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379 lines (295 loc) · 12.6 KB
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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.
import math
from typing import Optional
import numpy as np
import torch
from torch import nn
def get_timestep_embedding(
timesteps: torch.Tensor,
embedding_dim: int,
flip_sin_to_cos: bool = False,
downscale_freq_shift: float = 1,
scale: float = 1,
max_period: int = 10000,
):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings.
:param timesteps: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param embedding_dim: the dimension of the output. :param max_period: controls the minimum frequency of the
embeddings. :return: an [N x dim] Tensor of positional embeddings.
"""
assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array"
half_dim = embedding_dim // 2
exponent = -math.log(max_period) * torch.arange(
start=0, end=half_dim, dtype=torch.float32, device=timesteps.device
)
exponent = exponent / (half_dim - downscale_freq_shift)
emb = torch.exp(exponent)
emb = timesteps[:, None].float() * emb[None, :]
# scale embeddings
emb = scale * emb
# concat sine and cosine embeddings
emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1)
# flip sine and cosine embeddings
if flip_sin_to_cos:
emb = torch.cat([emb[:, half_dim:], emb[:, :half_dim]], dim=-1)
# zero pad
if embedding_dim % 2 == 1:
emb = torch.nn.functional.pad(emb, (0, 1, 0, 0))
return emb
def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0):
"""
grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or
[1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
"""
grid_h = np.arange(grid_size, dtype=np.float32)
grid_w = np.arange(grid_size, dtype=np.float32)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size, grid_size])
pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
if cls_token and extra_tokens > 0:
pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
return pos_embed
def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
if embed_dim % 2 != 0:
raise ValueError("embed_dim must be divisible by 2")
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
return emb
def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D)
"""
if embed_dim % 2 != 0:
raise ValueError("embed_dim must be divisible by 2")
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.0
omega = 1.0 / 10000**omega # (D/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
return emb
class PatchEmbed(nn.Module):
"""2D Image to Patch Embedding"""
def __init__(
self,
height=224,
width=224,
patch_size=16,
in_channels=3,
embed_dim=768,
layer_norm=False,
flatten=True,
bias=True,
):
super().__init__()
num_patches = (height // patch_size) * (width // patch_size)
self.flatten = flatten
self.layer_norm = layer_norm
self.proj = nn.Conv2d(
in_channels, embed_dim, kernel_size=(patch_size, patch_size), stride=patch_size, bias=bias
)
if layer_norm:
self.norm = nn.LayerNorm(embed_dim, elementwise_affine=False, eps=1e-6)
else:
self.norm = None
pos_embed = get_2d_sincos_pos_embed(embed_dim, int(num_patches**0.5))
self.register_buffer("pos_embed", torch.from_numpy(pos_embed).float().unsqueeze(0), persistent=False)
def forward(self, latent):
latent = self.proj(latent)
if self.flatten:
latent = latent.flatten(2).transpose(1, 2) # BCHW -> BNC
if self.layer_norm:
latent = self.norm(latent)
return latent + self.pos_embed
class TimestepEmbedding(nn.Module):
def __init__(
self,
in_channels: int,
time_embed_dim: int,
act_fn: str = "silu",
out_dim: int = None,
post_act_fn: Optional[str] = None,
cond_proj_dim=None,
):
super().__init__()
self.linear_1 = nn.Linear(in_channels, time_embed_dim)
if cond_proj_dim is not None:
self.cond_proj = nn.Linear(cond_proj_dim, in_channels, bias=False)
else:
self.cond_proj = None
if act_fn == "silu":
self.act = nn.SiLU()
elif act_fn == "mish":
self.act = nn.Mish()
elif act_fn == "gelu":
self.act = nn.GELU()
else:
raise ValueError(f"{act_fn} does not exist. Make sure to define one of 'silu', 'mish', or 'gelu'")
if out_dim is not None:
time_embed_dim_out = out_dim
else:
time_embed_dim_out = time_embed_dim
self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim_out)
if post_act_fn is None:
self.post_act = None
elif post_act_fn == "silu":
self.post_act = nn.SiLU()
elif post_act_fn == "mish":
self.post_act = nn.Mish()
elif post_act_fn == "gelu":
self.post_act = nn.GELU()
else:
raise ValueError(f"{post_act_fn} does not exist. Make sure to define one of 'silu', 'mish', or 'gelu'")
def forward(self, sample, condition=None):
if condition is not None:
sample = sample + self.cond_proj(condition)
sample = self.linear_1(sample)
if self.act is not None:
sample = self.act(sample)
sample = self.linear_2(sample)
if self.post_act is not None:
sample = self.post_act(sample)
return sample
class Timesteps(nn.Module):
def __init__(self, num_channels: int, flip_sin_to_cos: bool, downscale_freq_shift: float):
super().__init__()
self.num_channels = num_channels
self.flip_sin_to_cos = flip_sin_to_cos
self.downscale_freq_shift = downscale_freq_shift
def forward(self, timesteps):
t_emb = get_timestep_embedding(
timesteps,
self.num_channels,
flip_sin_to_cos=self.flip_sin_to_cos,
downscale_freq_shift=self.downscale_freq_shift,
)
return t_emb
class GaussianFourierProjection(nn.Module):
"""Gaussian Fourier embeddings for noise levels."""
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
self.W = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)
self.weight = self.W
def forward(self, x):
if self.log:
x = torch.log(x)
x_proj = x[:, None] * self.weight[None, :] * 2 * np.pi
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
class ImagePositionalEmbeddings(nn.Module):
"""
Converts latent image classes into vector embeddings. Sums the vector embeddings with positional embeddings for the
height and width of the latent space.
For more details, see figure 10 of the dall-e paper: https://arxiv.org/abs/2102.12092
For VQ-diffusion:
Output vector embeddings are used as input for the transformer.
Note that the vector embeddings for the transformer are different than the vector embeddings from the VQVAE.
Args:
num_embed (`int`):
Number of embeddings for the latent pixels embeddings.
height (`int`):
Height of the latent image i.e. the number of height embeddings.
width (`int`):
Width of the latent image i.e. the number of width embeddings.
embed_dim (`int`):
Dimension of the produced vector embeddings. Used for the latent pixel, height, and width embeddings.
"""
def __init__(
self,
num_embed: int,
height: int,
width: int,
embed_dim: int,
):
super().__init__()
self.height = height
self.width = width
self.num_embed = num_embed
self.embed_dim = embed_dim
self.emb = nn.Embedding(self.num_embed, embed_dim)
self.height_emb = nn.Embedding(self.height, embed_dim)
self.width_emb = nn.Embedding(self.width, embed_dim)
def forward(self, index):
emb = self.emb(index)
height_emb = self.height_emb(torch.arange(self.height, device=index.device).view(1, self.height))
# 1 x H x D -> 1 x H x 1 x D
height_emb = height_emb.unsqueeze(2)
width_emb = self.width_emb(torch.arange(self.width, device=index.device).view(1, self.width))
# 1 x W x D -> 1 x 1 x W x D
width_emb = width_emb.unsqueeze(1)
pos_emb = height_emb + width_emb
# 1 x H x W x D -> 1 x L xD
pos_emb = pos_emb.view(1, self.height * self.width, -1)
emb = emb + pos_emb[:, : emb.shape[1], :]
return emb
class LabelEmbedding(nn.Module):
"""
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
Args:
num_classes (`int`): The number of classes.
hidden_size (`int`): The size of the vector embeddings.
dropout_prob (`float`): The probability of dropping a label.
"""
def __init__(self, num_classes, hidden_size, dropout_prob):
super().__init__()
use_cfg_embedding = dropout_prob > 0
self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
self.num_classes = num_classes
self.dropout_prob = dropout_prob
def token_drop(self, labels, force_drop_ids=None):
"""
Drops labels to enable classifier-free guidance.
"""
if force_drop_ids is None:
drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob
else:
drop_ids = torch.tensor(force_drop_ids == 1)
labels = torch.where(drop_ids, self.num_classes, labels)
return labels
def forward(self, labels, force_drop_ids=None):
use_dropout = self.dropout_prob > 0
if (self.training and use_dropout) or (force_drop_ids is not None):
labels = self.token_drop(labels, force_drop_ids)
embeddings = self.embedding_table(labels)
return embeddings
class CombinedTimestepLabelEmbeddings(nn.Module):
def __init__(self, num_classes, embedding_dim, class_dropout_prob=0.1):
super().__init__()
self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=1)
self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
self.class_embedder = LabelEmbedding(num_classes, embedding_dim, class_dropout_prob)
def forward(self, timestep, class_labels, hidden_dtype=None):
timesteps_proj = self.time_proj(timestep)
timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype)) # (N, D)
class_labels = self.class_embedder(class_labels) # (N, D)
conditioning = timesteps_emb + class_labels # (N, D)
return conditioning
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