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| # Copyright 2023 The HuggingFace Team. 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 dataclasses import dataclass | |
| from typing import Any, Dict, Optional | |
| import torch | |
| import torch.nn.functional as F | |
| from torch import nn | |
| from diffusers.configuration_utils import ConfigMixin, register_to_config | |
| from diffusers.models.embeddings import ImagePositionalEmbeddings | |
| from diffusers.utils import BaseOutput, deprecate | |
| from .attention import BasicTransformerBlock | |
| from diffusers.models.embeddings import PatchEmbed | |
| from diffusers.models.modeling_utils import ModelMixin | |
| class Transformer2DModelOutput(BaseOutput): | |
| """ | |
| Args: | |
| sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete): | |
| Hidden states conditioned on `encoder_hidden_states` input. If discrete, returns probability distributions | |
| for the unnoised latent pixels. | |
| """ | |
| sample: torch.FloatTensor | |
| class Transformer2DModel(ModelMixin, ConfigMixin): | |
| """ | |
| Transformer model for image-like data. Takes either discrete (classes of vector embeddings) or continuous (actual | |
| embeddings) inputs. | |
| When input is continuous: First, project the input (aka embedding) and reshape to b, t, d. Then apply standard | |
| transformer action. Finally, reshape to image. | |
| When input is discrete: First, input (classes of latent pixels) is converted to embeddings and has positional | |
| embeddings applied, see `ImagePositionalEmbeddings`. Then apply standard transformer action. Finally, predict | |
| classes of unnoised image. | |
| Note that it is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised | |
| image do not contain a prediction for the masked pixel as the unnoised image cannot be masked. | |
| Parameters: | |
| num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. | |
| attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. | |
| in_channels (`int`, *optional*): | |
| Pass if the input is continuous. The number of channels in the input and output. | |
| num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. | |
| dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. | |
| cross_attention_dim (`int`, *optional*): The number of encoder_hidden_states dimensions to use. | |
| sample_size (`int`, *optional*): Pass if the input is discrete. The width of the latent images. | |
| Note that this is fixed at training time as it is used for learning a number of position embeddings. See | |
| `ImagePositionalEmbeddings`. | |
| num_vector_embeds (`int`, *optional*): | |
| Pass if the input is discrete. The number of classes of the vector embeddings of the latent pixels. | |
| Includes the class for the masked latent pixel. | |
| activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward. | |
| num_embeds_ada_norm ( `int`, *optional*): Pass if at least one of the norm_layers is `AdaLayerNorm`. | |
| The number of diffusion steps used during training. Note that this is fixed at training time as it is used | |
| to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for | |
| up to but not more than steps than `num_embeds_ada_norm`. | |
| attention_bias (`bool`, *optional*): | |
| Configure if the TransformerBlocks' attention should contain a bias parameter. | |
| """ | |
| def __init__( | |
| self, | |
| num_attention_heads: int = 16, | |
| attention_head_dim: int = 88, | |
| in_channels: Optional[int] = None, | |
| out_channels: Optional[int] = None, | |
| num_layers: int = 1, | |
| dropout: float = 0.0, | |
| norm_num_groups: int = 32, | |
| cross_attention_dim: Optional[int] = None, | |
| attention_bias: bool = False, | |
| sample_size: Optional[int] = None, | |
| num_vector_embeds: Optional[int] = None, | |
| patch_size: Optional[int] = None, | |
| activation_fn: str = "geglu", | |
| num_embeds_ada_norm: Optional[int] = None, | |
| use_linear_projection: bool = False, | |
| only_cross_attention: bool = False, | |
| upcast_attention: bool = False, | |
| norm_type: str = "layer_norm", | |
| norm_elementwise_affine: bool = True, | |
| use_gated_attention: bool = False, | |
| ): | |
| super().__init__() | |
| self.use_linear_projection = use_linear_projection | |
| self.num_attention_heads = num_attention_heads | |
| self.attention_head_dim = attention_head_dim | |
| inner_dim = num_attention_heads * attention_head_dim | |
| # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)` | |
| # Define whether input is continuous or discrete depending on configuration | |
| self.is_input_continuous = (in_channels is not None) and (patch_size is None) | |
| self.is_input_vectorized = num_vector_embeds is not None | |
| self.is_input_patches = in_channels is not None and patch_size is not None | |
| if norm_type == "layer_norm" and num_embeds_ada_norm is not None: | |
| deprecation_message = ( | |
| f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or" | |
| " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config." | |
| " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect" | |
| " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it" | |
| " would be very nice if you could open a Pull request for the `transformer/config.json` file" | |
| ) | |
| deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False) | |
| norm_type = "ada_norm" | |
| if self.is_input_continuous and self.is_input_vectorized: | |
| raise ValueError( | |
| f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make" | |
| " sure that either `in_channels` or `num_vector_embeds` is None." | |
| ) | |
| elif self.is_input_vectorized and self.is_input_patches: | |
| raise ValueError( | |
| f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make" | |
| " sure that either `num_vector_embeds` or `num_patches` is None." | |
| ) | |
| elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches: | |
| raise ValueError( | |
| f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:" | |
| f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None." | |
| ) | |
| # 2. Define input layers | |
| if self.is_input_continuous: | |
| self.in_channels = in_channels | |
| self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) | |
| if use_linear_projection: | |
| self.proj_in = nn.Linear(in_channels, inner_dim) | |
| else: | |
| self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) | |
| elif self.is_input_vectorized: | |
| assert sample_size is not None, "Transformer2DModel over discrete input must provide sample_size" | |
| assert num_vector_embeds is not None, "Transformer2DModel over discrete input must provide num_embed" | |
| self.height = sample_size | |
| self.width = sample_size | |
| self.num_vector_embeds = num_vector_embeds | |
| self.num_latent_pixels = self.height * self.width | |
| self.latent_image_embedding = ImagePositionalEmbeddings( | |
| num_embed=num_vector_embeds, embed_dim=inner_dim, height=self.height, width=self.width | |
| ) | |
| elif self.is_input_patches: | |
| assert sample_size is not None, "Transformer2DModel over patched input must provide sample_size" | |
| self.height = sample_size | |
| self.width = sample_size | |
| self.patch_size = patch_size | |
| self.pos_embed = PatchEmbed( | |
| height=sample_size, | |
| width=sample_size, | |
| patch_size=patch_size, | |
| in_channels=in_channels, | |
| embed_dim=inner_dim, | |
| ) | |
| # 3. Define transformers blocks | |
| self.transformer_blocks = nn.ModuleList( | |
| [ | |
| BasicTransformerBlock( | |
| inner_dim, | |
| num_attention_heads, | |
| attention_head_dim, | |
| dropout=dropout, | |
| cross_attention_dim=cross_attention_dim, | |
| activation_fn=activation_fn, | |
| num_embeds_ada_norm=num_embeds_ada_norm, | |
| attention_bias=attention_bias, | |
| only_cross_attention=only_cross_attention, | |
| upcast_attention=upcast_attention, | |
| norm_type=norm_type, | |
| norm_elementwise_affine=norm_elementwise_affine, | |
| use_gated_attention=use_gated_attention, | |
| ) | |
| for d in range(num_layers) | |
| ] | |
| ) | |
| # 4. Define output layers | |
| self.out_channels = in_channels if out_channels is None else out_channels | |
| if self.is_input_continuous: | |
| # TODO: should use out_channels for continuous projections | |
| if use_linear_projection: | |
| self.proj_out = nn.Linear(inner_dim, in_channels) | |
| else: | |
| self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) | |
| elif self.is_input_vectorized: | |
| self.norm_out = nn.LayerNorm(inner_dim) | |
| self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1) | |
| elif self.is_input_patches: | |
| self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6) | |
| self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim) | |
| self.proj_out_2 = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels) | |
| def forward( | |
| self, | |
| hidden_states: torch.Tensor, | |
| encoder_hidden_states: Optional[torch.Tensor] = None, | |
| timestep: Optional[torch.LongTensor] = None, | |
| class_labels: Optional[torch.LongTensor] = None, | |
| cross_attention_kwargs: Dict[str, Any] = None, | |
| attention_mask: Optional[torch.Tensor] = None, | |
| encoder_attention_mask: Optional[torch.Tensor] = None, | |
| return_dict: bool = True, | |
| return_cross_attention_probs: bool = False, | |
| ): | |
| """ | |
| Args: | |
| hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`. | |
| When continuous, `torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input | |
| hidden_states | |
| encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*): | |
| Conditional embeddings for cross attention layer. If not given, cross-attention defaults to | |
| self-attention. | |
| timestep ( `torch.LongTensor`, *optional*): | |
| Optional timestep to be applied as an embedding in AdaLayerNorm's. Used to indicate denoising step. | |
| class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): | |
| Optional class labels to be applied as an embedding in AdaLayerZeroNorm. Used to indicate class labels | |
| conditioning. | |
| encoder_attention_mask ( `torch.Tensor`, *optional* ). | |
| Cross-attention mask, applied to encoder_hidden_states. Two formats supported: | |
| Mask `(batch, sequence_length)` True = keep, False = discard. Bias `(batch, 1, sequence_length)` 0 | |
| = keep, -10000 = discard. | |
| If ndim == 2: will be interpreted as a mask, then converted into a bias consistent with the format | |
| above. This bias will be added to the cross-attention scores. | |
| 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.transformer_2d.Transformer2DModelOutput`] or `tuple`: | |
| [`~models.transformer_2d.Transformer2DModelOutput`] if `return_dict` is True, otherwise a `tuple`. When | |
| returning a tuple, the first element is the sample tensor. | |
| """ | |
| # ensure attention_mask is a bias, and give it a singleton query_tokens dimension. | |
| # we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward. | |
| # we can tell by counting dims; if ndim == 2: it's a mask rather than a bias. | |
| # expects mask of shape: | |
| # [batch, key_tokens] | |
| # adds singleton query_tokens dimension: | |
| # [batch, 1, key_tokens] | |
| # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: | |
| # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) | |
| # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) | |
| if attention_mask is not None and attention_mask.ndim == 2: | |
| # assume that mask is expressed as: | |
| # (1 = keep, 0 = discard) | |
| # convert mask into a bias that can be added to attention scores: | |
| # (keep = +0, discard = -10000.0) | |
| attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0 | |
| attention_mask = attention_mask.unsqueeze(1) | |
| # convert encoder_attention_mask to a bias the same way we do for attention_mask | |
| if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2: | |
| encoder_attention_mask = (1 - encoder_attention_mask.to(hidden_states.dtype)) * -10000.0 | |
| encoder_attention_mask = encoder_attention_mask.unsqueeze(1) | |
| # 1. Input | |
| if self.is_input_continuous: | |
| batch, _, height, width = hidden_states.shape | |
| residual = hidden_states | |
| hidden_states = self.norm(hidden_states) | |
| if not self.use_linear_projection: | |
| hidden_states = self.proj_in(hidden_states) | |
| inner_dim = hidden_states.shape[1] | |
| hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) | |
| else: | |
| inner_dim = hidden_states.shape[1] | |
| hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) | |
| hidden_states = self.proj_in(hidden_states) | |
| elif self.is_input_vectorized: | |
| hidden_states = self.latent_image_embedding(hidden_states) | |
| elif self.is_input_patches: | |
| hidden_states = self.pos_embed(hidden_states) | |
| base_attn_key = cross_attention_kwargs["attn_key"] | |
| # 2. Blocks | |
| cross_attention_probs_all = [] | |
| for block_ind, block in enumerate(self.transformer_blocks): | |
| cross_attention_kwargs["attn_key"] = base_attn_key + [block_ind] | |
| hidden_states = block( | |
| hidden_states, | |
| attention_mask=attention_mask, | |
| encoder_hidden_states=encoder_hidden_states, | |
| encoder_attention_mask=encoder_attention_mask, | |
| timestep=timestep, | |
| cross_attention_kwargs=cross_attention_kwargs, | |
| class_labels=class_labels, | |
| return_cross_attention_probs=return_cross_attention_probs, | |
| ) | |
| if return_cross_attention_probs: | |
| hidden_states, cross_attention_probs = hidden_states | |
| cross_attention_probs_all.append(cross_attention_probs) | |
| # 3. Output | |
| if self.is_input_continuous: | |
| if not self.use_linear_projection: | |
| hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() | |
| hidden_states = self.proj_out(hidden_states) | |
| else: | |
| hidden_states = self.proj_out(hidden_states) | |
| hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() | |
| output = hidden_states + residual | |
| elif self.is_input_vectorized: | |
| hidden_states = self.norm_out(hidden_states) | |
| logits = self.out(hidden_states) | |
| # (batch, self.num_vector_embeds - 1, self.num_latent_pixels) | |
| logits = logits.permute(0, 2, 1) | |
| # log(p(x_0)) | |
| output = F.log_softmax(logits.double(), dim=1).float() | |
| elif self.is_input_patches: | |
| # TODO: cleanup! | |
| conditioning = self.transformer_blocks[0].norm1.emb( | |
| timestep, class_labels, hidden_dtype=hidden_states.dtype | |
| ) | |
| shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1) | |
| hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None] | |
| hidden_states = self.proj_out_2(hidden_states) | |
| # unpatchify | |
| height = width = int(hidden_states.shape[1] ** 0.5) | |
| hidden_states = hidden_states.reshape( | |
| shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) | |
| ) | |
| hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) | |
| output = hidden_states.reshape( | |
| shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) | |
| ) | |
| if len(cross_attention_probs_all) == 1: | |
| # If we only have one transformer block in a Transformer2DModel, we do not create another nested level. | |
| cross_attention_probs_all = cross_attention_probs_all[0] | |
| if not return_dict: | |
| if return_cross_attention_probs: | |
| return (output, cross_attention_probs_all) | |
| return (output,) | |
| output = Transformer2DModelOutput(sample=output) | |
| if return_cross_attention_probs: | |
| return output, cross_attention_probs_all | |
| return output | |