Spaces:
Running
on
Zero
Running
on
Zero
File size: 19,315 Bytes
bfa59ab |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 |
# Copyright 2024 Stability AI and 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 typing import Any, Dict, Optional, Union
import numpy as np
import torch
import torch.nn as nn
import torch.utils.checkpoint
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.attention import FeedForward
from ...models.attention_processor import (
Attention,
AttentionProcessor,
StableAudioAttnProcessor2_0,
)
from ...models.modeling_utils import ModelMixin
from ...models.transformers.transformer_2d import Transformer2DModelOutput
from ...utils import is_torch_version, logging
from ...utils.torch_utils import maybe_allow_in_graph
logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class StableAudioGaussianFourierProjection(nn.Module):
"""Gaussian Fourier embeddings for noise levels."""
# Copied from diffusers.models.embeddings.GaussianFourierProjection.__init__
def __init__(
self, embedding_size: int = 256, scale: float = 1.0, set_W_to_weight=True, log=True, flip_sin_to_cos=False
):
super().__init__()
self.weight = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
self.log = log
self.flip_sin_to_cos = flip_sin_to_cos
if set_W_to_weight:
# to delete later
del self.weight
self.W = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
self.weight = self.W
del self.W
def forward(self, x):
if self.log:
x = torch.log(x)
x_proj = 2 * np.pi * x[:, None] @ self.weight[None, :]
if self.flip_sin_to_cos:
out = torch.cat([torch.cos(x_proj), torch.sin(x_proj)], dim=-1)
else:
out = torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)
return out
@maybe_allow_in_graph
class StableAudioDiTBlock(nn.Module):
r"""
Transformer block used in Stable Audio model (https://github.com/Stability-AI/stable-audio-tools). Allow skip
connection and QKNorm
Parameters:
dim (`int`): The number of channels in the input and output.
num_attention_heads (`int`): The number of heads to use for the query states.
num_key_value_attention_heads (`int`): The number of heads to use for the key and value states.
attention_head_dim (`int`): The number of channels in each head.
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
upcast_attention (`bool`, *optional*):
Whether to upcast the attention computation to float32. This is useful for mixed precision training.
"""
def __init__(
self,
dim: int,
num_attention_heads: int,
num_key_value_attention_heads: int,
attention_head_dim: int,
dropout=0.0,
cross_attention_dim: Optional[int] = None,
upcast_attention: bool = False,
norm_eps: float = 1e-5,
ff_inner_dim: Optional[int] = None,
):
super().__init__()
# Define 3 blocks. Each block has its own normalization layer.
# 1. Self-Attn
self.norm1 = nn.LayerNorm(dim, elementwise_affine=True, eps=norm_eps)
self.attn1 = Attention(
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=False,
upcast_attention=upcast_attention,
out_bias=False,
processor=StableAudioAttnProcessor2_0(),
)
# 2. Cross-Attn
self.norm2 = nn.LayerNorm(dim, norm_eps, True)
self.attn2 = Attention(
query_dim=dim,
cross_attention_dim=cross_attention_dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
kv_heads=num_key_value_attention_heads,
dropout=dropout,
bias=False,
upcast_attention=upcast_attention,
out_bias=False,
processor=StableAudioAttnProcessor2_0(),
) # is self-attn if encoder_hidden_states is none
# 3. Feed-forward
self.norm3 = nn.LayerNorm(dim, norm_eps, True)
self.ff = FeedForward(
dim,
dropout=dropout,
activation_fn="swiglu",
final_dropout=False,
inner_dim=ff_inner_dim,
bias=True,
)
# let chunk size default to None
self._chunk_size = None
self._chunk_dim = 0
def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int = 0):
# Sets chunk feed-forward
self._chunk_size = chunk_size
self._chunk_dim = dim
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
rotary_embedding: Optional[torch.FloatTensor] = None,
) -> torch.Tensor:
# Notice that normalization is always applied before the real computation in the following blocks.
# 0. Self-Attention
norm_hidden_states = self.norm1(hidden_states)
attn_output = self.attn1(
norm_hidden_states,
attention_mask=attention_mask,
rotary_emb=rotary_embedding,
)
hidden_states = attn_output + hidden_states
# 2. Cross-Attention
norm_hidden_states = self.norm2(hidden_states)
attn_output = self.attn2(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
)
hidden_states = attn_output + hidden_states
# 3. Feed-forward
norm_hidden_states = self.norm3(hidden_states)
ff_output = self.ff(norm_hidden_states)
hidden_states = ff_output + hidden_states
return hidden_states
class StableAudioDiTModel(ModelMixin, ConfigMixin):
"""
The Diffusion Transformer model introduced in Stable Audio.
Reference: https://github.com/Stability-AI/stable-audio-tools
Parameters:
sample_size ( `int`, *optional*, defaults to 1024): The size of the input sample.
in_channels (`int`, *optional*, defaults to 64): The number of channels in the input.
num_layers (`int`, *optional*, defaults to 24): The number of layers of Transformer blocks to use.
attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
num_attention_heads (`int`, *optional*, defaults to 24): The number of heads to use for the query states.
num_key_value_attention_heads (`int`, *optional*, defaults to 12):
The number of heads to use for the key and value states.
out_channels (`int`, defaults to 64): Number of output channels.
cross_attention_dim ( `int`, *optional*, defaults to 768): Dimension of the cross-attention projection.
time_proj_dim ( `int`, *optional*, defaults to 256): Dimension of the timestep inner projection.
global_states_input_dim ( `int`, *optional*, defaults to 1536):
Input dimension of the global hidden states projection.
cross_attention_input_dim ( `int`, *optional*, defaults to 768):
Input dimension of the cross-attention projection
"""
_supports_gradient_checkpointing = True
@register_to_config
def __init__(
self,
sample_size: int = 1024,
in_channels: int = 64,
num_layers: int = 24,
attention_head_dim: int = 64,
num_attention_heads: int = 24,
num_key_value_attention_heads: int = 12,
out_channels: int = 64,
cross_attention_dim: int = 768,
time_proj_dim: int = 256,
global_states_input_dim: int = 1536,
cross_attention_input_dim: int = 768,
):
super().__init__()
self.sample_size = sample_size
self.out_channels = out_channels
self.inner_dim = num_attention_heads * attention_head_dim
self.time_proj = StableAudioGaussianFourierProjection(
embedding_size=time_proj_dim // 2,
flip_sin_to_cos=True,
log=False,
set_W_to_weight=False,
)
self.timestep_proj = nn.Sequential(
nn.Linear(time_proj_dim, self.inner_dim, bias=True),
nn.SiLU(),
nn.Linear(self.inner_dim, self.inner_dim, bias=True),
)
self.global_proj = nn.Sequential(
nn.Linear(global_states_input_dim, self.inner_dim, bias=False),
nn.SiLU(),
nn.Linear(self.inner_dim, self.inner_dim, bias=False),
)
self.cross_attention_proj = nn.Sequential(
nn.Linear(cross_attention_input_dim, cross_attention_dim, bias=False),
nn.SiLU(),
nn.Linear(cross_attention_dim, cross_attention_dim, bias=False),
)
self.preprocess_conv = nn.Conv1d(in_channels, in_channels, 1, bias=False)
self.proj_in = nn.Linear(in_channels, self.inner_dim, bias=False)
self.transformer_blocks = nn.ModuleList(
[
StableAudioDiTBlock(
dim=self.inner_dim,
num_attention_heads=num_attention_heads,
num_key_value_attention_heads=num_key_value_attention_heads,
attention_head_dim=attention_head_dim,
cross_attention_dim=cross_attention_dim,
)
for i in range(num_layers)
]
)
self.proj_out = nn.Linear(self.inner_dim, self.out_channels, bias=False)
self.postprocess_conv = nn.Conv1d(self.out_channels, self.out_channels, 1, bias=False)
self.gradient_checkpointing = False
@property
# Copied from diffusers.models.unets.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, "get_processor"):
processors[f"{name}.processor"] = module.get_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.unets.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.transformers.hunyuan_transformer_2d.HunyuanDiT2DModel.set_default_attn_processor with Hunyuan->StableAudio
def set_default_attn_processor(self):
"""
Disables custom attention processors and sets the default attention implementation.
"""
self.set_attn_processor(StableAudioAttnProcessor2_0())
def _set_gradient_checkpointing(self, module, value=False):
if hasattr(module, "gradient_checkpointing"):
module.gradient_checkpointing = value
def forward(
self,
hidden_states: torch.FloatTensor,
timestep: torch.LongTensor = None,
encoder_hidden_states: torch.FloatTensor = None,
global_hidden_states: torch.FloatTensor = None,
rotary_embedding: torch.FloatTensor = None,
return_dict: bool = True,
attention_mask: Optional[torch.LongTensor] = None,
encoder_attention_mask: Optional[torch.LongTensor] = None,
) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
"""
The [`StableAudioDiTModel`] forward method.
Args:
hidden_states (`torch.FloatTensor` of shape `(batch size, in_channels, sequence_len)`):
Input `hidden_states`.
timestep ( `torch.LongTensor`):
Used to indicate denoising step.
encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, encoder_sequence_len, cross_attention_input_dim)`):
Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
global_hidden_states (`torch.FloatTensor` of shape `(batch size, global_sequence_len, global_states_input_dim)`):
Global embeddings that will be prepended to the hidden states.
rotary_embedding (`torch.Tensor`):
The rotary embeddings to apply on query and key tensors during attention calculation.
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
tuple.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_len)`, *optional*):
Mask to avoid performing attention on padding token indices, formed by concatenating the attention
masks
for the two text encoders together. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
encoder_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_len)`, *optional*):
Mask to avoid performing attention on padding token cross-attention indices, formed by concatenating
the attention masks
for the two text encoders together. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
Returns:
If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
`tuple` where the first element is the sample tensor.
"""
cross_attention_hidden_states = self.cross_attention_proj(encoder_hidden_states)
global_hidden_states = self.global_proj(global_hidden_states)
time_hidden_states = self.timestep_proj(self.time_proj(timestep.to(self.dtype)))
global_hidden_states = global_hidden_states + time_hidden_states.unsqueeze(1)
hidden_states = self.preprocess_conv(hidden_states) + hidden_states
# (batch_size, dim, sequence_length) -> (batch_size, sequence_length, dim)
hidden_states = hidden_states.transpose(1, 2)
hidden_states = self.proj_in(hidden_states)
# prepend global states to hidden states
hidden_states = torch.cat([global_hidden_states, hidden_states], dim=-2)
if attention_mask is not None:
prepend_mask = torch.ones((hidden_states.shape[0], 1), device=hidden_states.device, dtype=torch.bool)
attention_mask = torch.cat([prepend_mask, attention_mask], dim=-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,
cross_attention_hidden_states,
encoder_attention_mask,
rotary_embedding,
**ckpt_kwargs,
)
else:
hidden_states = block(
hidden_states=hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=cross_attention_hidden_states,
encoder_attention_mask=encoder_attention_mask,
rotary_embedding=rotary_embedding,
)
hidden_states = self.proj_out(hidden_states)
# (batch_size, sequence_length, dim) -> (batch_size, dim, sequence_length)
# remove prepend length that has been added by global hidden states
hidden_states = hidden_states.transpose(1, 2)[:, :, 1:]
hidden_states = self.postprocess_conv(hidden_states) + hidden_states
if not return_dict:
return (hidden_states,)
return Transformer2DModelOutput(sample=hidden_states)
|