from typing import Dict, Union import torch import torch.nn as nn from ..configuration_utils import ConfigMixin, register_to_config from ..modeling_utils import ModelMixin from .embeddings import TimestepEmbedding, Timesteps from .unet_blocks import UNetMidBlock2DCrossAttn, get_down_block, get_up_block class UNet2DConditionModel(ModelMixin, ConfigMixin): @register_to_config def __init__( self, sample_size=None, in_channels=4, out_channels=4, center_input_sample=False, flip_sin_to_cos=True, freq_shift=0, down_block_types=("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D"), up_block_types=("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"), block_out_channels=(320, 640, 1280, 1280), layers_per_block=2, downsample_padding=1, mid_block_scale_factor=1, act_fn="silu", norm_num_groups=32, norm_eps=1e-5, cross_attention_dim=1280, attention_head_dim=8, ): super().__init__() self.sample_size = sample_size time_embed_dim = block_out_channels[0] * 4 # input self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1)) # time self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) timestep_input_dim = block_out_channels[0] self.time_embedding = TimestepEmbedding(timestep_input_dim, 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, 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, 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, timestep: Union[torch.Tensor, float, int], encoder_hidden_states: torch.Tensor, ) -> Dict[str, torch.FloatTensor]: # 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 timesteps = timesteps.broadcast_to(sample.shape[0]) t_emb = self.time_proj(timesteps) emb = self.time_embedding(t_emb) # 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=emb, encoder_hidden_states=encoder_hidden_states ) 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, 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=emb, res_hidden_states_tuple=res_samples, encoder_hidden_states=encoder_hidden_states, ) else: sample = upsample_block(hidden_states=sample, temb=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) output = {"sample": sample} return output