from dataclasses import dataclass from typing import Dict, Optional, Union import torch import torch.nn.functional as F from torch import nn from ..configuration_utils import ConfigMixin, register_to_config from ..utils import BaseOutput from .attention import BasicTransformerBlock from .attention_processor import AttentionProcessor, AttnProcessor from .embeddings import TimestepEmbedding, Timesteps from .modeling_utils import ModelMixin @dataclass class PriorTransformerOutput(BaseOutput): """ The output of [`PriorTransformer`]. Args: predicted_image_embedding (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`): The predicted CLIP image embedding conditioned on the CLIP text embedding input. """ predicted_image_embedding: torch.FloatTensor class PriorTransformer(ModelMixin, ConfigMixin): """ A Prior Transformer model. Parameters: num_attention_heads (`int`, *optional*, defaults to 32): The number of heads to use for multi-head attention. attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head. num_layers (`int`, *optional*, defaults to 20): The number of layers of Transformer blocks to use. embedding_dim (`int`, *optional*, defaults to 768): The dimension of the model input `hidden_states` num_embeddings (`int`, *optional*, defaults to 77): The number of embeddings of the model input `hidden_states` additional_embeddings (`int`, *optional*, defaults to 4): The number of additional tokens appended to the projected `hidden_states`. The actual length of the used `hidden_states` is `num_embeddings + additional_embeddings`. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. time_embed_act_fn (`str`, *optional*, defaults to 'silu'): The activation function to use to create timestep embeddings. norm_in_type (`str`, *optional*, defaults to None): The normalization layer to apply on hidden states before passing to Transformer blocks. Set it to `None` if normalization is not needed. embedding_proj_norm_type (`str`, *optional*, defaults to None): The normalization layer to apply on the input `proj_embedding`. Set it to `None` if normalization is not needed. encoder_hid_proj_type (`str`, *optional*, defaults to `linear`): The projection layer to apply on the input `encoder_hidden_states`. Set it to `None` if `encoder_hidden_states` is `None`. added_emb_type (`str`, *optional*, defaults to `prd`): Additional embeddings to condition the model. Choose from `prd` or `None`. if choose `prd`, it will prepend a token indicating the (quantized) dot product between the text embedding and image embedding as proposed in the unclip paper https://arxiv.org/abs/2204.06125 If it is `None`, no additional embeddings will be prepended. time_embed_dim (`int, *optional*, defaults to None): The dimension of timestep embeddings. If None, will be set to `num_attention_heads * attention_head_dim` embedding_proj_dim (`int`, *optional*, default to None): The dimension of `proj_embedding`. If None, will be set to `embedding_dim`. clip_embed_dim (`int`, *optional*, default to None): The dimension of the output. If None, will be set to `embedding_dim`. """ @register_to_config def __init__( self, num_attention_heads: int = 32, attention_head_dim: int = 64, num_layers: int = 20, embedding_dim: int = 768, num_embeddings=77, additional_embeddings=4, dropout: float = 0.0, time_embed_act_fn: str = "silu", norm_in_type: Optional[str] = None, # layer embedding_proj_norm_type: Optional[str] = None, # layer encoder_hid_proj_type: Optional[str] = "linear", # linear added_emb_type: Optional[str] = "prd", # prd time_embed_dim: Optional[int] = None, embedding_proj_dim: Optional[int] = None, clip_embed_dim: Optional[int] = None, ): super().__init__() self.num_attention_heads = num_attention_heads self.attention_head_dim = attention_head_dim inner_dim = num_attention_heads * attention_head_dim self.additional_embeddings = additional_embeddings time_embed_dim = time_embed_dim or inner_dim embedding_proj_dim = embedding_proj_dim or embedding_dim clip_embed_dim = clip_embed_dim or embedding_dim self.time_proj = Timesteps(inner_dim, True, 0) self.time_embedding = TimestepEmbedding(inner_dim, time_embed_dim, out_dim=inner_dim, act_fn=time_embed_act_fn) self.proj_in = nn.Linear(embedding_dim, inner_dim) if embedding_proj_norm_type is None: self.embedding_proj_norm = None elif embedding_proj_norm_type == "layer": self.embedding_proj_norm = nn.LayerNorm(embedding_proj_dim) else: raise ValueError(f"unsupported embedding_proj_norm_type: {embedding_proj_norm_type}") self.embedding_proj = nn.Linear(embedding_proj_dim, inner_dim) if encoder_hid_proj_type is None: self.encoder_hidden_states_proj = None elif encoder_hid_proj_type == "linear": self.encoder_hidden_states_proj = nn.Linear(embedding_dim, inner_dim) else: raise ValueError(f"unsupported encoder_hid_proj_type: {encoder_hid_proj_type}") self.positional_embedding = nn.Parameter(torch.zeros(1, num_embeddings + additional_embeddings, inner_dim)) if added_emb_type == "prd": self.prd_embedding = nn.Parameter(torch.zeros(1, 1, inner_dim)) elif added_emb_type is None: self.prd_embedding = None else: raise ValueError( f"`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`." ) self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock( inner_dim, num_attention_heads, attention_head_dim, dropout=dropout, activation_fn="gelu", attention_bias=True, ) for d in range(num_layers) ] ) if norm_in_type == "layer": self.norm_in = nn.LayerNorm(inner_dim) elif norm_in_type is None: self.norm_in = None else: raise ValueError(f"Unsupported norm_in_type: {norm_in_type}.") self.norm_out = nn.LayerNorm(inner_dim) self.proj_to_clip_embeddings = nn.Linear(inner_dim, clip_embed_dim) causal_attention_mask = torch.full( [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings], -10000.0 ) causal_attention_mask.triu_(1) causal_attention_mask = causal_attention_mask[None, ...] self.register_buffer("causal_attention_mask", causal_attention_mask, persistent=False) self.clip_mean = nn.Parameter(torch.zeros(1, clip_embed_dim)) self.clip_std = nn.Parameter(torch.zeros(1, clip_embed_dim)) @property # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors def attn_processors(self) -> Dict[str, AttentionProcessor]: 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: torch.nn.Module, processors: Dict[str, AttentionProcessor]): 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 # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): The instantiated processor class or a dictionary of processor classes that will be set as the processor for **all** `Attention` layers. If `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable 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: torch.nn.Module, 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) # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor def set_default_attn_processor(self): """ Disables custom attention processors and sets the default attention implementation. """ self.set_attn_processor(AttnProcessor()) def forward( self, hidden_states, timestep: Union[torch.Tensor, float, int], proj_embedding: torch.FloatTensor, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.BoolTensor] = None, return_dict: bool = True, ): """ The [`PriorTransformer`] forward method. Args: hidden_states (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`): The currently predicted image embeddings. timestep (`torch.LongTensor`): Current denoising step. proj_embedding (`torch.FloatTensor` of shape `(batch_size, embedding_dim)`): Projected embedding vector the denoising process is conditioned on. encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_embeddings, embedding_dim)`): Hidden states of the text embeddings the denoising process is conditioned on. attention_mask (`torch.BoolTensor` of shape `(batch_size, num_embeddings)`): Text mask for the text embeddings. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.prior_transformer.PriorTransformerOutput`] instead of a plain tuple. Returns: [`~models.prior_transformer.PriorTransformerOutput`] or `tuple`: If return_dict is True, a [`~models.prior_transformer.PriorTransformerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. """ batch_size = hidden_states.shape[0] timesteps = timestep if not torch.is_tensor(timesteps): timesteps = torch.tensor([timesteps], dtype=torch.long, device=hidden_states.device) elif torch.is_tensor(timesteps) and len(timesteps.shape) == 0: timesteps = timesteps[None].to(hidden_states.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timesteps = timesteps * torch.ones(batch_size, dtype=timesteps.dtype, device=timesteps.device) timesteps_projected = self.time_proj(timesteps) # timesteps does not contain any weights and will always return f32 tensors # but time_embedding might be fp16, so we need to cast here. timesteps_projected = timesteps_projected.to(dtype=self.dtype) time_embeddings = self.time_embedding(timesteps_projected) if self.embedding_proj_norm is not None: proj_embedding = self.embedding_proj_norm(proj_embedding) proj_embeddings = self.embedding_proj(proj_embedding) if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None: encoder_hidden_states = self.encoder_hidden_states_proj(encoder_hidden_states) elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None: raise ValueError("`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set") hidden_states = self.proj_in(hidden_states) positional_embeddings = self.positional_embedding.to(hidden_states.dtype) additional_embeds = [] additional_embeddings_len = 0 if encoder_hidden_states is not None: additional_embeds.append(encoder_hidden_states) additional_embeddings_len += encoder_hidden_states.shape[1] if len(proj_embeddings.shape) == 2: proj_embeddings = proj_embeddings[:, None, :] if len(hidden_states.shape) == 2: hidden_states = hidden_states[:, None, :] additional_embeds = additional_embeds + [ proj_embeddings, time_embeddings[:, None, :], hidden_states, ] if self.prd_embedding is not None: prd_embedding = self.prd_embedding.to(hidden_states.dtype).expand(batch_size, -1, -1) additional_embeds.append(prd_embedding) hidden_states = torch.cat( additional_embeds, dim=1, ) # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens additional_embeddings_len = additional_embeddings_len + proj_embeddings.shape[1] + 1 if positional_embeddings.shape[1] < hidden_states.shape[1]: positional_embeddings = F.pad( positional_embeddings, ( 0, 0, additional_embeddings_len, self.prd_embedding.shape[1] if self.prd_embedding is not None else 0, ), value=0.0, ) hidden_states = hidden_states + positional_embeddings if attention_mask is not None: attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 attention_mask = F.pad(attention_mask, (0, self.additional_embeddings), value=0.0) attention_mask = (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype) attention_mask = attention_mask.repeat_interleave(self.config.num_attention_heads, dim=0) if self.norm_in is not None: hidden_states = self.norm_in(hidden_states) for block in self.transformer_blocks: hidden_states = block(hidden_states, attention_mask=attention_mask) hidden_states = self.norm_out(hidden_states) if self.prd_embedding is not None: hidden_states = hidden_states[:, -1] else: hidden_states = hidden_states[:, additional_embeddings_len:] predicted_image_embedding = self.proj_to_clip_embeddings(hidden_states) if not return_dict: return (predicted_image_embedding,) return PriorTransformerOutput(predicted_image_embedding=predicted_image_embedding) def post_process_latents(self, prior_latents): prior_latents = (prior_latents * self.clip_std) + self.clip_mean return prior_latents