from diffusers.models.unets.unet_2d_blocks import * from extensions.diffusers_diffsplat.models.transformers import TransformerMV2DModel # Copied from diffusers.models.unets.unet_2d_blocks.get_down_block # The only modifications: `CrossAttnDownBlock2D` -> `CrossAttnDownBlockMV2D` def get_down_mvblock( down_block_type: str, num_layers: int, in_channels: int, out_channels: int, temb_channels: int, add_downsample: bool, resnet_eps: float, resnet_act_fn: str, transformer_layers_per_block: int = 1, num_attention_heads: Optional[int] = None, resnet_groups: Optional[int] = None, cross_attention_dim: Optional[int] = None, downsample_padding: Optional[int] = None, dual_cross_attention: bool = False, use_linear_projection: bool = False, only_cross_attention: bool = False, upcast_attention: bool = False, resnet_time_scale_shift: str = "default", attention_type: str = "default", resnet_skip_time_act: bool = False, resnet_out_scale_factor: float = 1.0, cross_attention_norm: Optional[str] = None, attention_head_dim: Optional[int] = None, downsample_type: Optional[str] = None, dropout: float = 0.0, ): # If attn head dim is not defined, we default it to the number of heads if attention_head_dim is None: logger.warning( f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}." ) attention_head_dim = num_attention_heads down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type if down_block_type == "DownBlock2D": return DownBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, dropout=dropout, 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, ) # Multi-view Self-Attn elif down_block_type == "CrossAttnDownBlockMV2D": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockMV2D") return CrossAttnDownBlockMV2D( num_layers=num_layers, transformer_layers_per_block=transformer_layers_per_block, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, dropout=dropout, 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, num_attention_heads=num_attention_heads, 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, attention_type=attention_type, ) raise ValueError(f"{down_block_type} does not exist.") # Copied from diffusers.models.unets.unet_2d_blocks.get_mid_block # The only modifications: `UNetMidBlock2DCrossAttn` -> `UNetMidBlockMV2DCrossAttn` def get_mid_mvblock( mid_block_type: str, temb_channels: int, in_channels: int, resnet_eps: float, resnet_act_fn: str, resnet_groups: int, output_scale_factor: float = 1.0, transformer_layers_per_block: int = 1, num_attention_heads: Optional[int] = None, cross_attention_dim: Optional[int] = None, dual_cross_attention: bool = False, use_linear_projection: bool = False, mid_block_only_cross_attention: bool = False, upcast_attention: bool = False, resnet_time_scale_shift: str = "default", attention_type: str = "default", resnet_skip_time_act: bool = False, cross_attention_norm: Optional[str] = None, attention_head_dim: Optional[int] = 1, dropout: float = 0.0, ): # Multi-view Self-Attn if mid_block_type == "UNetMidBlockMV2DCrossAttn": return UNetMidBlockMV2DCrossAttn( transformer_layers_per_block=transformer_layers_per_block, in_channels=in_channels, temb_channels=temb_channels, dropout=dropout, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, output_scale_factor=output_scale_factor, resnet_time_scale_shift=resnet_time_scale_shift, cross_attention_dim=cross_attention_dim, num_attention_heads=num_attention_heads, resnet_groups=resnet_groups, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, attention_type=attention_type, ) elif mid_block_type is None: return None else: raise ValueError(f"unknown mid_block_type : {mid_block_type}") # Copied from diffusers.models.unets.unet_2d_blocks.get_up_block # The only modifications: `CrossAttnUpBlock2D` -> `CrossAttnUpBlockMV2D` def get_up_mvblock( up_block_type: str, num_layers: int, in_channels: int, out_channels: int, prev_output_channel: int, temb_channels: int, add_upsample: bool, resnet_eps: float, resnet_act_fn: str, resolution_idx: Optional[int] = None, transformer_layers_per_block: int = 1, num_attention_heads: Optional[int] = None, resnet_groups: Optional[int] = None, cross_attention_dim: Optional[int] = None, dual_cross_attention: bool = False, use_linear_projection: bool = False, only_cross_attention: bool = False, upcast_attention: bool = False, resnet_time_scale_shift: str = "default", attention_type: str = "default", resnet_skip_time_act: bool = False, resnet_out_scale_factor: float = 1.0, cross_attention_norm: Optional[str] = None, attention_head_dim: Optional[int] = None, upsample_type: Optional[str] = None, dropout: float = 0.0, ) -> nn.Module: # If attn head dim is not defined, we default it to the number of heads if attention_head_dim is None: logger.warning( f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}." ) attention_head_dim = num_attention_heads up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type if up_block_type == "UpBlock2D": return UpBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, resolution_idx=resolution_idx, dropout=dropout, 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, ) # Multi-view Self-Attn elif up_block_type == "CrossAttnUpBlockMV2D": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockMV2D") return CrossAttnUpBlockMV2D( num_layers=num_layers, transformer_layers_per_block=transformer_layers_per_block, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, resolution_idx=resolution_idx, dropout=dropout, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, cross_attention_dim=cross_attention_dim, num_attention_heads=num_attention_heads, 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, attention_type=attention_type, ) raise ValueError(f"{up_block_type} does not exist.") # Copied from diffusers.models.unets.unet_2d_blocks.CrossAttnDownBlock2D # The only modifications: `Transformer2DModel` -> `TransformerMV2DModel` class CrossAttnDownBlockMV2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, transformer_layers_per_block: Union[int, Tuple[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, num_attention_heads: int = 1, cross_attention_dim: int = 1280, output_scale_factor: float = 1.0, downsample_padding: int = 1, add_downsample: bool = True, dual_cross_attention: bool = False, use_linear_projection: bool = False, only_cross_attention: bool = False, upcast_attention: bool = False, attention_type: str = "default", ): super().__init__() resnets = [] attentions = [] self.has_cross_attention = True self.num_attention_heads = num_attention_heads if isinstance(transformer_layers_per_block, int): transformer_layers_per_block = [transformer_layers_per_block] * num_layers 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, ) ) if not dual_cross_attention: attentions.append( TransformerMV2DModel( num_attention_heads, out_channels // num_attention_heads, in_channels=out_channels, num_layers=transformer_layers_per_block[i], 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, attention_type=attention_type, ) ) else: attentions.append( DualTransformer2DModel( num_attention_heads, out_channels // num_attention_heads, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) 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 self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, encoder_attention_mask: Optional[torch.Tensor] = None, additional_residuals: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, Tuple[torch.Tensor, ...]]: if cross_attention_kwargs is not None: if cross_attention_kwargs.get("scale", None) is not None: logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") output_states = () blocks = list(zip(self.resnets, self.attentions)) for i, (resnet, attn) in enumerate(blocks): if torch.is_grad_enabled() and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] else: hidden_states = resnet(hidden_states, temb) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] # apply additional residuals to the output of the last pair of resnet and attention blocks if i == len(blocks) - 1 and additional_residuals is not None: hidden_states = hidden_states + additional_residuals output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) output_states = output_states + (hidden_states,) return hidden_states, output_states # Copied from diffusers.models.unets.unet_2d_blocks.UNetMidBlock2DCrossAttn # The only modifications: `Transformer2DModel` -> `TransformerMV2DModel` class UNetMidBlockMV2DCrossAttn(nn.Module): def __init__( self, in_channels: int, temb_channels: int, out_channels: Optional[int] = None, dropout: float = 0.0, num_layers: int = 1, transformer_layers_per_block: Union[int, Tuple[int]] = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_groups_out: Optional[int] = None, resnet_pre_norm: bool = True, num_attention_heads: int = 1, output_scale_factor: float = 1.0, cross_attention_dim: int = 1280, dual_cross_attention: bool = False, use_linear_projection: bool = False, upcast_attention: bool = False, attention_type: str = "default", ): super().__init__() out_channels = out_channels or in_channels self.in_channels = in_channels self.out_channels = out_channels self.has_cross_attention = True self.num_attention_heads = num_attention_heads resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) # support for variable transformer layers per block if isinstance(transformer_layers_per_block, int): transformer_layers_per_block = [transformer_layers_per_block] * num_layers resnet_groups_out = resnet_groups_out or resnet_groups # there is always at least one resnet resnets = [ ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, groups_out=resnet_groups_out, 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, ) ] attentions = [] for i in range(num_layers): if not dual_cross_attention: attentions.append( TransformerMV2DModel( num_attention_heads, out_channels // num_attention_heads, in_channels=out_channels, num_layers=transformer_layers_per_block[i], cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups_out, use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, attention_type=attention_type, ) ) else: attentions.append( DualTransformer2DModel( num_attention_heads, out_channels // num_attention_heads, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) resnets.append( ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups_out, 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, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, encoder_attention_mask: Optional[torch.Tensor] = None, ) -> torch.Tensor: if cross_attention_kwargs is not None: if cross_attention_kwargs.get("scale", None) is not None: logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") hidden_states = self.resnets[0](hidden_states, temb) for attn, resnet in zip(self.attentions, self.resnets[1:]): if torch.is_grad_enabled() and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) else: hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] hidden_states = resnet(hidden_states, temb) return hidden_states # Copied from diffusers.models.unets.unet_2d_blocks.CrossAttnUpBlock2D # The only modifications: `Transformer2DModel` -> `TransformerMV2DModel` class CrossAttnUpBlockMV2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, prev_output_channel: int, temb_channels: int, resolution_idx: Optional[int] = None, dropout: float = 0.0, num_layers: int = 1, transformer_layers_per_block: Union[int, Tuple[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, num_attention_heads: int = 1, cross_attention_dim: int = 1280, output_scale_factor: float = 1.0, add_upsample: bool = True, dual_cross_attention: bool = False, use_linear_projection: bool = False, only_cross_attention: bool = False, upcast_attention: bool = False, attention_type: str = "default", ): super().__init__() resnets = [] attentions = [] self.has_cross_attention = True self.num_attention_heads = num_attention_heads if isinstance(transformer_layers_per_block, int): transformer_layers_per_block = [transformer_layers_per_block] * num_layers 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, ) ) if not dual_cross_attention: attentions.append( TransformerMV2DModel( num_attention_heads, out_channels // num_attention_heads, in_channels=out_channels, num_layers=transformer_layers_per_block[i], 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, attention_type=attention_type, ) ) else: attentions.append( DualTransformer2DModel( num_attention_heads, out_channels // num_attention_heads, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.Tensor, res_hidden_states_tuple: Tuple[torch.Tensor, ...], temb: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, upsample_size: Optional[int] = None, attention_mask: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, ) -> torch.Tensor: if cross_attention_kwargs is not None: if cross_attention_kwargs.get("scale", None) is not None: logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") is_freeu_enabled = ( getattr(self, "s1", None) and getattr(self, "s2", None) and getattr(self, "b1", None) and getattr(self, "b2", None) ) for resnet, attn in zip(self.resnets, self.attentions): # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] # FreeU: Only operate on the first two stages if is_freeu_enabled: hidden_states, res_hidden_states = apply_freeu( self.resolution_idx, hidden_states, res_hidden_states, s1=self.s1, s2=self.s2, b1=self.b1, b2=self.b2, ) hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if torch.is_grad_enabled() and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] else: hidden_states = resnet(hidden_states, temb) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size) return hidden_states