resnet_time_scale_shift` is set to `default`, and if created will have `norm_num_groups` groups. norm_eps (`float`, *optional*, defaults to `1e-5`): The epsilon for normalization. resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`. class_embed_type (`str`, *optional*, defaults to `None`): The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, `"timestep"`, or `"identity"`. num_class_embeds (`int`, *optional*, defaults to `None`): Input dimension of the learnable embedding matrix to be projected to `time_embed_dim` when performing class conditioning with `class_embed_type` equal to `None`. """ @register_to_config def __init__( self, sample_size: Optional[Union[int, Tuple[int, int]]] = None, in_channels: int = 3, out_channels: int = 3, center_input_sample: bool = False, time_embedding_type: str = "positional", freq_shift: int = 0, flip_sin_to_cos: bool = True, down_block_types: Tuple[str, ...] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"), up_block_types: Tuple[str, ...] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"), block_out_channels: Tuple[int, ...] = (224, 448, 672, 896), layers_per_block: int = 2, mid_block_scale_factor: float = 1, downsample_padding: int = 1, downsample_type: str = "conv", upsample_type: str = "conv", dropout: float = 0.0, act_fn: str = "silu", attention_head_dim: Optional[int] = 8, norm_num_groups: int = 32, attn_norm_num_groups: Optional[int] = None, norm_eps: float = 1e-5, resnet_time_scale_shift: str = "default", add_attention: bool = True, class_embed_type: Optional[str] = None, num_class_embeds: Optional[int] = None, num_train_timesteps: Optional[int] = None, set_W_to_weight: Optional[bool] = True ): super().__init__() self.sample_size = sample_size time_embed_dim = block_out_channels[0] * 4 # Check inputs if len(down_block_types) != len(up_block_types): raise ValueError( f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." ) if len(block_out_channels) != len(down_block_types): raise ValueError( f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." ) # input self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)) # time if time_embedding_type == "fourier": self.time_proj = GaussianFourierProjection(embedding_size=block_out_channels[0], scale=16, set_W_to_weight=set_W_to_weight) timestep_input_dim = 2 * block_out_channels[0] elif time_embedding_type == "positional": self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) timestep_input_dim = block_out_channels[0] elif time_embedding_type == "learned": self.time_proj = nn.Embedding(num_train_timesteps, block_out_channels[0]) timestep_input_dim = block_out_channels[0] self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) # class embedding if class_embed_type is None and num_class_embeds is not None: self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) elif class_embed_type == "timestep": self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) elif class_embed_type == "identity": self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) else: self.class_embedding = None self.down_blocks = nn.ModuleList([]) self.mid_block = None self.up_blocks = nn.ModuleList([]) # down output_channel = block_out_channels[0] for i, down_block_type in enumerate(down_block_types): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 down_block = get_down_block( down_block_type, num_layers=layers_per_block, in_channels=input_channel, out_channels=output_channel, temb_channels=time_embed_dim, add_downsample=not is_final_block, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel, downsample_padding=downsample_padding, resnet_time_scale_shift=resnet_time_scale_shift, downsample_type=downsample_type, dropout=dropout, ) self.down_blocks.append(down_block) # mid self.mid_block = UNetMidBlock2D( in_channels=block_out_channels[-1], temb_channels=time_embed_dim, dropout=dropout, resnet_eps=norm_eps, resnet_act_fn=act_fn, output_scale_factor=mid_block_scale_factor, resnet_time_scale_shift=resnet_time_scale_shift, attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1], resnet_groups=norm_num_groups, attn_groups=attn_norm_num_groups, add_attention=add_attention, ) # up reversed_block_out_channels = list(reversed(block_out_channels)) output_channel = reversed_block_out_channels[0] for i, up_block_type in enumerate(up_block_types): prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] is_final_block = i == len(block_out_channels) - 1 up_block = get_up_block( up_block_type, num_layers=layers_per_block + 1, in_channels=input_channel, out_channels=output_channel, prev_output_channel=prev_output_channel, temb_channels=time_embed_dim, add_upsample=not is_final_block, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel, resnet_time_scale_shift=resnet_time_scale_shift, upsample_type=upsample_type, dropout=dropout, ) self.up_blocks.append(up_block) prev_output_channel = output_channel # out num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32) self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=norm_eps) self.conv_act = nn.SiLU() self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1) def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], class_labels: Optional[torch.Tensor] = None, return_dict: bool = True, ) -> Union[UNet2DOutput, Tuple]: r""" The [`UNet2DModel`] forward method. Args: sample (`torch.FloatTensor`): The noisy input tensor with the following shape `(batch, channel, height, width)`. timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. class_labels (`torch.FloatTensor`, *optional*, defaults to `None`): Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.unet_2d.UNet2DOutput`] instead of a plain tuple. Returns: [`~models.unet_2d.UNet2DOutput`] or `tuple`: If `return_dict` is True, an [`~models.unet_2d.UNet2DOutput`] is returned, otherwise a `tuple` is returned where the first element is the sample tensor. """ # 0. center input if necessary if self.config.center_input_sample: sample = 2 * sample - 1.0 # 1. time timesteps = timestep if not torch.is_tensor(timesteps): timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device) elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0: timesteps = timesteps[None].to(sample.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timesteps = timesteps * torch.ones(sample.shape[0], dtype=timesteps.dtype, device=timesteps.device) t_emb = self.time_proj(timesteps) # timesteps does not contain any weights and will always return f32 tensors # but time_embedding might actually be running in fp16. so we need to cast here. # there might be better ways to encapsulate this. t_emb = t_emb.to(dtype=self.dtype) emb = self.time_embedding(t_emb) if self.class_embedding is not None: if class_labels is None: raise ValueError("class_labels should be provided when doing class conditioning") if self.config.class_embed_type == "timestep": class_labels = self.time_proj(class_labels) class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) emb = emb + class_emb elif self.class_embedding is None and class_labels is not None: raise ValueError("class_embedding needs to be initialized in order to use class conditioning") # 2. pre-process skip_sample = sample sample = self.conv_in(sample) # 3. down down_block_res_samples = (sample,) for downsample_block in self.down_blocks: if hasattr(downsample_block, "skip_conv"): sample, res_samples, skip_sample = downsample_block( hidden_states=sample, temb=emb, skip_sample=skip_sample ) else: sample, res_samples = downsample_block(hidden_states=sample, temb=emb) down_block_res_samples += res_samples # 4. mid sample = self.mid_block(sample, emb) # 5. up skip_sample = None for upsample_block in self.up_blocks: res_samples = down_block_res_samples[-len(upsample_block.resnets) :] down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] if hasattr(upsample_block, "skip_conv"): sample, skip_sample = upsample_block(sample, res_samples, emb, skip_sample) else: sample = upsample_block(sample, res_samples, emb) # 6. post-process sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) if skip_sample is not None: sample += skip_sample if self.config.time_embedding_type == "fourier": timesteps = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:])))) sample = sample / timesteps if not return_dict: return (sample,) return UNet2DOutput(sample=sample) NUM_CLASSES_FLOOR_HUE = 10 NUM_CLASSES_OBJECT_HUE = 10 NUM_CLASSES_ORIENTATION = 15 NUM_CLASSES_SCALE = 8 NUM_CLASSES_SHAPE = 4 NUM_CLASSES_WALL_HUE = 10 class ClassConditionedUnetForShapes3D(ModelMixin, ConfigMixin): @register_to_config def __init__(self, num_classes_floor_hue=NUM_CLASSES_FLOOR_HUE + 1, num_classes_object_hue=NUM_CLASSES_OBJECT_HUE + 1, num_classes_orientation=NUM_CLASSES_ORIENTATION + 1, num_classes_scale=NUM_CLASSES_SCALE + 1, num_classes_shape=NUM_CLASSES_SHAPE + 1, num_classes_wall_hue=NUM_CLASSES_WALL_HUE + 1, sample_size: Optional[Union[int, Tuple[int, int]]] = None, in_channels: int = 3, out_channels: int = 3, center_input_sample: bool = False, time_embedding_type: str = "positional", freq_shift: int = 0, flip_sin_to_cos: bool = True, down_block_types: Tuple[str, ...] = ( "DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"), up_block_types: Tuple[str, ...] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"), block_out_channels: Tuple[int, ...] = (224, 448, 672, 896), layers_per_block: int = 2, mid_block_scale_factor: float = 1, downsample_padding: int = 1, downsample_type: str = "conv", upsample_type: str = "conv", dropout: float = 0.0, act_fn: str = "silu", attention_head_dim: Optional[int] = 8, norm_num_groups: int = 32, attn_norm_num_groups: Optional[int] = None, norm_eps: float = 1e-5, resnet_time_scale_shift: str = "default", add_attention: bool = True, class_embed_type: Optional[str] = None, num_class_embeds: Optional[int] = None, num_train_timesteps: Optional[int] = None, set_W_to_weight: Optional[bool] = True ): super().__init__() self.class_floor_hue = nn.Embedding(num_classes_floor_hue, num_classes_floor_hue) self.class_object_hue = nn.Embedding(num_classes_object_hue, num_classes_object_hue) self.class_orientation = nn.Embedding(num_classes_orientation, num_classes_orientation) self.class_scale = nn.Embedding(num_classes_scale, num_classes_scale) self.class_shape = nn.Embedding(num_classes_shape, num_classes_shape) self.class_wall_hue = nn.Embedding(num_classes_wall_hue, num_classes_wall_hue) self.model = UNet2DModel( sample_size=sample_size, in_channels=in_channels, out_channels=out_channels, center_input_sample=center_input_sample, time_embedding_type=time_embedding_type, freq_shift=freq_shift, flip_sin_to_cos=flip_sin_to_cos, down_block_types=down_block_types, up_block_types=up_block_types, block_out_channels=block_out_channels, layers_per_block=layers_per_block, mid_block_scale_factor=mid_block_scale_factor, downsample_padding=downsample_padding, downsample_type=downsample_type, upsample_type=upsample_type, dropout=dropout, act_fn=act_fn, attention_head_dim=attention_head_dim, norm_num_groups=norm_num_groups, attn_norm_num_groups=attn_norm_num_groups, norm_eps=norm_eps, resnet_time_scale_shift=resnet_time_scale_shift, add_attention=add_attention, class_embed_type=class_embed_type, num_class_embeds=num_class_embeds, num_train_timesteps=num_train_timesteps, set_W_to_weight=set_W_to_weight ) def forward(self, x, t, class_labels): bs, ch, w, h = x.shape class_cond_floor_hue = self.class_floor_hue(class_labels[:, 0]) class_cond_floor_hue = class_cond_floor_hue.view(bs, class_cond_floor_hue.shape[1], 1, 1).expand(bs, class_cond_floor_hue.shape[1], w, h) class_cond_object_hue = self.class_object_hue(class_labels[:, 1]) class_cond_object_hue = class_cond_object_hue.view(bs, class_cond_object_hue.shape[1], 1, 1).expand(bs, class_cond_object_hue.shape[1], w, h) class_cond_orientation = self.class_orientation(class_labels[:, 2]) class_cond_orientation = class_cond_orientation.view(bs, class_cond_orientation.shape[1], 1, 1).expand(bs, class_cond_orientation.shape[1], w, h) class_cond_scale = self.class_scale(class_labels[:, 3]) class_cond_scale = class_cond_scale.view(bs, class_cond_scale.shape[1], 1, 1).expand(bs, class_cond_scale.shape[1], w, h) class_cond_shape = self.class_shape(class_labels[:, 4]) class_cond_shape = class_cond_shape.view(bs, class_cond_shape.shape[1], 1, 1).expand(bs, class_cond_shape.shape[1], w, h) class_cond_wall_hue = self.class_wall_hue(class_labels[:, 5]) class_cond_wall_hue = class_cond_wall_hue.view(bs, class_cond_wall_hue.shape[1], 1, 1).expand(bs, class_cond_wall_hue.shape[1], w, h) net_input = torch.cat([x, class_cond_floor_hue, class_cond_object_hue, class_cond_orientation, class_cond_scale, class_cond_shape, class_cond_wall_hue], dim=1) return self.model(net_input, t) class MultiLabelConditionalUNet2DModelForShapes3D(ModelMixin, ConfigMixin): r""" A 2D UNet model that takes a noisy sample and a timestep and returns a sample shaped output. This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented for all models (such as downloading or saving). Parameters: sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): Height and width of input/output sample. Dimensions must be a multiple of `2 ** (len(block_out_channels) - 1)`. in_channels (`int`, *optional*, defaults to 3): Number of channels in the input sample. out_channels (`int`, *optional*, defaults to 3): Number of channels in the output. center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. time_embedding_type (`str`, *optional*, defaults to `"positional"`): Type of time embedding to use. freq_shift (`int`, *optional*, defaults to 0): Frequency shift for Fourier time embedding. flip_sin_to_cos (`bool`, *optional*, defaults to `True`): Whether to flip sin to cos for Fourier time embedding. down_block_types (`Tuple[str]`, *optional*, defaults to `("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")`): Tuple of downsample block types. mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2D"`): Block type for middle of UNet, it can be either `UNetMidBlock2D` or `UnCLIPUNetMidBlock2D`. up_block_types (`Tuple[str]`, *optional*, defaults to `("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")`): Tuple of upsample block types. block_out_channels (`Tuple[int]`, *optional*, defaults to `(224, 448, 672, 896)`): Tuple of block output channels. layers_per_block (`int`, *optional*, defaults to `2`): The number of layers per block. mid_block_scale_factor (`float`, *optional*, defaults to `1`): The scale factor for the mid block. downsample_padding (`int`, *optional*, defaults to `1`): The padding for the downsample convolution. downsample_type (`str`, *optional*, defaults to `conv`): The downsample type for downsampling layers. Choose between "conv" and "resnet" upsample_type (`str`, *optional*, defaults to `conv`): The upsample type for upsampling layers. Choose between "conv" and "resnet" dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. attention_head_dim (`int`, *optional*, defaults to `8`): The attention head dimension. norm_num_groups (`int`, *optional*, defaults to `32`): The number of groups for normalization. attn_norm_num_groups (`int`, *optional*, defaults to `None`): If set to an integer, a group norm layer will be created in the mid block's [`Attention`] layer with the given number of groups. If left as `None`, the group norm layer will only be created if `resnet_time_scale_shift` is set to `default`, and if created will have `norm_num_groups` groups. norm_eps (`float`, *optional*, defaults to `1e-5`): The epsilon for normalization. resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`. class_embed_type (`str`, *optional*, defaults to `None`): The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, `"timestep"`, or `"identity"`. num_class_embeds (`int`, *optional*, defaults to `None`): Input dimension of the learnable embedding matrix to be projected to `time_embed_dim` when performing class conditioning with `class_embed_type` equal to `None`. """ @register_to_config def __init__( self, sample_size: Optional[Union[int, Tuple[int, int]]] = None, in_channels: int = 3, out_channels: int = 3, center_input_sample: bool = False, time_embedding_type: str = "positional", freq_shift: int = 0, flip_sin_to_cos: bool = True, down_block_types: Tuple[str, ...] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D"), up_block_types: Tuple[str, ...] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D"), block_out_channels: Tuple[int, ...] = (224, 448, 672, 896), layers_per_block: int = 2, mid_block_scale_factor: float = 1, downsample_padding: int = 1, downsample_type: str = "conv", upsample_type: str = "conv", dropout: float = 0.0, act_fn: str = "silu", attention_head_dim: Optional[int] = 8, norm_num_groups: int = 32, attn_norm_num_groups: Optional[int] = None, norm_eps: float = 1e-5, resnet_time_scale_shift: str = "default", add_attention: bool = True, class_embed_type: Optional[str] = None, num_class_embeds_floor_hue=NUM_CLASSES_FLOOR_HUE + 1, num_class_embeds_object_hue=NUM_CLASSES_OBJECT_HUE + 1, num_class_embeds_orientation=NUM_CLASSES_ORIENTATION + 1, num_class_embeds_scale=NUM_CLASSES_SCALE + 1, num_class_embeds_shape=NUM_CLASSES_SHAPE + 1, num_class_embeds_wall_hue=NUM_CLASSES_WALL_HUE + 1, num_train_timesteps: Optional[int] = None, set_W_to_weight: Optional[bool] = True ): super().__init__() self.sample_size = sample_size time_embed_dim = block_out_channels[0] * 4 # Check inputs if len(down_block_types) != len(up_block_types): raise ValueError( f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." ) if len(block_out_channels) != len(down_block_types): raise ValueError( f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." ) # input self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)) # time if time_embedding_type == "fourier": self.time_proj = GaussianFourierProjection(embedding_size=block_out_channels[0], scale=16, set_W_to_weight=set_W_to_weight) timestep_input_dim = 2 * block_out_channels[0] elif time_embedding_type == "positional": self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) timestep_input_dim = block_out_channels[0] elif time_embedding_type == "learned": self.time_proj = nn.Embedding(num_train_timesteps, block_out_channels[0]) timestep_input_dim = block_out_channels[0] self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) # class embedding if class_embed_type is None and num_class_embeds_floor_hue is not None: self.class_embedding_floor_hue = nn.Embedding(num_class_embeds_floor_hue, time_embed_dim) self.class_embedding_object_hue = nn.Embedding(num_class_embeds_object_hue, time_embed_dim) self.class_embedding_orientation = nn.Embedding(num_class_embeds_orientation, time_embed_dim) self.class_embedding_scale = nn.Embedding(num_class_embeds_scale, time_embed_dim) self.class_embedding_shape = nn.Embedding(num_class_embeds_shape, time_embed_dim) self.class_embedding_wall_hue = nn.Embedding(num_class_embeds_wall_hue, time_embed_dim) elif class_embed_type == "timestep": self.class_embedding_floor_hue = TimestepEmbedding(timestep_input_dim, time_embed_dim) self.class_embedding_object_hue = TimestepEmbedding(timestep_input_dim, time_embed_dim) self.class_embedding_orientation = TimestepEmbedding(timestep_input_dim, time_embed_dim) self.class_embedding_scale = TimestepEmbedding(timestep_input_dim, time_embed_dim) self.class_embedding_shape = TimestepEmbedding(timestep_input_dim, time_embed_dim) self.class_embedding_wall_hue = TimestepEmbedding(timestep_input_dim, time_embed_dim) elif class_embed_type == "identity": self.class_embedding_floor_hue = nn.Identity(time_embed_dim, time_embed_dim) self.class_embedding_object_hue = nn.Identity(time_embed_dim, time_embed_dim) self.class_embedding_orientation = nn.Identity(time_embed_dim, time_embed_dim) self.class_embedding_scale = nn.Identity(time_embed_dim, time_embed_dim) self.class_embedding_shape = nn.Identity(time_embed_dim, time_embed_dim) self.class_embedding_wall_hue = nn.Identity(time_embed_dim, time_embed_dim) else: self.class_embedding_floor_hue = None self.down_blocks = nn.ModuleList([]) self.mid_block = None self.up_blocks = nn.ModuleList([]) # down output_channel = block_out_channels[0] for i, down_block_type in enumerate(down_block_types): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 down_block = get_down_block( down_block_type, num_layers=layers_per_block, in_channels=input_channel, out_channels=output_channel, temb_channels=time_embed_dim, add_downsample=not is_final_block, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel, downsample_padding=downsample_padding, resnet_time_scale_shift=resnet_time_scale_shift, downsample_type=downsample_type, dropout=dropout, ) self.down_blocks.append(down_block) # mid self.mid_block = UNetMidBlock2D( in_channels=block_out_channels[-1], temb_channels=time_embed_dim, dropout=dropout, resnet_eps=norm_eps, resnet_act_fn=act_fn, output_scale_factor=mid_block_scale_factor, resnet_time_scale_shift=resnet_time_scale_shift, attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1], resnet_groups=norm_num_groups, attn_groups=attn_norm_num_groups, add_attention=add_attention, ) # up reversed_block_out_channels = list(reversed(block_out_channels)) output_channel = reversed_block_out_channels[0] for i, up_block_type in enumerate(up_block_types): prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] is_final_block = i == len(block_out_channels) - 1 up_block = get_up_block( up_block_type, num_layers=layers_per_block + 1, in_channels=input_channel, out_channels=output_channel, prev_output_channel=prev_output_channel, temb_channels=time_embed_dim, add_upsample=not is_final_block, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel, resnet_time_scale_shift=resnet_time_scale_shift, upsample_type=upsample_type, dropout=dropout, ) self.up_blocks.append(up_block) prev_output_channel = output_channel # out num_groups_out = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4, 32) self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=num_groups_out, eps=norm_eps) self.conv_act = nn.SiLU() self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, kernel_size=3, padding=1) def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], class_labels: Optional[torch.Tensor] = None, return_dict: bool = True, ) -> Union[UNet2DOutput, Tuple]: r""" The [`UNet2DModel`] forward method. Args: sample (`torch.FloatTensor`): The noisy input tensor with the following shape `(batch, channel, height, width)`. timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. class_labels (`torch.FloatTensor`, *optional*, defaults to `None`): Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.unet_2d.UNet2DOutput`] instead of a plain tuple. Returns: [`~models.unet_2d.UNet2DOutput`] or `tuple`: If `return_dict` is True, an [`~models.unet_2d.UNet2DOutput`] is returned, otherwise a `tuple` is returned where the first element is the sample tensor. """ # 0. center input if necessary if self.config.center_input_sample: sample = 2 * sample - 1.0 # 1. time timesteps = timestep if not torch.is_tensor(timesteps): timesteps = torch.tensor([timesteps], dtype=torch.long, device=sample.device) elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0: timesteps = timesteps[None].to(sample.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timesteps = timesteps * torch.ones(sample.shape[0], dtype=timesteps.dtype, device=timesteps.device) t_emb = self.time_proj(timesteps) # timesteps does not contain any weights and will always return f32 tensors # but time_embedding might actually be running in fp16. so we need to cast here. # there might be better ways to encapsulate this. t_emb = t_emb.to(dtype=self.dtype) emb = self.time_embedding(t_emb) if self.class_embedding_floor_hue is not None: if class_labels is None: raise ValueError("class_labels should be provided when doing class conditioning") class_labels_floor_hue = class_labels[:, 0] class_labels_object_hue = class_labels[:, 1] class_labels_orientation = class_labels[:, 2] class_labels_scale = class_labels[:, 3] class_labels_shape = class_labels[:, 4] class_labels_wall_hue = class_labels[:, 5] if self.config.class_embed_type == "timestep": class_labels_floor_hue = self.time_proj(class_labels_floor_hue) class_labels_object_hue = self.time_proj(class_labels_object_hue) class_labels_orientation = self.time_proj(class_labels_orientation) class_labels_scale = self.time_proj(class_labels_scale) class_labels_shape = self.time_proj(class_labels_shape) class_labels_wall_hue = self.time_proj(class_labels_wall_hue) def add_embedding_if_non_zero(class_labels, class_embedding): # Create an output tensor initialized to zero of the required shape output = torch.zeros((class_labels.size(0), emb.size(1)), device=emb.device) # Check for non-zero indices non_zero_indices = class_labels.nonzero(as_tuple=True) if non_zero_indices[0].numel() > 0: # Compute embeddings for non-zero indices only embeddings = class_embedding(class_labels[non_zero_indices]) # Place computed embeddings back into the correct positions output[non_zero_indices] = embeddings return output if self.class_embedding_floor_hue: emb += self.class_embedding_floor_hue(class_labels_floor_hue) if self.class_embedding_object_hue: emb += self.class_embedding_object_hue(class_labels_object_hue) if self.class_embedding_orientation: emb += self.class_embedding_orientation(class_labels_orientation) if self.class_embedding_scale: emb += self.class_embedding_scale(class_labels_scale) if self.class_embedding_shape: emb += self.class_embedding_shape(class_labels_shape) if self.class_embedding_wall_hue: emb += self.class_embedding_wall_hue(class_labels_wall_hue) elif self.class_embedding_floor_hue is None and class_labels is not None: raise ValueError("class_embedding needs to be initialized in order to use class conditioning") # 2. pre-process skip_sample = sample sample = self.conv_in(sample) # 3. down down_block_res_samples = (sample,) for downsample_block in self.down_blocks: if hasattr(downsample_block, "skip_conv"): sample, res_samples, skip_sample = downsample_block( hidden_states=sample, temb=emb, skip_sample=skip_sample ) else: sample, res_samples = downsample_block(hidden_states=sample, temb=emb) down_block_res_samples += res_samples # 4. mid sample = self.mid_block(sample, emb) # 5. up skip_sample = None for upsample_block in self.up_blocks: res_samples = down_block_res_samples[-len(upsample_block.resnets) :] down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] if hasattr(upsample_block, "skip_conv"): sample, skip_sample = upsample_block(sample, res_samples, emb, skip_sample) else: sample = upsample_block(sample, res_samples, emb) # 6. post-process sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) if skip_sample is not None: sample += skip_sample if self.config.time_embedding_type == "fourier": timesteps = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:])))) sample = sample / timesteps if not return_dict: return (sample,) return UNet2DOutput(sample=sample)