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Diffusion Transformer (DiT) Backbone for Flow Matching in ViuAI_TTS_200M.
Enhanced with:
1. In-Context Reference Audio Conditioning (Voice Prompting / Zero-Shot Cloning)
2. Classifier-Free Guidance (CFG) Support
3. AdaLN-Zero (Adaptive Layer Normalization) Modulation
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional
class TimestepEmbedder(nn.Module):
"""
Embeds scalar timesteps t into vector representations using sinusoidal embeddings + MLP.
"""
def __init__(self, hidden_dim: int, frequency_embedding_size: int = 256):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_dim, bias=True),
nn.SiLU(),
nn.Linear(hidden_dim, hidden_dim, bias=True),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t: torch.Tensor, dim: int, max_period: int = 10000) -> torch.Tensor:
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: torch.Tensor) -> torch.Tensor:
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
return self.mlp(t_freq)
def modulate(x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
class DiTBlock(nn.Module):
"""
A DiT block with adaptive layer norm (AdaLN-Zero) conditioning and Cross-Attention on text & reference audio.
"""
def __init__(self, hidden_dim: int, num_heads: int, cond_dim: int, ffn_dim: int, dropout: float = 0.1):
super().__init__()
self.hidden_dim = hidden_dim
self.num_heads = num_heads
self.head_dim = hidden_dim // num_heads
# 1. Self-Attention
self.norm1 = nn.LayerNorm(hidden_dim, elementwise_affine=False, eps=1e-6)
self.qkv = nn.Linear(hidden_dim, hidden_dim * 3, bias=False)
self.attn_proj = nn.Linear(hidden_dim, hidden_dim, bias=False)
# 2. Cross-Attention on Text Conditioning & Reference Audio
self.norm2 = nn.LayerNorm(hidden_dim, elementwise_affine=False, eps=1e-6)
self.q_cross = nn.Linear(hidden_dim, hidden_dim, bias=False)
self.kv_cross = nn.Linear(cond_dim, hidden_dim * 2, bias=False)
self.cross_proj = nn.Linear(hidden_dim, hidden_dim, bias=False)
# 3. Feed Forward Network
self.norm3 = nn.LayerNorm(hidden_dim, elementwise_affine=False, eps=1e-6)
self.ffn = nn.Sequential(
nn.Linear(hidden_dim, ffn_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(ffn_dim, hidden_dim),
nn.Dropout(dropout),
)
# AdaLN modulation MLP produces 9 parameters:
# shift, scale, gate for: self-attn, cross-attn, and ffn
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(cond_dim, 9 * hidden_dim, bias=True)
)
# Zero-initialize the modulation weights (AdaLN-Zero) for ultra-stable training
nn.init.constant_(self.adaLN_modulation[-1].weight, 0)
nn.init.constant_(self.adaLN_modulation[-1].bias, 0)
def forward(
self,
x: torch.Tensor,
c: torch.Tensor,
conditioning: torch.Tensor,
cond_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""
Args:
x: Audio latents/mel sequence [B, T_audio, hidden_dim]
c: Timestep conditioning vector [B, cond_dim]
conditioning: Combined Text + Reference Voice Latents [B, T_cond, cond_dim]
cond_mask: Optional condition padding mask [B, T_cond]
"""
B, T, C = x.shape
params = self.adaLN_modulation(c).chunk(9, dim=-1)
shift_msa, scale_msa, gate_msa, shift_cross, scale_cross, gate_cross, shift_mlp, scale_mlp, gate_mlp = params
# 1. Self Attention with Modulation
norm_x = modulate(self.norm1(x), shift_msa, scale_msa)
qkv = self.qkv(norm_x).reshape(B, T, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
attn = F.scaled_dot_product_attention(q, k, v)
attn = attn.transpose(1, 2).reshape(B, T, C)
x = x + gate_msa.unsqueeze(1) * self.attn_proj(attn)
# 2. Cross Attention on Combined Text + Voice Prompt Conditioning
norm_x = modulate(self.norm2(x), shift_cross, scale_cross)
q_c = self.q_cross(norm_x).reshape(B, T, self.num_heads, self.head_dim).transpose(1, 2)
kv_c = self.kv_cross(conditioning).reshape(B, conditioning.shape[1], 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
k_c, v_c = kv_c[0], kv_c[1]
cross_attn = F.scaled_dot_product_attention(
q_c, k_c, v_c,
attn_mask=cond_mask.unsqueeze(1).unsqueeze(2) if cond_mask is not None else None
)
cross_attn = cross_attn.transpose(1, 2).reshape(B, T, C)
x = x + gate_cross.unsqueeze(1) * self.cross_proj(cross_attn)
# 3. Feed Forward with Modulation
norm_x = modulate(self.norm3(x), shift_mlp, scale_mlp)
x = x + gate_mlp.unsqueeze(1) * self.ffn(norm_x)
return x
class DiTBackbone(nn.Module):
"""
Diffusion Transformer (DiT) Core Model (~148M Parameters in 9 layers).
Enhanced for 2026 SOTA Voice Cloning and In-Context Audio Prompting.
"""
def __init__(
self,
in_channels: int = 80,
hidden_dim: int = 896,
num_layers: int = 9,
num_heads: int = 14,
ffn_dim: int = 3584,
cond_dim: int = 512,
time_embed_dim: int = 512,
dropout: float = 0.1,
):
super().__init__()
self.in_channels = in_channels
self.hidden_dim = hidden_dim
self.cond_dim = cond_dim
# Input projection for audio mel / latents
self.in_proj = nn.Linear(in_channels, hidden_dim)
# Reference voice audio prompt encoder (projects 80-mel to cond_dim)
self.ref_audio_proj = nn.Sequential(
nn.Linear(in_channels, cond_dim),
nn.SiLU(),
nn.Linear(cond_dim, cond_dim),
)
# Null condition vector for Classifier-Free Guidance (CFG)
self.null_cond = nn.Parameter(torch.randn(1, 1, cond_dim) * 0.02)
# Timestep embedder
self.time_embedder = TimestepEmbedder(time_embed_dim)
self.time_proj = nn.Linear(time_embed_dim, cond_dim)
# DiT Blocks
self.blocks = nn.ModuleList([
DiTBlock(hidden_dim, num_heads, cond_dim, ffn_dim, dropout)
for _ in range(num_layers)
])
# Final Layer (AdaLN-Zero + Linear Out)
self.final_norm = nn.LayerNorm(hidden_dim, elementwise_affine=False, eps=1e-6)
self.final_adaLN = nn.Sequential(
nn.SiLU(),
nn.Linear(cond_dim, 2 * hidden_dim, bias=True)
)
self.out_proj = nn.Linear(hidden_dim, in_channels)
# Zero init final layer for ultra-stable flow matching training
nn.init.constant_(self.final_adaLN[-1].weight, 0)
nn.init.constant_(self.final_adaLN[-1].bias, 0)
nn.init.constant_(self.out_proj.weight, 0)
nn.init.constant_(self.out_proj.bias, 0)
@staticmethod
def get_sinusoidal_pos_embedding(seq_len: int, dim: int, device: torch.device) -> torch.Tensor:
"""Computes 1D sinusoidal positional embeddings for sequence length seq_len."""
half_dim = dim // 2
inv_freq = 1.0 / (10000.0 ** (torch.arange(0, half_dim, device=device).float() / half_dim))
t = torch.arange(seq_len, device=device, dtype=torch.float)
freqs = torch.einsum("i,j->ij", t, inv_freq)
emb = torch.cat((freqs.sin(), freqs.cos()), dim=-1)
if dim % 2:
emb = torch.cat([emb, torch.zeros_like(emb[:, :1])], dim=-1)
return emb.unsqueeze(0) # [1, seq_len, dim]
def forward(
self,
x_t: torch.Tensor,
t: torch.Tensor,
text_cond: torch.Tensor,
ref_mel: Optional[torch.Tensor] = None,
drop_cond: bool = False,
cond_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""
Args:
x_t: Noisy audio latents [B, T_audio, in_channels]
t: Continuous timesteps [B] in [0, 1]
text_cond: Text representation [B, T_text, cond_dim]
ref_mel: Optional 3-5 sec human reference audio mel [B, T_ref, 80]
drop_cond: If True, uses null condition for CFG sampling
cond_mask: Optional boolean padding mask for text_cond [B, T_text]
Returns:
v_t: Predicted velocity field [B, T_audio, in_channels]
"""
B = x_t.shape[0]
# Time conditioning
t_emb = self.time_proj(self.time_embedder(t))
# Handle Classifier-Free Guidance (CFG) conditioning
ref_latents = None
if ref_mel is not None:
ref_latents = self.ref_audio_proj(ref_mel)
total_cond_len = text_cond.shape[1] + (ref_latents.shape[1] if ref_latents is not None else 0)
if drop_cond:
full_cond = self.null_cond.expand(B, total_cond_len, -1)
full_mask = None
else:
if ref_latents is not None:
full_cond = torch.cat([ref_latents, text_cond], dim=1)
if cond_mask is not None:
ref_mask = torch.ones((B, ref_latents.shape[1]), dtype=torch.bool, device=cond_mask.device)
full_mask = torch.cat([ref_mask, cond_mask], dim=1)
else:
full_mask = None
else:
full_cond = text_cond
full_mask = cond_mask
# Audio projection with 1D Temporal Positional Embeddings
h = self.in_proj(x_t)
pos_emb = self.get_sinusoidal_pos_embedding(h.shape[1], self.hidden_dim, h.device)
h = h + pos_emb
# Pass through DiT blocks
for block in self.blocks:
h = block(h, t_emb, full_cond, cond_mask=full_mask)
# Final modulation and projection to target velocity
shift, scale = self.final_adaLN(t_emb).chunk(2, dim=-1)
h = modulate(self.final_norm(h), shift, scale)
return self.out_proj(h)
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