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| from typing import Optional, Tuple, Union | |
| import torch | |
| import torch.nn as nn | |
| import torch.utils.checkpoint | |
| from .embeddings import TimeEmbbeding | |
| from .unet_blocks import ( | |
| CrossAttnDownBlock2D, | |
| CrossAttnUpBlock2D, | |
| DownBlock2D, | |
| UNetMidBlock2DCrossAttn, | |
| UpBlock2D, | |
| get_down_block, | |
| get_up_block, | |
| ) | |
| class TimestepEmbedding(nn.Module): | |
| def __init__(self, channel, time_embed_dim, act_fn="silu"): | |
| super().__init__() | |
| self.linear_1 = nn.Linear(channel, time_embed_dim) | |
| self.act = None | |
| if act_fn == "silu": | |
| self.act = nn.SiLU() | |
| self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim) | |
| def forward(self, sample): | |
| sample = self.linear_1(sample) | |
| if self.act is not None: | |
| sample = self.act(sample) | |
| sample = self.linear_2(sample) | |
| return sample | |
| class UNet2DConditionModel(nn.Module): | |
| r""" | |
| UNet2DConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep | |
| and returns sample shaped output. | |
| Parameters: | |
| sample_size (`int`, *optional*): The size of the input sample. | |
| in_channels (`int`, *optional*, defaults to 4): The number of channels in the input sample. | |
| out_channels (`int`, *optional*, defaults to 4): The number of channels in the output. | |
| center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. | |
| flip_sin_to_cos (`bool`, *optional*, defaults to `False`): | |
| Whether to flip the sin to cos in the time embedding. | |
| freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. | |
| down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): | |
| The tuple of downsample blocks to use. | |
| up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D",)`): | |
| The tuple of upsample blocks to use. | |
| block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): | |
| The tuple of output channels for each block. | |
| layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. | |
| downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. | |
| mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. | |
| act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. | |
| norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. | |
| norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. | |
| cross_attention_dim (`int`, *optional*, defaults to 1280): The dimension of the cross attention features. | |
| attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. | |
| """ | |
| _supports_gradient_checkpointing = True | |
| def __init__( | |
| self, | |
| sample_size: Optional[int] = None, | |
| in_channels: int = 4, | |
| out_channels: int = 4, | |
| center_input_sample: bool = False, | |
| flip_sin_to_cos: bool = True, | |
| freq_shift: int = 0, | |
| down_block_types: Tuple[str] = ( | |
| "CrossAttnDownBlock2D", | |
| "CrossAttnDownBlock2D", | |
| "CrossAttnDownBlock2D", | |
| "DownBlock2D", | |
| ), | |
| up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"), | |
| block_out_channels: Tuple[int] = (320, 640, 1280, 1280), | |
| layers_per_block: int = 2, | |
| downsample_padding: int = 1, | |
| mid_block_scale_factor: float = 1, | |
| act_fn: str = "silu", | |
| norm_num_groups: int = 32, | |
| norm_eps: float = 1e-5, | |
| cross_attention_dim: int = 768, | |
| attention_head_dim: int = 8, | |
| ): | |
| super().__init__() | |
| self.sample_size = sample_size | |
| time_embed_dim = block_out_channels[0] * 4 | |
| self.emb = nn.Embedding(2, cross_attention_dim) | |
| # input | |
| self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)) | |
| # time | |
| self.time_embedding = TimeEmbbeding(block_out_channels[0], time_embed_dim) | |
| 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, | |
| cross_attention_dim=cross_attention_dim, | |
| attn_num_head_channels=attention_head_dim, | |
| downsample_padding=downsample_padding, | |
| ) | |
| self.down_blocks.append(down_block) | |
| # mid | |
| self.mid_block = UNetMidBlock2DCrossAttn( | |
| in_channels=block_out_channels[-1], | |
| temb_channels=time_embed_dim, | |
| resnet_eps=norm_eps, | |
| resnet_act_fn=act_fn, | |
| output_scale_factor=mid_block_scale_factor, | |
| resnet_time_scale_shift="default", | |
| cross_attention_dim=cross_attention_dim, | |
| attn_num_head_channels=attention_head_dim, | |
| resnet_groups=norm_num_groups, | |
| ) | |
| # 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, | |
| cross_attention_dim=cross_attention_dim, | |
| attn_num_head_channels=attention_head_dim, | |
| ) | |
| self.up_blocks.append(up_block) | |
| prev_output_channel = output_channel | |
| # out | |
| self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps) | |
| self.conv_act = nn.SiLU() | |
| self.conv_out = nn.Conv2d(block_out_channels[0], out_channels, 3, padding=1) | |
| def forward( | |
| self, | |
| sample: torch.FloatTensor, | |
| t: torch.Tensor, | |
| encoder_hidden_states: torch.Tensor = None, | |
| self_cond: torch.Tensor = None | |
| ): | |
| encoder_hidden_states = self.emb(encoder_hidden_states) | |
| # encoder_hidden_states = None # ------------------------ WARNING Disabled --------------------- | |
| """r | |
| Args: | |
| sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor | |
| timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps | |
| encoder_hidden_states (`torch.FloatTensor`): (batch, channel, height, width) encoder hidden states | |
| Returns: | |
| [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: | |
| [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When | |
| returning a tuple, 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 | |
| t_emb = self.time_embedding(t) | |
| # 2. pre-process | |
| sample = self.conv_in(sample) | |
| # 3. down | |
| down_block_res_samples = (sample,) | |
| for downsample_block in self.down_blocks: | |
| if hasattr(downsample_block, "attentions") and downsample_block.attentions is not None: | |
| sample, res_samples = downsample_block( | |
| hidden_states=sample, | |
| temb=t_emb, | |
| encoder_hidden_states=encoder_hidden_states, | |
| ) | |
| else: | |
| sample, res_samples = downsample_block(hidden_states=sample, temb=t_emb) | |
| down_block_res_samples += res_samples | |
| # 4. mid | |
| sample = self.mid_block(sample, t_emb, encoder_hidden_states=encoder_hidden_states) | |
| # 5. up | |
| 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, "attentions") and upsample_block.attentions is not None: | |
| sample = upsample_block( | |
| hidden_states=sample, | |
| temb=t_emb, | |
| res_hidden_states_tuple=res_samples, | |
| encoder_hidden_states=encoder_hidden_states, | |
| ) | |
| else: | |
| sample = upsample_block(hidden_states=sample, temb=t_emb, res_hidden_states_tuple=res_samples) | |
| # 6. post-process | |
| # make sure hidden states is in float32 | |
| # when running in half-precision | |
| sample = self.conv_norm_out(sample.float()).type(sample.dtype) | |
| sample = self.conv_act(sample) | |
| sample = self.conv_out(sample) | |
| return sample, [] | |