# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np import paddle import paddle.nn as nn from paddle.distributed.fleet.utils import recompute from ...configuration_utils import ConfigMixin, register_to_config from ...modeling_utils import ModelMixin from ...models.attention import DualTransformer2DModel, Transformer2DModel from ...models.cross_attention import ( AttnProcessor, CrossAttention, CrossAttnAddedKVProcessor, ) from ...models.embeddings import TimestepEmbedding, Timesteps from ...models.unet_2d_condition import UNet2DConditionOutput from ...utils import logging logger = logging.get_logger(__name__) # pylint: disable=invalid-name def get_down_block( down_block_type, num_layers, in_channels, out_channels, temb_channels, add_downsample, resnet_eps, resnet_act_fn, attn_num_head_channels, resnet_groups=None, cross_attention_dim=None, downsample_padding=None, dual_cross_attention=False, use_linear_projection=False, only_cross_attention=False, upcast_attention=False, resnet_time_scale_shift="default", ): down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type if down_block_type == "DownBlockFlat": return DownBlockFlat( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, 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, ) elif down_block_type == "CrossAttnDownBlockFlat": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockFlat") return CrossAttnDownBlockFlat( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, 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, attn_num_head_channels=attn_num_head_channels, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, resnet_time_scale_shift=resnet_time_scale_shift, ) raise ValueError(f"{down_block_type} is not supported.") def get_up_block( up_block_type, num_layers, in_channels, out_channels, prev_output_channel, temb_channels, add_upsample, resnet_eps, resnet_act_fn, attn_num_head_channels, resnet_groups=None, cross_attention_dim=None, dual_cross_attention=False, use_linear_projection=False, only_cross_attention=False, upcast_attention=False, resnet_time_scale_shift="default", ): up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type if up_block_type == "UpBlockFlat": return UpBlockFlat( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, 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, ) elif up_block_type == "CrossAttnUpBlockFlat": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockFlat") return CrossAttnUpBlockFlat( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attn_num_head_channels, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, resnet_time_scale_shift=resnet_time_scale_shift, ) raise ValueError(f"{up_block_type} is not supported.") # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel with UNet2DConditionModel->UNetFlatConditionModel, nn.Conv2d->LinearMultiDim, Block2D->BlockFlat class UNetFlatConditionModel(ModelMixin, ConfigMixin): r""" UNetFlatConditionModel is a conditional 2D UNet model that takes in a noisy sample, conditional state, and a timestep and returns sample shaped output. This model inherits from [`ModelMixin`]. Check the superclass documentation for the generic methods the library implements for all the models (such as downloading or saving, etc.) Parameters: sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): Height and width of input/output 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 `("CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "DownBlockFlat")`): The tuple of downsample blocks to use. mid_block_type (`str`, *optional*, defaults to `"UNetMidBlockFlatCrossAttn"`): The mid block type. Choose from `UNetMidBlockFlatCrossAttn` or `UNetMidBlockFlatSimpleCrossAttn`. up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat",)`): 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. resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config for resnet blocks, see [`~models.resnet.ResnetBlockFlat`]. 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"`. """ _supports_gradient_checkpointing = True @register_to_config 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] = ( "CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "CrossAttnDownBlockFlat", "DownBlockFlat", ), mid_block_type: str = "UNetMidBlockFlatCrossAttn", up_block_types: Tuple[str] = ( "UpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat", "CrossAttnUpBlockFlat", ), only_cross_attention: Union[bool, Tuple[bool]] = False, 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 = 1280, attention_head_dim: Union[int, Tuple[int]] = 8, dual_cross_attention: bool = False, use_linear_projection: bool = False, class_embed_type: Optional[str] = None, num_class_embeds: Optional[int] = None, upcast_attention: bool = False, resnet_time_scale_shift: str = "default", ): super().__init__() self.sample_size = sample_size time_embed_dim = block_out_channels[0] * 4 # input self.conv_in = LinearMultiDim(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) # 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.LayerList([]) self.mid_block = None self.up_blocks = nn.LayerList([]) if isinstance(only_cross_attention, bool): only_cross_attention = [only_cross_attention] * len(down_block_types) if isinstance(attention_head_dim, int): attention_head_dim = (attention_head_dim,) * len(down_block_types) # 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[i], downsample_padding=downsample_padding, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention[i], upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, ) self.down_blocks.append(down_block) # mid if mid_block_type == "UNetMidBlockFlatCrossAttn": self.mid_block = UNetMidBlockFlatCrossAttn( 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=resnet_time_scale_shift, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attention_head_dim[-1], resnet_groups=norm_num_groups, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, ) elif mid_block_type == "UNetMidBlockFlatSimpleCrossAttn": self.mid_block = UNetMidBlockFlatSimpleCrossAttn( 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, cross_attention_dim=cross_attention_dim, attn_num_head_channels=attention_head_dim[-1], resnet_groups=norm_num_groups, resnet_time_scale_shift=resnet_time_scale_shift, ) else: raise ValueError(f"unknown mid_block_type : {mid_block_type}") # count how many layers upsample the images self.num_upsamplers = 0 # up reversed_block_out_channels = list(reversed(block_out_channels)) reversed_attention_head_dim = list(reversed(attention_head_dim)) reversed_only_cross_attention = list(reversed(only_cross_attention)) output_channel = reversed_block_out_channels[0] for i, up_block_type in enumerate(up_block_types): is_final_block = i == len(block_out_channels) - 1 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)] # add upsample block for all BUT final layer if not is_final_block: add_upsample = True self.num_upsamplers += 1 else: add_upsample = False 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=add_upsample, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, cross_attention_dim=cross_attention_dim, attn_num_head_channels=reversed_attention_head_dim[i], dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=reversed_only_cross_attention[i], upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, ) 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, epsilon=norm_eps ) self.conv_act = nn.Silu() self.conv_out = LinearMultiDim(block_out_channels[0], out_channels, kernel_size=3, padding=1) @property def attn_processors(self) -> Dict[str, AttnProcessor]: r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with indexed by its weight name. """ # set recursively processors = {} def fn_recursive_add_processors(name: str, module: nn.Layer, processors: Dict[str, AttnProcessor]): if hasattr(module, "set_processor"): processors[f"{name}.processor"] = module.processor for sub_name, child in module.named_children(): fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) return processors for name, module in self.named_children(): fn_recursive_add_processors(name, module, processors) return processors def set_attn_processor(self, processor: Union[AttnProcessor, Dict[str, AttnProcessor]]): r""" Parameters: `processor (`dict` of `AttnProcessor` or `AttnProcessor`): The instantiated processor class or a dictionary of processor classes that will be set as the processor of **all** `CrossAttention` layers. In case `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainablae attention processors.: """ count = len(self.attn_processors.keys()) if isinstance(processor, dict) and len(processor) != count: raise ValueError( f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" f" number of attention layers: {count}. Please make sure to pass {count} processor classes." ) def fn_recursive_attn_processor(name: str, module: nn.Layer, processor): if hasattr(module, "set_processor"): if not isinstance(processor, dict): module.set_processor(processor) else: module.set_processor(processor.pop(f"{name}.processor")) for sub_name, child in module.named_children(): fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) for name, module in self.named_children(): fn_recursive_attn_processor(name, module, processor) def set_attention_slice(self, slice_size): r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease. Args: slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`. """ sliceable_head_dims = [] def fn_recursive_retrieve_slicable_dims(module: nn.Layer): if hasattr(module, "set_attention_slice"): sliceable_head_dims.append(module.sliceable_head_dim) for child in module.children(): fn_recursive_retrieve_slicable_dims(child) # retrieve number of attention layers for module in self.children(): fn_recursive_retrieve_slicable_dims(module) num_slicable_layers = len(sliceable_head_dims) if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory slice_size = [dim // 2 for dim in sliceable_head_dims] elif slice_size == "max": # make smallest slice possible slice_size = num_slicable_layers * [1] slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size if len(slice_size) != len(sliceable_head_dims): raise ValueError( f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." ) for i in range(len(slice_size)): size = slice_size[i] dim = sliceable_head_dims[i] if size is not None and size > dim: raise ValueError(f"size {size} has to be smaller or equal to {dim}.") # Recursively walk through all the children. # Any children which exposes the set_attention_slice method # gets the message def fn_recursive_set_attention_slice(module: nn.Layer, slice_size: List[int]): if hasattr(module, "set_attention_slice"): module.set_attention_slice(slice_size.pop()) for child in module.children(): fn_recursive_set_attention_slice(child, slice_size) reversed_slice_size = list(reversed(slice_size)) for module in self.children(): fn_recursive_set_attention_slice(module, reversed_slice_size) def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, (CrossAttnDownBlockFlat, DownBlockFlat, CrossAttnUpBlockFlat, UpBlockFlat)): module.gradient_checkpointing = value def forward( self, sample: paddle.Tensor, timestep: Union[paddle.Tensor, float, int], encoder_hidden_states: paddle.Tensor, class_labels: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, return_dict: bool = True, ) -> Union[UNet2DConditionOutput, Tuple]: r""" Args: sample (`paddle.Tensor`): (batch, channel, height, width) noisy inputs tensor timestep (`paddle.Tensor` or `float` or `int`): (batch) timesteps encoder_hidden_states (`paddle.Tensor`): (batch, sequence_length, feature_dim) encoder hidden states return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. 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. """ # By default samples have to be AT least a multiple of the overall upsampling factor. # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). # However, the upsampling interpolation output size can be forced to fit any upsampling size # on the fly if necessary. default_overall_up_factor = 2**self.num_upsamplers # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` forward_upsample_size = False upsample_size = None if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): logger.info("Forward upsample size to force interpolation output size.") forward_upsample_size = True # prepare attention_mask if attention_mask is not None: attention_mask = (1 - attention_mask.cast(sample.dtype)) * -10000.0 attention_mask = attention_mask.unsqueeze(1) # 0. center input if necessary if self.config.center_input_sample: sample = 2 * sample - 1.0 # 1. time timesteps = timestep if not paddle.is_tensor(timesteps): # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can timesteps = paddle.to_tensor([timesteps], dtype="int64") elif paddle.is_tensor(timesteps) and len(timesteps.shape) == 0: timesteps = timesteps[None] # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timesteps = timesteps.expand( [ sample.shape[0], ] ) 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.cast(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 num_class_embeds > 0") if self.config.class_embed_type == "timestep": class_labels = self.time_proj(class_labels) class_emb = self.class_embedding(class_labels).cast(self.dtype) emb = emb + class_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, "has_cross_attention") and downsample_block.has_cross_attention: sample, res_samples = downsample_block( hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, ) 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, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, ) # 5. up for i, upsample_block in enumerate(self.up_blocks): is_final_block = i == len(self.up_blocks) - 1 res_samples = down_block_res_samples[-len(upsample_block.resnets) :] down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] # if we have not reached the final block and need to forward the # upsample size, we do it here if not is_final_block and forward_upsample_size: upsample_size = down_block_res_samples[-1].shape[2:] if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, upsample_size=upsample_size, attention_mask=attention_mask, ) else: sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size ) # 6. post-process sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) if not return_dict: return (sample,) return UNet2DConditionOutput(sample=sample) class LinearMultiDim(nn.Linear): def __init__(self, in_features, out_features=None, second_dim=4, *args, **kwargs): in_features = [in_features, second_dim, 1] if isinstance(in_features, int) else list(in_features) if out_features is None: out_features = in_features out_features = [out_features, second_dim, 1] if isinstance(out_features, int) else list(out_features) self.in_features_multidim = in_features self.out_features_multidim = out_features super().__init__(np.array(in_features).prod(), np.array(out_features).prod()) def forward(self, input_tensor, *args, **kwargs): shape = input_tensor.shape n_dim = len(self.in_features_multidim) input_tensor = input_tensor.reshape([*shape[0:-n_dim], self.in_features]) output_tensor = super().forward(input_tensor) output_tensor = output_tensor.reshape([*shape[0:-n_dim], *self.out_features_multidim]) return output_tensor class ResnetBlockFlat(nn.Layer): def __init__( self, *, in_channels, out_channels=None, dropout=0.0, temb_channels=512, groups=32, groups_out=None, pre_norm=True, eps=1e-6, time_embedding_norm="default", use_in_shortcut=None, second_dim=4, **kwargs, ): super().__init__() self.pre_norm = pre_norm self.pre_norm = True in_channels = [in_channels, second_dim, 1] if isinstance(in_channels, int) else list(in_channels) self.in_channels_prod = np.array(in_channels).prod() self.channels_multidim = in_channels if out_channels is not None: out_channels = [out_channels, second_dim, 1] if isinstance(out_channels, int) else list(out_channels) out_channels_prod = np.array(out_channels).prod() self.out_channels_multidim = out_channels else: out_channels_prod = self.in_channels_prod self.out_channels_multidim = self.channels_multidim self.time_embedding_norm = time_embedding_norm if groups_out is None: groups_out = groups self.norm1 = nn.GroupNorm(num_groups=groups, num_channels=self.in_channels_prod, epsilon=eps) self.conv1 = nn.Conv2D(self.in_channels_prod, out_channels_prod, kernel_size=1, padding=0) if temb_channels is not None: self.time_emb_proj = nn.Linear(temb_channels, out_channels_prod) else: self.time_emb_proj = None self.norm2 = nn.GroupNorm(num_groups=groups_out, num_channels=out_channels_prod, epsilon=eps) self.dropout = nn.Dropout(dropout) self.conv2 = nn.Conv2D(out_channels_prod, out_channels_prod, kernel_size=1, padding=0) self.nonlinearity = nn.Silu() self.use_in_shortcut = ( self.in_channels_prod != out_channels_prod if use_in_shortcut is None else use_in_shortcut ) self.conv_shortcut = None if self.use_in_shortcut: self.conv_shortcut = nn.Conv2D( self.in_channels_prod, out_channels_prod, kernel_size=1, stride=1, padding=0 ) def forward(self, input_tensor, temb): shape = input_tensor.shape n_dim = len(self.channels_multidim) input_tensor = input_tensor.reshape([*shape[0:-n_dim], self.in_channels_prod, 1, 1]) input_tensor = input_tensor.reshape([-1, self.in_channels_prod, 1, 1]) hidden_states = input_tensor hidden_states = self.norm1(hidden_states) hidden_states = self.nonlinearity(hidden_states) hidden_states = self.conv1(hidden_states) if temb is not None: temb = self.time_emb_proj(self.nonlinearity(temb))[:, :, None, None] hidden_states = hidden_states + temb hidden_states = self.norm2(hidden_states) hidden_states = self.nonlinearity(hidden_states) hidden_states = self.dropout(hidden_states) hidden_states = self.conv2(hidden_states) if self.conv_shortcut is not None: input_tensor = self.conv_shortcut(input_tensor) output_tensor = input_tensor + hidden_states output_tensor = output_tensor.reshape([*shape[0:-n_dim], -1]) output_tensor = output_tensor.reshape([*shape[0:-n_dim], *self.out_channels_multidim]) return output_tensor # Copied from diffusers.models.unet_2d_blocks.DownBlock2D with DownBlock2D->DownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim class DownBlockFlat(nn.Layer): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: 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, output_scale_factor=1.0, add_downsample=True, downsample_padding=1, ): super().__init__() resnets = [] for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlockFlat( 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, ) ) self.resnets = nn.LayerList(resnets) if add_downsample: self.downsamplers = nn.LayerList( [ LinearMultiDim( 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, temb=None): output_states = () for resnet in self.resnets: if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward hidden_states = recompute(create_custom_forward(resnet), hidden_states, temb) else: hidden_states = resnet(hidden_states, temb) output_states += (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) output_states += (hidden_states,) return hidden_states, output_states # Copied from diffusers.models.unet_2d_blocks.CrossAttnDownBlock2D with CrossAttnDownBlock2D->CrossAttnDownBlockFlat, ResnetBlock2D->ResnetBlockFlat, Downsample2D->LinearMultiDim class CrossAttnDownBlockFlat(nn.Layer): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: 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, attn_num_head_channels=1, cross_attention_dim=1280, output_scale_factor=1.0, downsample_padding=1, add_downsample=True, dual_cross_attention=False, use_linear_projection=False, only_cross_attention=False, upcast_attention=False, ): super().__init__() resnets = [] attentions = [] self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlockFlat( 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( Transformer2DModel( attn_num_head_channels, out_channels // attn_num_head_channels, in_channels=out_channels, num_layers=1, 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, ) ) else: attentions.append( DualTransformer2DModel( attn_num_head_channels, out_channels // attn_num_head_channels, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) self.attentions = nn.LayerList(attentions) self.resnets = nn.LayerList(resnets) if add_downsample: self.downsamplers = nn.LayerList( [ LinearMultiDim( 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, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None ): output_states = () for resnet, attn in zip(self.resnets, self.attentions): if self.training 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)[0] # move [0] else: return module(*inputs) return custom_forward hidden_states = recompute(create_custom_forward(resnet), hidden_states, temb) hidden_states = recompute( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, cross_attention_kwargs, ) # [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, ).sample output_states += (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) output_states += (hidden_states,) return hidden_states, output_states # Copied from diffusers.models.unet_2d_blocks.UpBlock2D with UpBlock2D->UpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim class UpBlockFlat(nn.Layer): def __init__( self, in_channels: int, prev_output_channel: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: 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, output_scale_factor=1.0, add_upsample=True, ): super().__init__() resnets = [] 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( ResnetBlockFlat( 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, ) ) self.resnets = nn.LayerList(resnets) if add_upsample: self.upsamplers = nn.LayerList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False def forward(self, hidden_states, res_hidden_states_tuple, temb=None, upsample_size=None): for resnet in self.resnets: # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = paddle.concat([hidden_states, res_hidden_states], axis=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward hidden_states = recompute(create_custom_forward(resnet), hidden_states, temb) else: hidden_states = resnet(hidden_states, temb) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size) return hidden_states # Copied from diffusers.models.unet_2d_blocks.CrossAttnUpBlock2D with CrossAttnUpBlock2D->CrossAttnUpBlockFlat, ResnetBlock2D->ResnetBlockFlat, Upsample2D->LinearMultiDim class CrossAttnUpBlockFlat(nn.Layer): def __init__( self, in_channels: int, out_channels: int, prev_output_channel: int, temb_channels: int, dropout: float = 0.0, num_layers: 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, attn_num_head_channels=1, cross_attention_dim=1280, output_scale_factor=1.0, add_upsample=True, dual_cross_attention=False, use_linear_projection=False, only_cross_attention=False, upcast_attention=False, ): super().__init__() resnets = [] attentions = [] self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels 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( ResnetBlockFlat( 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( Transformer2DModel( attn_num_head_channels, out_channels // attn_num_head_channels, in_channels=out_channels, num_layers=1, 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, ) ) else: attentions.append( DualTransformer2DModel( attn_num_head_channels, out_channels // attn_num_head_channels, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) self.attentions = nn.LayerList(attentions) self.resnets = nn.LayerList(resnets) if add_upsample: self.upsamplers = nn.LayerList([LinearMultiDim(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states, res_hidden_states_tuple, temb=None, encoder_hidden_states=None, cross_attention_kwargs=None, upsample_size=None, attention_mask=None, ): # TODO(Patrick, William) - attention mask is not used 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] hidden_states = paddle.concat([hidden_states, res_hidden_states], axis=1) if self.training 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)[0] # move [0] else: return module(*inputs) return custom_forward hidden_states = recompute(create_custom_forward(resnet), hidden_states, temb) hidden_states = recompute( create_custom_forward(attn, return_dict=False), hidden_states, encoder_hidden_states, cross_attention_kwargs, ) # [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, ).sample if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size) return hidden_states # Copied from diffusers.models.unet_2d_blocks.UNetMidBlock2DCrossAttn with UNetMidBlock2DCrossAttn->UNetMidBlockFlatCrossAttn, ResnetBlock2D->ResnetBlockFlat class UNetMidBlockFlatCrossAttn(nn.Layer): def __init__( self, in_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: 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, attn_num_head_channels=1, output_scale_factor=1.0, cross_attention_dim=1280, dual_cross_attention=False, use_linear_projection=False, upcast_attention=False, ): super().__init__() self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) # there is always at least one resnet resnets = [ ResnetBlockFlat( in_channels=in_channels, out_channels=in_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, ) ] attentions = [] for _ in range(num_layers): if not dual_cross_attention: attentions.append( Transformer2DModel( attn_num_head_channels, in_channels // attn_num_head_channels, in_channels=in_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, ) ) else: attentions.append( DualTransformer2DModel( attn_num_head_channels, in_channels // attn_num_head_channels, in_channels=in_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) resnets.append( ResnetBlockFlat( in_channels=in_channels, out_channels=in_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, ) ) self.attentions = nn.LayerList(attentions) self.resnets = nn.LayerList(resnets) def forward( self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None ): hidden_states = self.resnets[0](hidden_states, temb) for attn, resnet in zip(self.attentions, self.resnets[1:]): hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, ).sample hidden_states = resnet(hidden_states, temb) return hidden_states # Copied from diffusers.models.unet_2d_blocks.UNetMidBlock2DSimpleCrossAttn with UNetMidBlock2DSimpleCrossAttn->UNetMidBlockFlatSimpleCrossAttn, ResnetBlock2D->ResnetBlockFlat class UNetMidBlockFlatSimpleCrossAttn(nn.Layer): def __init__( self, in_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: 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, attn_num_head_channels=1, output_scale_factor=1.0, cross_attention_dim=1280, ): super().__init__() self.has_cross_attention = True self.attn_num_head_channels = attn_num_head_channels resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) self.num_heads = in_channels // self.attn_num_head_channels # there is always at least one resnet resnets = [ ResnetBlockFlat( in_channels=in_channels, out_channels=in_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, ) ] attentions = [] for _ in range(num_layers): attentions.append( CrossAttention( query_dim=in_channels, cross_attention_dim=in_channels, heads=self.num_heads, dim_head=attn_num_head_channels, added_kv_proj_dim=cross_attention_dim, norm_num_groups=resnet_groups, bias=True, upcast_softmax=True, processor=CrossAttnAddedKVProcessor(), ) ) resnets.append( ResnetBlockFlat( in_channels=in_channels, out_channels=in_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, ) ) self.attentions = nn.LayerList(attentions) self.resnets = nn.LayerList(resnets) def forward( self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None, cross_attention_kwargs=None ): cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} hidden_states = self.resnets[0](hidden_states, temb) for attn, resnet in zip(self.attentions, self.resnets[1:]): # attn hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, **cross_attention_kwargs, ) # resnet hidden_states = resnet(hidden_states, temb) return hidden_states