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PixArt.py 0 → 100644
View file @ a44e71bd
from abc import abstractmethod
from functools import partial
import math
from typing import Iterable
import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from timm.models.vision_transformer import Attention, Mlp
from positional_encodings.torch_encodings import PositionalEncoding1D
from timm.models.layers import DropPath
from .utils import auto_grad_checkpoint, to_2tuple
from .PixArt_blocks import (
t2i_modulate,
WindowAttention,
MultiHeadCrossAttention,
T2IFinalLayer,
TimestepEmbedder,
FinalLayer,
)
import math
class PatchEmbed(nn.Module):
"""2D Image to Patch Embedding"""
def __init__(
self,
img_size=(256, 16),
patch_size=(16, 4),
overlap=(0, 0),
in_chans=128,
embed_dim=768,
norm_layer=None,
flatten=True,
bias=True,
):
super().__init__()
self.img_size = img_size
self.patch_size = patch_size
self.ol = overlap
self.grid_size = (
math.ceil((img_size[0] - patch_size[0]) / (patch_size[0] - overlap[0])) + 1,
math.ceil((img_size[1] - patch_size[1]) / (patch_size[1] - overlap[1])) + 1,
)
self.pad_size = (
(self.grid_size[0] - 1) * (self.patch_size[0] - overlap[0])
+ self.patch_size[0]
- self.img_size[0],
+(self.grid_size[1] - 1) * (self.patch_size[1] - overlap[1])
+ self.patch_size[1]
- self.img_size[1],
)
self.pad_size = (self.pad_size[0] // 2, self.pad_size[1] // 2)
self.num_patches = self.grid_size[0] * self.grid_size[1]
self.flatten = flatten
self.proj = nn.Conv2d(
in_chans,
embed_dim,
kernel_size=patch_size,
stride=(patch_size[0] - overlap[0], patch_size[1] - overlap[1]),
bias=bias,
)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
x = F.pad(
x,
(
self.pad_size[-1],
self.pad_size[-1],
self.pad_size[-2],
self.pad_size[-2],
),
"constant",
0,
)
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
x = self.norm(x)
return x
class PatchEmbed_1D(nn.Module):
def __init__(
self,
img_size=(256, 16),
in_chans=8,
embed_dim=1152,
norm_layer=None,
bias=True,
):
super().__init__()
self.proj = nn.Linear(in_chans * img_size[1], embed_dim, bias=bias)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
x = th.einsum("bctf->btfc", x)
x = x.flatten(2) # BTFC -> BTD
x = self.proj(x)
x = self.norm(x)
return x
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
def t2i_modulate(x, shift, scale):
return x * (1 + scale) + shift
class PixArtBlock(nn.Module):
"""
A PixArt block with adaptive layer norm (adaLN-single) conditioning.
"""
def __init__(
self,
hidden_size,
num_heads,
mlp_ratio=4.0,
drop_path=0.0,
window_size=0,
input_size=None,
use_rel_pos=False,
**block_kwargs
):
super().__init__()
self.hidden_size = hidden_size
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = WindowAttention(
hidden_size,
num_heads=num_heads,
qkv_bias=True,
input_size=input_size if window_size == 0 else (window_size, window_size),
use_rel_pos=use_rel_pos,
**block_kwargs
)
self.cross_attn = MultiHeadCrossAttention(
hidden_size, num_heads, **block_kwargs
)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
# to be compatible with lower version pytorch
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.mlp = Mlp(
in_features=hidden_size,
hidden_features=int(hidden_size * mlp_ratio),
act_layer=approx_gelu,
drop=0,
)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.window_size = window_size
self.scale_shift_table = nn.Parameter(
th.randn(6, hidden_size) / hidden_size**0.5
)
def forward(self, x, y, t, mask=None, **kwargs):
B, N, C = x.shape
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
self.scale_shift_table[None] + t.reshape(B, 6, -1)
).chunk(6, dim=1)
x = x + self.drop_path(
gate_msa
* self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa)).reshape(
B, N, C
)
)
x = x + self.cross_attn(x, y, mask)
x = x + self.drop_path(
gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))
)
return x
from ldm.modules.diffusionmodules.attention import CrossAttention_1D
class PixArtBlock_Slow(nn.Module):
"""
A PixArt block with adaptive layer norm (adaLN-single) conditioning.
"""
def __init__(
self,
hidden_size,
num_heads,
mlp_ratio=4.0,
drop_path=0.0,
window_size=0,
input_size=None,
use_rel_pos=False,
**block_kwargs
):
super().__init__()
self.hidden_size = hidden_size
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = CrossAttention_1D(
query_dim=hidden_size,
context_dim=hidden_size,
heads=num_heads,
dim_head=int(hidden_size / num_heads),
)
self.cross_attn = CrossAttention_1D(
query_dim=hidden_size,
context_dim=hidden_size,
heads=num_heads,
dim_head=int(hidden_size / num_heads),
)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
# to be compatible with lower version pytorch
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.mlp = Mlp(
in_features=hidden_size,
hidden_features=int(hidden_size * mlp_ratio),
act_layer=approx_gelu,
drop=0,
)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.window_size = window_size
self.scale_shift_table = nn.Parameter(
th.randn(6, hidden_size) / hidden_size**0.5
)
def forward(self, x, y, t, mask=None, **kwargs):
B, N, C = x.shape
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
self.scale_shift_table[None] + t.reshape(B, 6, -1)
).chunk(6, dim=1)
x = x + self.drop_path(
gate_msa
* self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa)).reshape(
B, N, C
)
)
x = x + self.cross_attn(x, y, mask)
x = x + self.drop_path(
gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))
)
return x
class PixArt(nn.Module):
"""
Diffusion model with a Transformer backbone.
"""
def __init__(
self,
input_size=(256, 16),
patch_size=(16, 4),
overlap=(0, 0),
in_channels=8,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4.0,
class_dropout_prob=0.1,
pred_sigma=True,
drop_path: float = 0.0,
window_size=0,
window_block_indexes=None,
use_rel_pos=False,
cond_dim=1024,
lewei_scale=1.0,
use_cfg=True,
cfg_scale=4.0,
config=None,
model_max_length=120,
**kwargs
):
if window_block_indexes is None:
window_block_indexes = []
super().__init__()
self.use_cfg = use_cfg
self.cfg_scale = cfg_scale
self.input_size = input_size
self.pred_sigma = pred_sigma
self.in_channels = in_channels
self.out_channels = in_channels * 2 if pred_sigma else in_channels
self.patch_size = patch_size
self.num_heads = num_heads
self.lewei_scale = (lewei_scale,)
self.x_embedder = PatchEmbed(
input_size, patch_size, overlap, in_channels, hidden_size, bias=True
)
self.t_embedder = TimestepEmbedder(hidden_size)
num_patches = self.x_embedder.num_patches
self.base_size = input_size[0] // self.patch_size[0] * 2
# Will use fixed sin-cos embedding:
self.register_buffer("pos_embed", th.zeros(1, num_patches, hidden_size))
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.t_block = nn.Sequential(
nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True)
)
self.y_embedder = nn.Linear(cond_dim, hidden_size)
drop_path = [
x.item() for x in th.linspace(0, drop_path, depth)
] # stochastic depth decay rule
self.blocks = nn.ModuleList(
[
PixArtBlock(
hidden_size,
num_heads,
mlp_ratio=mlp_ratio,
drop_path=drop_path[i],
input_size=(
self.x_embedder.grid_size[0],
self.x_embedder.grid_size[1],
),
window_size=0,
use_rel_pos=False,
)
for i in range(depth)
]
)
self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
self.initialize_weights()
def forward(self, x, timestep, context_list, context_mask_list=None, **kwargs):
"""
Forward pass of PixArt.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
y: (N, 1, 120, C) tensor of class labels
"""
x = x.to(self.dtype)
timestep = timestep.to(self.dtype)
y = context_list[0].to(self.dtype)
pos_embed = self.pos_embed.to(self.dtype)
self.h, self.w = self.x_embedder.grid_size[0], self.x_embedder.grid_size[1]
x = self.x_embedder(x) + pos_embed
t = self.t_embedder(timestep.to(x.dtype))
t0 = self.t_block(t)
y = self.y_embedder(y)
mask = context_mask_list[0]
assert mask is not None
# if mask is not None:
y = y.masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
y_lens = mask.sum(dim=1).tolist()
y_lens = [int(_) for _ in y_lens]
for block in self.blocks:
x = auto_grad_checkpoint(block, x, y, t0, y_lens)
x = self.final_layer(x, t)
x = self.unpatchify(x)
return x
def forward_with_dpmsolver(self, x, timestep, y, mask=None, **kwargs):
"""
dpm solver donnot need variance prediction
"""
# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
model_out = self.forward(x, timestep, y, mask)
return model_out.chunk(2, dim=1)[0]
def forward_with_cfg(self, x, timestep, y, cfg_scale, mask=None, **kwargs):
"""
Forward pass of PixArt, but also batches the unconditional forward pass for classifier-free guidance.
"""
# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
half = x[: len(x) // 2]
combined = th.cat([half, half], dim=0)
model_out = self.forward(combined, timestep, y, mask)
model_out = model_out["x"] if isinstance(model_out, dict) else model_out
eps, rest = model_out[:, :8], model_out[:, 8:]
cond_eps, uncond_eps = th.split(eps, len(eps) // 2, dim=0)
half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
eps = th.cat([half_eps, half_eps], dim=0)
return eps
def unpatchify(self, x):
"""
x: (N, T, patch_size 0 * patch_size 1 * C)
imgs: (Bs. 256. 16. 8)
"""
c = self.out_channels
p0 = self.x_embedder.patch_size[0]
p1 = self.x_embedder.patch_size[1]
h, w = self.x_embedder.grid_size[0], self.x_embedder.grid_size[1]
x = x.reshape(shape=(x.shape[0], h, w, p0, p1, c))
x = th.einsum("nhwpqc->nchpwq", x)
imgs = x.reshape(shape=(x.shape[0], c, h * p0, w * p1))
return imgs
def initialize_weights(self):
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
th.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
# Initialize (and freeze) pos_embed by sin-cos embedding:
pos_embed = get_2d_sincos_pos_embed(
self.pos_embed.shape[-1],
self.x_embedder.grid_size,
lewei_scale=self.lewei_scale,
base_size=self.base_size,
)
self.pos_embed.data.copy_(th.from_numpy(pos_embed).float().unsqueeze(0))
# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
w = self.x_embedder.proj.weight.data
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
# Initialize timestep embedding MLP:
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
nn.init.normal_(self.t_block[1].weight, std=0.02)
# Initialize caption embedding MLP:
nn.init.normal_(self.y_embedder.weight, std=0.02)
# Zero-out adaLN modulation layers in PixArt blocks:
for block in self.blocks:
nn.init.constant_(block.cross_attn.proj.weight, 0)
nn.init.constant_(block.cross_attn.proj.bias, 0)
# Zero-out output layers:
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
@property
def dtype(self):
return next(self.parameters()).dtype
class SwiGLU(nn.Module):
def __init__(
self,
dim: int,
hidden_dim: int,
multiple_of: int,
):
super().__init__()
hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
self.w1 = nn.Linear(dim, hidden_dim, bias=False)
self.w2 = nn.Linear(hidden_dim, dim, bias=False)
self.w3 = nn.Linear(dim, hidden_dim, bias=False)
def forward(self, x):
return self.w2(F.silu(self.w1(x)) * self.w3(x))
class MDTBlock(nn.Module):
"""
A PixArt block with adaptive layer norm (adaLN-single) conditioning.
"""
def __init__(
self,
hidden_size,
num_heads,
mlp_ratio=4.0,
FFN_type="SwiGLU",
drop_path=0.0,
window_size=0,
input_size=None,
use_rel_pos=False,
skip=False,
**block_kwargs
):
super().__init__()
self.hidden_size = hidden_size
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = WindowAttention(
hidden_size,
num_heads=num_heads,
qkv_bias=True,
input_size=input_size if window_size == 0 else (window_size, window_size),
use_rel_pos=use_rel_pos,
**block_kwargs
)
self.cross_attn = MultiHeadCrossAttention(
hidden_size, num_heads, **block_kwargs
)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
# to be compatible with lower version pytorch
approx_gelu = lambda: nn.GELU(approximate="tanh")
if FFN_type == "mlp":
self.mlp = Mlp(
in_features=hidden_size,
hidden_features=int(hidden_size * mlp_ratio),
act_layer=approx_gelu,
drop=0,
)
elif FFN_type == "SwiGLU":
self.mlp = SwiGLU(hidden_size, int(hidden_size * mlp_ratio), 1)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.window_size = window_size
self.scale_shift_table = nn.Parameter(
th.randn(6, hidden_size) / hidden_size**0.5
)
self.skip_linear = nn.Linear(2 * hidden_size, hidden_size) if skip else None
def forward(self, x, y, t, mask=None, skip=None, ids_keep=None, **kwargs):
B, N, C = x.shape
if self.skip_linear is not None:
x = self.skip_linear(th.cat([x, skip], dim=-1))
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
self.scale_shift_table[None] + t.reshape(B, 6, -1)
).chunk(6, dim=1)
x = x + self.drop_path(
gate_msa
* self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa)).reshape(
B, N, C
)
)
x = x + self.cross_attn(x, y, mask)
x = x + self.drop_path(
gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))
)
return x
class DEBlock(nn.Module):
"""
Decoder block with added SpecTNT transformer
"""
def __init__(
self,
hidden_size,
num_heads,
mlp_ratio=4.0,
FFN_type="SwiGLU",
drop_path=0.0,
window_size=0,
input_size=None,
use_rel_pos=False,
skip=False,
num_f=None,
num_t=None,
**block_kwargs
):
super().__init__()
self.hidden_size = hidden_size
self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.attn = WindowAttention(
hidden_size,
num_heads=num_heads,
qkv_bias=True,
input_size=input_size if window_size == 0 else (window_size, window_size),
use_rel_pos=use_rel_pos,
**block_kwargs
)
self.cross_attn = MultiHeadCrossAttention(
hidden_size, num_heads, **block_kwargs
)
self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.norm3 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.norm4 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.norm5 = nn.LayerNorm(
hidden_size * num_f, elementwise_affine=False, eps=1e-6
)
self.norm6 = nn.LayerNorm(
hidden_size * num_f, elementwise_affine=False, eps=1e-6
)
# to be compatible with lower version pytorch
approx_gelu = lambda: nn.GELU(approximate="tanh")
if FFN_type == "mlp":
self.mlp = Mlp(
in_features=hidden_size,
hidden_features=int(hidden_size * mlp_ratio),
act_layer=approx_gelu,
drop=0,
)
elif FFN_type == "SwiGLU":
self.mlp = SwiGLU(hidden_size, int(hidden_size * mlp_ratio), 1)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.window_size = window_size
self.scale_shift_table = nn.Parameter(
th.randn(6, hidden_size) / hidden_size**0.5
)
self.skip_linear = nn.Linear(2 * hidden_size, hidden_size) if skip else None
self.F_transformer = WindowAttention(
hidden_size,
num_heads=4,
qkv_bias=True,
input_size=input_size if window_size == 0 else (window_size, window_size),
use_rel_pos=use_rel_pos,
**block_kwargs
)
self.T_transformer = WindowAttention(
hidden_size * num_f,
num_heads=16,
qkv_bias=True,
input_size=input_size if window_size == 0 else (window_size, window_size),
use_rel_pos=use_rel_pos,
**block_kwargs
)
self.f_pos = nn.Embedding(num_f, hidden_size)
self.t_pos = nn.Embedding(num_t, hidden_size * num_f)
self.num_f = num_f
self.num_t = num_t
def forward(self, x, end, y, t, mask=None, skip=None, ids_keep=None, **kwargs):
B, D, C = x.shape
T = self.num_t
F_add_1 = self.num_f
x_normal = x
if self.skip_linear is not None:
x_normal = self.skip_linear(th.cat([x_normal, skip], dim=-1))
D = T * (F_add_1 - 1)
shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (
self.scale_shift_table[None] + t.reshape(B, 6, -1)
).chunk(6, dim=1)
x_normal = x_normal + self.drop_path(
gate_msa
* self.attn(
t2i_modulate(self.norm1(x_normal), shift_msa, scale_msa)
).reshape(B, D, C)
)
x_normal = x_normal.reshape(B, T, F_add_1 - 1, C)
x_normal = th.cat((x_normal, end), 2)
x_normal = x_normal.reshape(B * T, F_add_1, C)
pos_f = th.arange(self.num_f, device=x.device).unsqueeze(0).expand(B * T, -1)
x_normal = x_normal + self.f_pos(pos_f)
x_normal = x_normal + self.F_transformer(self.norm3(x_normal))
x_normal = x_normal.reshape(B, T, F_add_1 * C)
pos_t = th.arange(self.num_t, device=x.device).unsqueeze(0).expand(B, -1)
x_normal = x_normal + self.t_pos(pos_t)
x_normal = x_normal + self.T_transformer(self.norm5(x_normal))
x_normal = x_normal.reshape(B, T, F_add_1, C)
end = x_normal[:, :, -1, :].unsqueeze(2)
x_normal = x_normal[:, :, :-1, :]
x_normal = x_normal.reshape(B, T * (F_add_1 - 1), C)
x_normal = x_normal + self.cross_attn(x_normal, y, mask)
x_normal = x_normal + self.drop_path(
gate_mlp
* self.mlp(t2i_modulate(self.norm2(x_normal), shift_mlp, scale_mlp))
)
return x_normal, end
class PixArt_MDT(nn.Module):
"""
Diffusion model with a Transformer backbone.
"""
def __init__(
self,
input_size=(256, 16),
patch_size=(16, 4),
overlap=(0, 0),
in_channels=8,
hidden_size=1152,
depth=28,
num_heads=16,
mlp_ratio=4.0,
class_dropout_prob=0.1,
pred_sigma=False,
drop_path: float = 0.0,
window_size=0,
window_block_indexes=None,
use_rel_pos=False,
cond_dim=1024,
lewei_scale=1.0,
use_cfg=True,
cfg_scale=4.0,
config=None,
model_max_length=120,
mask_ratio=None,
decode_layer=4,
**kwargs
):
if window_block_indexes is None:
window_block_indexes = []
super().__init__()
self.use_cfg = use_cfg
self.cfg_scale = cfg_scale
self.input_size = input_size
self.pred_sigma = pred_sigma
self.in_channels = in_channels
self.out_channels = in_channels
self.patch_size = patch_size
self.num_heads = num_heads
self.lewei_scale = (lewei_scale,)
decode_layer = int(decode_layer)
self.x_embedder = PatchEmbed(
input_size, patch_size, overlap, in_channels, hidden_size, bias=True
)
self.t_embedder = TimestepEmbedder(hidden_size)
num_patches = self.x_embedder.num_patches
self.base_size = input_size[0] // self.patch_size[0] * 2
# Will use fixed sin-cos embedding:
self.register_buffer("pos_embed", th.zeros(1, num_patches, hidden_size))
approx_gelu = lambda: nn.GELU(approximate="tanh")
self.t_block = nn.Sequential(
nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True)
)
self.y_embedder = nn.Linear(cond_dim, hidden_size)
half_depth = (depth - decode_layer) // 2
self.half_depth = half_depth
drop_path_half = [
x.item() for x in th.linspace(0, drop_path, half_depth)
] # stochastic depth decay rule
drop_path_decode = [x.item() for x in th.linspace(0, drop_path, decode_layer)]
self.en_inblocks = nn.ModuleList(
[
MDTBlock(
hidden_size,
num_heads,
mlp_ratio=mlp_ratio,
drop_path=drop_path_half[i],
input_size=(
self.x_embedder.grid_size[0],
self.x_embedder.grid_size[1],
),
window_size=0,
use_rel_pos=False,
FFN_type="mlp",
)
for i in range(half_depth)
]
)
self.en_outblocks = nn.ModuleList(
[
MDTBlock(
hidden_size,
num_heads,
mlp_ratio=mlp_ratio,
drop_path=drop_path_half[i],
input_size=(
self.x_embedder.grid_size[0],
self.x_embedder.grid_size[1],
),
window_size=0,
use_rel_pos=False,
skip=True,
FFN_type="mlp",
)
for i in range(half_depth)
]
)
self.de_blocks = nn.ModuleList(
[
MDTBlock(
hidden_size,
num_heads,
mlp_ratio=mlp_ratio,
drop_path=drop_path_decode[i],
input_size=(
self.x_embedder.grid_size[0],
self.x_embedder.grid_size[1],
),
window_size=0,
use_rel_pos=False,
skip=True,
FFN_type="mlp",
)
for i in range(decode_layer)
]
)
self.sideblocks = nn.ModuleList(
[
MDTBlock(
hidden_size,
num_heads,
mlp_ratio=mlp_ratio,
input_size=(
self.x_embedder.grid_size[0],
self.x_embedder.grid_size[1],
),
window_size=0,
use_rel_pos=False,
FFN_type="mlp",
)
for _ in range(1)
]
)
self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
self.decoder_pos_embed = nn.Parameter(
th.zeros(1, num_patches, hidden_size), requires_grad=True
)
if mask_ratio is not None:
self.mask_token = nn.Parameter(th.zeros(1, 1, hidden_size))
self.mask_ratio = float(mask_ratio)
self.decode_layer = int(decode_layer)
else:
self.mask_token = nn.Parameter(
th.zeros(1, 1, hidden_size), requires_grad=False
)
self.mask_ratio = None
self.decode_layer = int(decode_layer)
self.initialize_weights()
def forward(self, x, t, context, mask=None, enable_mask=False, **kwargs):
"""
Forward pass of PixArt.
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (N,) tensor of diffusion timesteps
y: (N, 1, 120, C) tensor of class labels
"""
x = x.to(self.dtype)
t = t.to(self.dtype)
y = context.to(self.dtype)
pos_embed = self.pos_embed.to(self.dtype)
self.h, self.w = self.x_embedder.grid_size[0], self.x_embedder.grid_size[1]
x = self.x_embedder(x) + pos_embed
t = self.t_embedder(t.to(x.dtype))
t0 = self.t_block(t)
y = self.y_embedder(y)
try:
mask = mask
except:
mask = th.ones(x.shape[0], 1).to(x.device)
print("MASK !!!!!!!!!!!!!!!!!!!!!!!!!")
assert mask is not None
y = y.masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
y_lens = mask.sum(dim=1).tolist()
y_lens = [int(_) for _ in y_lens]
input_skip = x
masked_stage = False
skips = []
# TODO : masking op for training
if self.mask_ratio is not None and self.training:
rand_mask_ratio = th.rand(1, device=x.device)
rand_mask_ratio = rand_mask_ratio * 0.2 + self.mask_ratio
x, mask, ids_restore, ids_keep = self.random_masking(x, rand_mask_ratio)
masked_stage = True
for block in self.en_inblocks:
if masked_stage:
x = auto_grad_checkpoint(block, x, y, t0, y_lens, ids_keep=ids_keep)
else:
x = auto_grad_checkpoint(block, x, y, t0, y_lens, ids_keep=None)
skips.append(x)
for block in self.en_outblocks:
if masked_stage:
x = auto_grad_checkpoint(
block, x, y, t0, y_lens, skip=skips.pop(), ids_keep=ids_keep
)
else:
x = auto_grad_checkpoint(
block, x, y, t0, y_lens, skip=skips.pop(), ids_keep=None
)
if self.mask_ratio is not None and self.training:
x = self.forward_side_interpolater(x, y, t0, y_lens, mask, ids_restore)
masked_stage = False
else:
# add pos embed
x = x + self.decoder_pos_embed
for i in range(len(self.de_blocks)):
block = self.de_blocks[i]
this_skip = input_skip
x = auto_grad_checkpoint(
block, x, y, t0, y_lens, skip=this_skip, ids_keep=None
)
x = self.final_layer(x, t)
x = self.unpatchify(x)
return x
def forward_with_dpmsolver(self, x, timestep, y, mask=None, **kwargs):
"""
dpm solver donnot need variance prediction
"""
# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
model_out = self.forward(x, timestep, y, mask)
return model_out.chunk(2, dim=1)[0]
def forward_with_cfg(
self, x, timestep, context_list, context_mask_list=None, cfg_scale=4.0, **kwargs
):
"""
Forward pass of PixArt, but also batches the unconditional forward pass for classifier-free guidance.
"""
# https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
half = x[: len(x) // 2]
combined = th.cat([half, half], dim=0)
model_out = self.forward(
combined, timestep, context_list, context_mask_list=None
)
model_out = model_out["x"] if isinstance(model_out, dict) else model_out
eps, rest = model_out[:, :8], model_out[:, 8:]
cond_eps, uncond_eps = th.split(eps, len(eps) // 2, dim=0)
half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
eps = th.cat([half_eps, half_eps], dim=0)
return eps
def unpatchify(self, x):
"""
x: (N, T, patch_size 0 * patch_size 1 * C)
imgs: (Bs. 256. 16. 8)
"""
if self.x_embedder.ol == (0, 0) or self.x_embedder.ol == [0, 0]:
c = self.out_channels
p0 = self.x_embedder.patch_size[0]
p1 = self.x_embedder.patch_size[1]
h, w = self.x_embedder.grid_size[0], self.x_embedder.grid_size[1]
x = x.reshape(shape=(x.shape[0], h, w, p0, p1, c))
x = th.einsum("nhwpqc->nchpwq", x)
imgs = x.reshape(shape=(x.shape[0], c, h * p0, w * p1))
return imgs
lf = self.x_embedder.grid_size[0]
rf = self.x_embedder.grid_size[1]
lp = self.x_embedder.patch_size[0]
rp = self.x_embedder.patch_size[1]
lo = self.x_embedder.ol[0]
ro = self.x_embedder.ol[1]
lm = self.x_embedder.img_size[0]
rm = self.x_embedder.img_size[1]
lpad = self.x_embedder.pad_size[0]
rpad = self.x_embedder.pad_size[1]
bs = x.shape[0]
torch_map = self.torch_map
c = self.out_channels
x = x.reshape(shape=(bs, lf, rf, lp, rp, c))
x = th.einsum("nhwpqc->nchwpq", x)
added_map = th.zeros(bs, c, lm + 2 * lpad, rm + 2 * rpad).to(x.device)
for i in range(lf):
for j in range(rf):
xx = (i) * (lp - lo)
yy = (j) * (rp - ro)
added_map[:, :, xx : (xx + lp), yy : (yy + rp)] += x[:, :, i, j, :, :]
added_map = added_map[:][:][lpad : lm + lpad, rpad : rm + rpad]
return th.mul(added_map.to(x.device), torch_map.to(x.device))
def random_masking(self, x, mask_ratio):
"""
Perform per-sample random masking by per-sample shuffling.
Per-sample shuffling is done by argsort random noise.
x: [N, L, D], sequence
"""
N, L, D = x.shape # batch, length, dim
len_keep = int(L * (1 - mask_ratio))
noise = th.rand(N, L, device=x.device)
# sort noise for each sample
# ascend: small is keep, large is remove
ids_shuffle = th.argsort(noise, dim=1)
ids_restore = th.argsort(ids_shuffle, dim=1)
# keep the first subset
ids_keep = ids_shuffle[:, :len_keep]
x_masked = th.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
# generate the binary mask: 0 is keep, 1 is remove
mask = th.ones([N, L], device=x.device)
mask[:, :len_keep] = 0
# unshuffle to get the binary mask
mask = th.gather(mask, dim=1, index=ids_restore)
return x_masked, mask, ids_restore, ids_keep
def forward_side_interpolater(self, x, y, t0, y_lens, mask, ids_restore):
# append mask tokens to sequence
mask_tokens = self.mask_token.repeat(
x.shape[0], ids_restore.shape[1] - x.shape[1], 1
)
x_ = th.cat([x, mask_tokens], dim=1)
x = th.gather(
x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2])
) # unshuffle
# add pos embed
x = x + self.decoder_pos_embed
# pass to the basic block
x_before = x
for sideblock in self.sideblocks:
x = sideblock(x, y, t0, y_lens, ids_keep=None)
# masked shortcut
mask = mask.unsqueeze(dim=-1)
x = x * mask + (1 - mask) * x_before
return x
def initialize_weights(self):
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
th.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
# Initialize (and freeze) pos_embed by sin-cos embedding:
pos_embed = get_2d_sincos_pos_embed(
self.pos_embed.shape[-1],
self.x_embedder.grid_size,
lewei_scale=self.lewei_scale,
base_size=self.base_size,
)
self.pos_embed.data.copy_(th.from_numpy(pos_embed).float().unsqueeze(0))
# Initialize patch_embed like nn.Linear (instead of nn.Conv2d):
w = self.x_embedder.proj.weight.data
nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
# Initialize timestep embedding MLP:
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
nn.init.normal_(self.t_block[1].weight, std=0.02)
# Initialize caption embedding MLP:
nn.init.normal_(self.y_embedder.weight, std=0.02)
# Zero-out adaLN modulation layers in PixArt blocks:
for block in self.en_inblocks:
nn.init.constant_(block.cross_attn.proj.weight, 0)
nn.init.constant_(block.cross_attn.proj.bias, 0)
for block in self.en_outblocks:
nn.init.constant_(block.cross_attn.proj.weight, 0)
nn.init.constant_(block.cross_attn.proj.bias, 0)
for block in self.de_blocks:
nn.init.constant_(block.cross_attn.proj.weight, 0)
nn.init.constant_(block.cross_attn.proj.bias, 0)
for block in self.sideblocks:
nn.init.constant_(block.cross_attn.proj.weight, 0)
nn.init.constant_(block.cross_attn.proj.bias, 0)
# Zero-out output layers:
nn.init.constant_(self.final_layer.linear.weight, 0)
nn.init.constant_(self.final_layer.linear.bias, 0)
if self.x_embedder.ol == [0, 0] or self.x_embedder.ol == (0, 0):
return
lf = self.x_embedder.grid_size[0]
rf = self.x_embedder.grid_size[1]
lp = self.x_embedder.patch_size[0]
rp = self.x_embedder.patch_size[1]
lo = self.x_embedder.ol[0]
ro = self.x_embedder.ol[1]
lm = self.x_embedder.img_size[0]
rm = self.x_embedder.img_size[1]
lpad = self.x_embedder.pad_size[0]
rpad = self.x_embedder.pad_size[1]
torch_map = th.zeros(lm + 2 * lpad, rm + 2 * rpad).to("cuda")
for i in range(lf):
for j in range(rf):
xx = (i) * (lp - lo)
yy = (j) * (rp - ro)
torch_map[xx : (xx + lp), yy : (yy + rp)] += 1
torch_map = torch_map[lpad : lm + lpad, rpad : rm + rpad]
self.torch_map = th.reciprocal(torch_map)
@property
def dtype(self):
return next(self.parameters()).dtype
def get_2d_sincos_pos_embed(
embed_dim,
grid_size,
cls_token=False,
extra_tokens=0,
lewei_scale=1.0,
base_size_x=256 // 4,
base_size_y=16 // 4,
base_size=128,
):
"""
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)
"""
if isinstance(grid_size, int):
grid_size = to_2tuple(grid_size)
grid_h = (
np.arange(grid_size[0], dtype=np.float32)
/ (grid_size[0] / base_size_x)
/ lewei_scale
)
grid_w = (
np.arange(grid_size[1], dtype=np.float32)
/ (grid_size[1] / base_size_y)
/ lewei_scale
)
grid = np.meshgrid(grid_w, grid_h) # here w goes first
grid = np.stack(grid, axis=0)
grid = grid.reshape([2, 1, grid_size[1], grid_size[0]])
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):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])
return np.concatenate([emb_h, emb_w], axis=1)
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)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.0
omega = 1.0 / 10000**omega
pos = pos.reshape(-1)
out = np.einsum("m,d->md", pos, omega)
emb_sin = np.sin(out)
emb_cos = np.cos(out)
return np.concatenate([emb_sin, emb_cos], axis=1)
PixArt_blocks.py 0 → 100644
View file @ a44e71bd
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# GLIDE: https://github.com/openai/glide-text2im
# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
# --------------------------------------------------------
import math
import torch
import torch.nn as nn
from timm.models.vision_transformer import Mlp, Attention as Attention_
from einops import rearrange, repeat
import torch.nn.functional as F
from .utils import add_decomposed_rel_pos
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
def t2i_modulate(x, shift, scale):
return x * (1 + scale) + shift
class MultiHeadCrossAttention_org(nn.Module):
def __init__(self, n_feat, n_head, attn_drop=0.0, proj_drop=0):
"""Construct an MultiHeadedAttention object."""
super(MultiHeadCrossAttention, self).__init__()
assert n_feat % n_head == 0
# We assume d_v always equals d_k
self.d_k = n_feat // n_head
self.h = n_head
self.linear_q = nn.Linear(n_feat, n_feat)
self.linear_k = nn.Linear(n_feat, n_feat)
self.linear_v = nn.Linear(n_feat, n_feat)
self.proj = nn.Linear(n_feat, n_feat)
self.attn = None
self.dropout = nn.Dropout(p=attn_drop)
self.proj_drop = nn.Dropout(proj_drop)
def forward_qkv(self, query, key, value):
"""Transform query, key and value.
Args:
query (torch.Tensor): Query tensor (#batch, time1, size).
key (torch.Tensor): Key tensor (#batch, time2, size).
value (torch.Tensor): Value tensor (#batch, time2, size).
Returns:
torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k).
torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k).
torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k).
"""
n_batch = query.size(0)
q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
q = q.transpose(1, 2)
k = k.transpose(1, 2)
v = v.transpose(1, 2)
return q, k, v
def forward_attention(self, value, scores, mask):
"""Compute attention context vector.
Args:
value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).
Returns:
torch.Tensor: Transformed value (#batch, time1, d_model)
weighted by the attention score (#batch, time1, time2).
"""
n_batch = value.size(0)
if mask is not None:
mask = mask.unsqueeze(1).eq(0)
min_value = torch.finfo(scores.dtype).min
scores = scores.masked_fill(mask, min_value)
self.attn = torch.softmax(scores, dim=-1).masked_fill(mask, 0.0)
else:
self.attn = torch.softmax(scores, dim=-1)
p_attn = self.dropout(self.attn)
x = torch.matmul(p_attn, value)
x = x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
return self.proj_drop(self.proj(x))
def forward(self, x, cond, mask):
"""Compute scaled dot product attention.
Args:
query (torch.Tensor): Query tensor (#batch, time1, size).
key (torch.Tensor): Key tensor (#batch, time2, size).
value (torch.Tensor): Value tensor (#batch, time2, size).
mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
(#batch, time1, time2).
Returns:
torch.Tensor: Output tensor (#batch, time1, d_model).
"""
q, k, v = self.forward_qkv(x, cond, cond)
scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
return self.forward_attention(v, scores, mask)
class MultiHeadCrossAttention(nn.Module):
def __init__(
self, d_model, num_heads, attn_drop=0.0, proj_drop=0.0, **block_kwargs
):
super(MultiHeadCrossAttention, self).__init__()
assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
self.d_model = d_model
self.num_heads = num_heads
self.head_dim = d_model // num_heads
self.q_linear = nn.Linear(d_model, d_model)
self.kv_linear = nn.Linear(d_model, d_model * 2)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(d_model, d_model)
self.proj_drop = nn.Dropout(proj_drop)
def forward(self, x, cond, mask=None):
# query/value: img tokens; key: condition; mask: if padding tokens
B, N, C = x.shape
assert mask is not None
q = self.q_linear(x).view(B, N, self.num_heads, self.head_dim)
if isinstance(mask, list):
# mask = y_lens: list of kv sequence lengths per batch item.
# cond is (1, total_valid_tokens, C) — batch flattened, padding removed.
kv = self.kv_linear(cond).view(1, -1, 2, self.num_heads, self.head_dim)
k, v = kv.unbind(2)
total_kv = k.shape[1]
# (B, N, heads, dim) → (B, heads, N, dim)
q = q.permute(0, 2, 1, 3)
# (1, total_kv, heads, dim) → (B, heads, total_kv, dim)
k = k.permute(0, 2, 1, 3).expand(B, -1, -1, -1)
v = v.permute(0, 2, 1, 3).expand(B, -1, -1, -1)
# Build block-diagonal attention mask from sequence lengths
attn_mask = torch.full(
(B, 1, N, total_kv), float("-inf"), dtype=q.dtype, device=q.device
)
offset = 0
for i, kv_len in enumerate(mask):
attn_mask[i, :, :, offset:offset + kv_len] = 0
offset += kv_len
else:
# mask is a padding mask tensor (B, 1, N_kv)
kv = self.kv_linear(cond).view(B, -1, 2, self.num_heads, self.head_dim)
k, v = kv.unbind(2)
# (B, N, heads, dim) → (B, heads, N, dim)
q = q.permute(0, 2, 1, 3)
k = k.permute(0, 2, 1, 3)
v = v.permute(0, 2, 1, 3)
attn_mask = torch.zeros_like(mask, dtype=q.dtype)
attn_mask.masked_fill_(mask == 0, float("-inf"))
attn_mask = attn_mask.unsqueeze(1)
x = F.scaled_dot_product_attention(
q, k, v, dropout_p=self.attn_drop.p, attn_mask=attn_mask
)
# (B, heads, N, dim) → (B, N, C)
x = x.permute(0, 2, 1, 3).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class WindowAttention(Attention_):
"""Multi-head Attention block with relative position embeddings."""
def __init__(
self,
dim,
num_heads=8,
qkv_bias=True,
use_rel_pos=False,
rel_pos_zero_init=True,
input_size=None,
**block_kwargs,
):
"""
Args:
dim (int): Number of input channels.
num_heads (int): Number of attention heads.
qkv_bias (bool: If True, add a learnable bias to query, key, value.
rel_pos (bool): If True, add relative positional embeddings to the attention map.
rel_pos_zero_init (bool): If True, zero initialize relative positional parameters.
input_size (int or None): Input resolution for calculating the relative positional
parameter size.
"""
super().__init__(dim, num_heads=num_heads, qkv_bias=qkv_bias, **block_kwargs)
self.use_rel_pos = use_rel_pos
if self.use_rel_pos:
# initialize relative positional embeddings
self.rel_pos_h = nn.Parameter(
torch.zeros(2 * input_size[0] - 1, self.head_dim)
)
self.rel_pos_w = nn.Parameter(
torch.zeros(2 * input_size[1] - 1, self.head_dim)
)
if not rel_pos_zero_init:
nn.init.trunc_normal_(self.rel_pos_h, std=0.02)
nn.init.trunc_normal_(self.rel_pos_w, std=0.02)
def forward(self, x, mask=None):
B, N, C = x.shape
qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
q, k, v = qkv.unbind(2)
if use_fp32_attention := getattr(self, "fp32_attention", False):
q, k, v = q.float(), k.float(), v.float()
# (B, N, heads, dim) → (B, heads, N, dim) for SDPA
q = q.permute(0, 2, 1, 3)
k = k.permute(0, 2, 1, 3)
v = v.permute(0, 2, 1, 3)
attn_mask = None
if mask is not None:
attn_mask = torch.zeros(
B * self.num_heads, q.shape[2], k.shape[2],
dtype=q.dtype, device=q.device,
)
attn_mask.masked_fill_(
mask.squeeze(1).repeat(self.num_heads, 1, 1) == 0, float("-inf")
)
attn_mask = attn_mask.view(B, self.num_heads, q.shape[2], k.shape[2])
x = F.scaled_dot_product_attention(
q, k, v, dropout_p=self.attn_drop.p, attn_mask=attn_mask
)
# (B, heads, N, dim) → (B, N, C)
x = x.permute(0, 2, 1, 3).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
#################################################################################
# AMP attention with fp32 softmax to fix loss NaN problem during training #
#################################################################################
class Attention(Attention_):
def forward(self, x):
B, N, C = x.shape
qkv = (
self.qkv(x)
.reshape(B, N, 3, self.num_heads, C // self.num_heads)
.permute(2, 0, 3, 1, 4)
)
q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
use_fp32_attention = getattr(self, "fp32_attention", False)
if use_fp32_attention:
q, k = q.float(), k.float()
with torch.cuda.amp.autocast(enabled=not use_fp32_attention):
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class FinalLayer(nn.Module):
"""
The final layer of PixArt.
"""
def __init__(self, hidden_size, patch_size, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.linear = nn.Linear(
hidden_size, patch_size * patch_size * out_channels, bias=True
)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)
)
def forward(self, x, c):
shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
x = modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class T2IFinalLayer(nn.Module):
"""
The final layer of PixArt.
"""
def __init__(self, hidden_size, patch_size, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.linear = nn.Linear(
hidden_size, patch_size[0] * patch_size[1] * out_channels, bias=True
)
self.scale_shift_table = nn.Parameter(
torch.randn(2, hidden_size) / hidden_size**0.5
)
self.out_channels = out_channels
self.initialize_weights()
def initialize_weights(self):
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
def forward(self, x, t):
shift, scale = (self.scale_shift_table[None] + t[:, None]).chunk(2, dim=1)
x = t2i_modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class MaskFinalLayer(nn.Module):
"""
The final layer of PixArt.
"""
def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels):
super().__init__()
self.norm_final = nn.LayerNorm(
final_hidden_size, elementwise_affine=False, eps=1e-6
)
self.linear = nn.Linear(
final_hidden_size, patch_size * patch_size * out_channels, bias=True
)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(), nn.Linear(c_emb_size, 2 * final_hidden_size, bias=True)
)
def forward(self, x, t):
shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
x = modulate(self.norm_final(x), shift, scale)
x = self.linear(x)
return x
class DecoderLayer(nn.Module):
"""
The final layer of PixArt.
"""
def __init__(self, hidden_size, decoder_hidden_size):
super().__init__()
self.norm_decoder = nn.LayerNorm(
hidden_size, elementwise_affine=False, eps=1e-6
)
self.linear = nn.Linear(hidden_size, decoder_hidden_size, bias=True)
self.adaLN_modulation = nn.Sequential(
nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)
)
def forward(self, x, t):
shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
x = modulate(self.norm_decoder(x), shift, scale)
x = self.linear(x)
return x
#################################################################################
# Embedding Layers for Timesteps and Class Labels #
#################################################################################
class TimestepEmbedder(nn.Module):
"""
Embeds scalar timesteps into vector representations.
"""
def __init__(self, hidden_size, frequency_embedding_size=256):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
:param t: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an (N, D) Tensor of positional embeddings.
"""
# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
half = dim // 2
freqs = torch.exp(
-math.log(max_period)
* torch.arange(start=0, end=half, dtype=torch.float32, device=t.device)
/ half
)
args = t[:, None].float() * freqs[None]
embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
embedding = torch.cat(
[embedding, torch.zeros_like(embedding[:, :1])], dim=-1
)
return embedding
def forward(self, t):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(
self.dtype
)
return self.mlp(t_freq)
@property
def dtype(self):
return next(self.parameters()).dtype
class SizeEmbedder(TimestepEmbedder):
"""
Embeds scalar timesteps into vector representations.
"""
def __init__(self, hidden_size, frequency_embedding_size=256):
super().__init__(
hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size
)
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=True),
nn.SiLU(),
nn.Linear(hidden_size, hidden_size, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
self.outdim = hidden_size
def forward(self, s, bs):
if s.ndim == 1:
s = s[:, None]
assert s.ndim == 2
if s.shape[0] != bs:
s = s.repeat(bs // s.shape[0], 1)
assert s.shape[0] == bs
b, dims = s.shape[0], s.shape[1]
s = rearrange(s, "b d -> (b d)")
s_freq = self.timestep_embedding(s, self.frequency_embedding_size).to(
self.dtype
)
s_emb = self.mlp(s_freq)
s_emb = rearrange(s_emb, "(b d) d2 -> b (d d2)", b=b, d=dims, d2=self.outdim)
return s_emb
@property
def dtype(self):
return next(self.parameters()).dtype
class LabelEmbedder(nn.Module):
"""
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
"""
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]).cuda() < self.dropout_prob
else:
drop_ids = force_drop_ids == 1
labels = torch.where(drop_ids, self.num_classes, labels)
return labels
def forward(self, labels, train, force_drop_ids=None):
use_dropout = self.dropout_prob > 0
if (train and use_dropout) or (force_drop_ids is not None):
labels = self.token_drop(labels, force_drop_ids)
return self.embedding_table(labels)
class CaptionEmbedder(nn.Module):
"""
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
"""
def __init__(
self,
in_channels,
hidden_size,
uncond_prob,
act_layer=nn.GELU(approximate="tanh"),
token_num=120,
):
super().__init__()
self.y_proj = Mlp(
in_features=in_channels,
hidden_features=hidden_size,
out_features=hidden_size,
act_layer=act_layer,
drop=0,
)
self.register_buffer(
"y_embedding",
nn.Parameter(torch.randn(token_num, in_channels) / in_channels**0.5),
)
self.uncond_prob = uncond_prob
def token_drop(self, caption, force_drop_ids=None):
"""
Drops labels to enable classifier-free guidance.
"""
if force_drop_ids is None:
drop_ids = torch.rand(caption.shape[0]).cuda() < self.uncond_prob
else:
drop_ids = force_drop_ids == 1
caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption)
return caption
def forward(self, caption, train, force_drop_ids=None):
if train:
assert caption.shape[2:] == self.y_embedding.shape
use_dropout = self.uncond_prob > 0
if (train and use_dropout) or (force_drop_ids is not None):
caption = self.token_drop(caption, force_drop_ids)
caption = self.y_proj(caption)
return caption
class CaptionEmbedderDoubleBr(nn.Module):
"""
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
"""
def __init__(
self,
in_channels,
hidden_size,
uncond_prob,
act_layer=nn.GELU(approximate="tanh"),
token_num=120,
):
super().__init__()
self.proj = Mlp(
in_features=in_channels,
hidden_features=hidden_size,
out_features=hidden_size,
act_layer=act_layer,
drop=0,
)
self.embedding = nn.Parameter(torch.randn(1, in_channels) / 10**0.5)
self.y_embedding = nn.Parameter(torch.randn(token_num, in_channels) / 10**0.5)
self.uncond_prob = uncond_prob
def token_drop(self, global_caption, caption, force_drop_ids=None):
"""
Drops labels to enable classifier-free guidance.
"""
if force_drop_ids is None:
drop_ids = torch.rand(global_caption.shape[0]).cuda() < self.uncond_prob
else:
drop_ids = force_drop_ids == 1
global_caption = torch.where(drop_ids[:, None], self.embedding, global_caption)
caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption)
return global_caption, caption
def forward(self, caption, train, force_drop_ids=None):
assert caption.shape[2:] == self.y_embedding.shape
global_caption = caption.mean(dim=2).squeeze()
use_dropout = self.uncond_prob > 0
if (train and use_dropout) or (force_drop_ids is not None):
global_caption, caption = self.token_drop(
global_caption, caption, force_drop_ids
)
y_embed = self.proj(global_caption)
return y_embed, caption
README.md
View file @ a44e71bd
# AudioFly # AudioFly
## 论文
暂无
## 模型简介
AudioFly 是一个音频生成模型。它根据文本描述合成音效。该模型可以以 44.1 kHz 的采样率生成高质量音频。生成的音频与提示文本有很强的一致性。 AudioFly 是一个音频生成模型。它根据文本描述合成音效。该模型可以以 44.1 kHz 的采样率生成高质量音频。生成的音频与提示文本有很强的一致性。
AudioFly 采用了潜在扩散模型架构。该模型拥有 10 亿个参数，并在大量多样化的语料库上进行了训练。训练数据包括开源数据集，如 AudioSet、AudioCaps 和 TUT，以及专有的内部数据。该模型在单一事件和多事件场景中表现良好。在这两种情况下，生成的音频都能准确反映所描述的内容。在 AudioCaps 数据集上，AudioF
\ No newline at end of file AudioFly 采用了潜在扩散模型架构。该模型拥有 10 亿个参数，并在大量多样化的语料库上进行了训练。训练数据包括开源数据集，如 AudioSet、AudioCaps 和 TUT，以及专有的内部数据。该模型在单一事件和多事件场景中表现良好。在这两种情况下，生成的音频都能准确反映所描述的内容。在 AudioCaps 数据集上，AudioFly 的性能优于之前的音频生成模型。
## 环境依赖
| 软件 | 版本 |
| :------: | :------: |
| DTK | 26.04 |
| Python | 3.10 |
| Transformers | 4.56.1 |
| vLLM | 0.18.1+das.dtk2604 |
推荐使用镜像:
harbor.sourcefind.cn:5443/dcu/admin/base/custom:vllm0.18.1-ubuntu22.04-dtk26.04-py3.10-20260529-iflytek
- 挂载地址`-v` 根据实际模型情况修改
```bash
docker run -it \
--shm-size 60g \
--network=host \
--name Spark-X1 \
--privileged \
--device=/dev/kfd \
--device=/dev/dri \
--device=/dev/mkfd \
--group-add video \
--cap-add=SYS_PTRACE \
--security-opt seccomp=unconfined \
-u root \
-v /opt/hyhal/:/opt/hyhal/:ro \
-v /path/your_code_data/:/path/your_code_data/ \
harbor.sourcefind.cn:5443/dcu/admin/base/custom:vllm0.18.1-ubuntu22.04-dtk26.04-py3.10-20260529-iflytek bash
```
更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用。
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装。
其它环境配置说明：
```bash
#下载模型以后，替换下载路径内的文件
cp PixArt_blocks.py AudioFly/ldm/modules/diffusionmodules/PixArt_blocks.py
cp PixArt.py AudioFly/ldm/modules/diffusionmodules/PixArt.py
```
## 预训练权重
**请根据`支持的DCU型号`选择对应模型下载，FP8模型仅在BW1100/BW1101上支持，其他型号请勿使用！**
| 模型名称 | 权重大小 | 数据类型 | 支持的DCU型号 | 最低卡数需求 | 下载地址 |
| :------: | :------: | :------: | :------------: | :----------: | :------: |
| AudioFly | 1B | BF16 | BW1000 | 1 | [ModelScope](https://modelscope.cn/models/iflytek/AudioFly) |
## 数据集
暂无
## 训练
暂无
## 推理
### Pytorch
#### 单机推理
```bash
cd AudioFly
python run.py
```
## 效果展示
输入：
'Fierce winds howl through the valley'
输出：
<audio controls src="./doc/result.wav"></audio>
### 精度
DCU与GPU精度一致，推理框架：pytorch
## 源码仓库及问题反馈
- https://developer.sourcefind.cn/codes/modelzoo/audiofly
## 参考资料
- https://modelscope.csdn.net/68da3b11a6dc56200e8ae2ae.html
- https://modelscope.cn/models/iflytek/AudioFly
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# 模型唯一标识
modelCode=15319
# 模型名称
modelName=AudioFly
# 模型描述
modelDescription=AudioFly 是一个音频生成模型。它根据文本描述合成音效。该模型可以以 44.1 kHz 的采样率生成高质量音频。
# 运行过程
processType=推理
# 算法类别
appCategory=语音合成
# 框架类型
frameType=pytorch
# 加速卡类型
accelerateType=BW1000
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import yaml
import torch
from ldm.utils.util import instantiate_from_config
configs = yaml.load(open('/public/home/weishb/iflytek/AudioFly/config/config.yaml', "r"), Loader=yaml.FullLoader)
model = instantiate_from_config(configs["model"])
checkpoint = torch.load('/public/home/weishb/iflytek/AudioFly/models/ldm/model.ckpt')
model.load_state_dict(checkpoint, strict=False)
model.eval()
model = model.cuda()
text = 'Fierce winds howl through the valley'
name = 'result'
savedir = './result'
model.generate_sample(
textlist=[text],
name=name,
cfg=3.5,# Guidance scale (controls how strongly generation follows the text prompt）; not recommended to change
ddim_steps=200, # Number of denoising steps in the diffusion process; not recommended to change
outputdir=f"{savedir}")
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