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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 ..configuration_utils import ConfigMixin, register_to_config | |
from ..models.embeddings import ImagePositionalEmbeddings | |
from ..utils import USE_PEFT_BACKEND, BaseOutput, deprecate, is_torch_version | |
from .attention import BasicTransformerBlock | |
from .embeddings import PatchEmbed, PixArtAlphaTextProjection | |
from .lora import LoRACompatibleConv, LoRACompatibleLinear | |
from .modeling_utils import ModelMixin | |
from .normalization import AdaLayerNormSingle | |
class Transformer2DModelOutput(BaseOutput): | |
""" | |
The output of [`Transformer2DModel`]. | |
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): | |
The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability | |
distributions for the unnoised latent pixels. | |
""" | |
sample: torch.FloatTensor | |
class Transformer2DModel(ModelMixin, ConfigMixin): | |
""" | |
A 2D Transformer model for image-like data. | |
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*): | |
The number of channels in the input and output (specify if the input is **continuous**). | |
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*): The width of the latent images (specify if the input is **discrete**). | |
This is fixed during training since it is used to learn a number of position embeddings. | |
num_vector_embeds (`int`, *optional*): | |
The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**). | |
Includes the class for the masked latent pixel. | |
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. | |
num_embeds_ada_norm ( `int`, *optional*): | |
The number of diffusion steps used during training. Pass if at least one of the norm_layers is | |
`AdaLayerNorm`. This is fixed during training since 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 steps than `num_embeds_ada_norm`. | |
attention_bias (`bool`, *optional*): | |
Configure if the `TransformerBlocks` attention should contain a bias parameter. | |
""" | |
_supports_gradient_checkpointing = True | |
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, | |
double_self_attention: bool = False, | |
upcast_attention: bool = False, | |
norm_type: str = "layer_norm", | |
norm_elementwise_affine: bool = True, | |
norm_eps: float = 1e-5, | |
attention_type: str = "default", | |
caption_channels: int = None, | |
): | |
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 | |
conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv | |
linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear | |
# 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 = linear_cls(in_channels, inner_dim) | |
else: | |
self.proj_in = conv_cls(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 | |
interpolation_scale = self.config.sample_size // 64 # => 64 (= 512 pixart) has interpolation scale 1 | |
interpolation_scale = max(interpolation_scale, 1) | |
self.pos_embed = PatchEmbed( | |
height=sample_size, | |
width=sample_size, | |
patch_size=patch_size, | |
in_channels=in_channels, | |
embed_dim=inner_dim, | |
interpolation_scale=interpolation_scale, | |
) | |
# 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, | |
double_self_attention=double_self_attention, | |
upcast_attention=upcast_attention, | |
norm_type=norm_type, | |
norm_elementwise_affine=norm_elementwise_affine, | |
norm_eps=norm_eps, | |
attention_type=attention_type, | |
) | |
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 = linear_cls(inner_dim, in_channels) | |
else: | |
self.proj_out = conv_cls(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 and norm_type != "ada_norm_single": | |
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) | |
elif self.is_input_patches and norm_type == "ada_norm_single": | |
self.norm_out = nn.LayerNorm(inner_dim, elementwise_affine=False, eps=1e-6) | |
self.scale_shift_table = nn.Parameter(torch.randn(2, inner_dim) / inner_dim**0.5) | |
self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * self.out_channels) | |
# 5. PixArt-Alpha blocks. | |
self.adaln_single = None | |
self.use_additional_conditions = False | |
if norm_type == "ada_norm_single": | |
self.use_additional_conditions = self.config.sample_size == 128 | |
# TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use | |
# additional conditions until we find better name | |
self.adaln_single = AdaLayerNormSingle(inner_dim, use_additional_conditions=self.use_additional_conditions) | |
self.caption_projection = None | |
if caption_channels is not None: | |
self.caption_projection = PixArtAlphaTextProjection(in_features=caption_channels, hidden_size=inner_dim) | |
self.gradient_checkpointing = False | |
def _set_gradient_checkpointing(self, module, value=False): | |
if hasattr(module, "gradient_checkpointing"): | |
module.gradient_checkpointing = value | |
def forward( | |
self, | |
hidden_states: torch.Tensor, | |
encoder_hidden_states: Optional[torch.Tensor] = None, | |
timestep: Optional[torch.LongTensor] = None, | |
added_cond_kwargs: Dict[str, torch.Tensor] = 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, | |
): | |
""" | |
The [`Transformer2DModel`] forward method. | |
Args: | |
hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous): | |
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*): | |
Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. | |
class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): | |
Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in | |
`AdaLayerZeroNorm`. | |
cross_attention_kwargs ( `Dict[str, Any]`, *optional*): | |
A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under | |
`self.processor` in | |
[diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). | |
attention_mask ( `torch.Tensor`, *optional*): | |
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask | |
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large | |
negative values to the attention scores corresponding to "discard" tokens. | |
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.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain | |
tuple. | |
Returns: | |
If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a | |
`tuple` where 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) | |
# Retrieve lora scale. | |
lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 | |
# 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, scale=lora_scale) | |
if not USE_PEFT_BACKEND | |
else 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, scale=lora_scale) | |
if not USE_PEFT_BACKEND | |
else self.proj_in(hidden_states) | |
) | |
elif self.is_input_vectorized: | |
hidden_states = self.latent_image_embedding(hidden_states) | |
elif self.is_input_patches: | |
height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size | |
hidden_states = self.pos_embed(hidden_states) | |
if self.adaln_single is not None: | |
if self.use_additional_conditions and added_cond_kwargs is None: | |
raise ValueError( | |
"`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`." | |
) | |
batch_size = hidden_states.shape[0] | |
timestep, embedded_timestep = self.adaln_single( | |
timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype | |
) | |
# 2. Blocks | |
if self.caption_projection is not None: | |
batch_size = hidden_states.shape[0] | |
encoder_hidden_states = self.caption_projection(encoder_hidden_states) | |
encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1]) | |
for block in self.transformer_blocks: | |
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) | |
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(block), | |
hidden_states, | |
attention_mask, | |
encoder_hidden_states, | |
encoder_attention_mask, | |
timestep, | |
cross_attention_kwargs, | |
class_labels, | |
**ckpt_kwargs, | |
) | |
else: | |
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, | |
) | |
# 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, scale=lora_scale) | |
if not USE_PEFT_BACKEND | |
else self.proj_out(hidden_states) | |
) | |
else: | |
hidden_states = ( | |
self.proj_out(hidden_states, scale=lora_scale) | |
if not USE_PEFT_BACKEND | |
else 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() | |
if self.is_input_patches: | |
if self.config.norm_type != "ada_norm_single": | |
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) | |
elif self.config.norm_type == "ada_norm_single": | |
shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1) | |
hidden_states = self.norm_out(hidden_states) | |
# Modulation | |
hidden_states = hidden_states * (1 + scale) + shift | |
hidden_states = self.proj_out(hidden_states) | |
hidden_states = hidden_states.squeeze(1) | |
# unpatchify | |
if self.adaln_single is None: | |
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 not return_dict: | |
return (output,) | |
return Transformer2DModelOutput(sample=output) | |