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dd7a24f | 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 | from __future__ import annotations
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def modulate(x, shift, scale):
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
def timestep_embedding(t, dim, max_period=10000):
half = dim // 2
freqs = torch.exp(-math.log(max_period) * torch.arange(half, device=t.device) / half)
args = t[:, None].float() * freqs[None]
emb = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
if dim % 2:
emb = torch.cat([emb, torch.zeros_like(emb[:, :1])], dim=-1)
return emb
def sincos_2d(dim, grid_h, grid_w):
assert dim % 4 == 0
gy = np.arange(grid_h, dtype=np.float32)
gx = np.arange(grid_w, dtype=np.float32)
gyy, gxx = np.meshgrid(gy, gx, indexing="ij")
d4 = dim // 4
omega = 1.0 / (10000 ** (np.arange(d4, dtype=np.float32) / d4))
def emb1(p):
out = p.reshape(-1)[:, None] * omega[None]
return np.concatenate([np.sin(out), np.cos(out)], axis=1)
pe = np.concatenate([emb1(gyy), emb1(gxx)], axis=1)
return torch.from_numpy(pe).float()
class Attention(nn.Module):
def __init__(self, dim, heads):
super().__init__()
self.heads = heads
self.q = nn.Linear(dim, dim)
self.kv = nn.Linear(dim, dim * 2)
self.proj = nn.Linear(dim, dim)
def forward(self, x, ctx=None):
ctx = x if ctx is None else ctx
B, N, C = x.shape
M = ctx.shape[1]
h = self.heads
q = self.q(x).reshape(B, N, h, C // h).transpose(1, 2)
kv = self.kv(ctx).reshape(B, M, 2, h, C // h).permute(2, 0, 3, 1, 4)
k, v = kv[0], kv[1]
o = F.scaled_dot_product_attention(q, k, v)
o = o.transpose(1, 2).reshape(B, N, C)
return self.proj(o)
class Block(nn.Module):
def __init__(self, dim, heads, mlp_ratio=4.0):
super().__init__()
self.norm1 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
self.attn = Attention(dim, heads)
self.norm_ca = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
self.cross = Attention(dim, heads)
self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
hidden = int(dim * mlp_ratio)
self.mlp = nn.Sequential(nn.Linear(dim, hidden), nn.GELU(approximate="tanh"),
nn.Linear(hidden, dim))
self.ada = nn.Sequential(nn.SiLU(), nn.Linear(dim, 6 * dim))
self.cross_gate = nn.Parameter(torch.zeros(1))
def forward(self, x, c, text):
shift1, scale1, gate1, shift2, scale2, gate2 = self.ada(c).chunk(6, dim=1)
x = x + gate1.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift1, scale1))
x = x + self.cross_gate * self.cross(self.norm_ca(x), text)
x = x + gate2.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift2, scale2))
return x
class AudioDiT(nn.Module):
def __init__(self, mel_ch=1, x_res=384, y_res=256, patch=16, dim=384, depth=12,
heads=6, text_seq_dim=768, text_pool_dim=512, mlp_ratio=4.0):
super().__init__()
assert x_res % patch == 0 and y_res % patch == 0
self.mel_ch = mel_ch
self.x_res = x_res
self.y_res = y_res
self.patch = patch
self.grid_h = y_res // patch
self.grid_w = x_res // patch
self.patch_dim = mel_ch * patch * patch
self.x_embed = nn.Linear(self.patch_dim, dim)
self.register_buffer("pos", sincos_2d(dim, self.grid_h, self.grid_w).unsqueeze(0))
self.t_mlp = nn.Sequential(nn.Linear(dim, dim), nn.SiLU(), nn.Linear(dim, dim))
self.text_proj = nn.Linear(text_seq_dim, dim)
self.text_pool = nn.Linear(text_pool_dim, dim)
self.blocks = nn.ModuleList([Block(dim, heads, mlp_ratio) for _ in range(depth)])
self.norm_out = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
self.ada_out = nn.Sequential(nn.SiLU(), nn.Linear(dim, 2 * dim))
self.head = nn.Linear(dim, self.patch_dim)
self.dim = dim
self._init()
def _init(self):
for m in self.modules():
if isinstance(m, nn.Linear):
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
nn.init.zeros_(m.bias)
for b in self.blocks:
nn.init.zeros_(b.ada[-1].weight); nn.init.zeros_(b.ada[-1].bias)
nn.init.zeros_(self.ada_out[-1].weight); nn.init.zeros_(self.ada_out[-1].bias)
nn.init.zeros_(self.head.weight); nn.init.zeros_(self.head.bias)
def patchify(self, x):
B, C, H, W = x.shape
p = self.patch
x = x.reshape(B, C, H // p, p, W // p, p)
x = x.permute(0, 2, 4, 1, 3, 5).reshape(B, (H // p) * (W // p), C * p * p)
return x
def unpatchify(self, x):
B, N, _ = x.shape
p = self.patch
gh, gw = self.grid_h, self.grid_w
C = self.mel_ch
x = x.reshape(B, gh, gw, C, p, p).permute(0, 3, 1, 4, 2, 5)
return x.reshape(B, C, gh * p, gw * p)
def forward(self, x, t, text_seq, text_pool):
x = self.x_embed(self.patchify(x)) + self.pos
c = self.t_mlp(timestep_embedding(t, self.dim)) + self.text_pool(text_pool)
text = self.text_proj(text_seq)
for blk in self.blocks:
x = blk(x, c, text)
shift, scale = self.ada_out(c).chunk(2, dim=1)
x = modulate(self.norm_out(x), shift, scale)
x = self.head(x)
return self.unpatchify(x)
def num_params(self):
return sum(p.numel() for p in self.parameters())
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