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import math
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.models.auto import AutoModel
from transformers.modeling_utils import PreTrainedModel
# from transformers.modeling_layers import GradientCheckpointingLayer
from transformers.activations import ACT2FN
from transformers.utils import logging
from .configuration_vibevoice import VibeVoiceDiffusionHeadConfig
logger = logging.get_logger(__name__)
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6, elementwise_affine=True, memory_efficient=False):
super().__init__()
self.dim = dim
self.eps = eps
self.elementwise_affine = elementwise_affine
if self.elementwise_affine:
self.weight = nn.Parameter(torch.ones(dim))
else:
self.register_parameter('weight', None)
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
output = self._norm(x.float()).type_as(x)
if self.weight is not None:
output = output * self.weight
return output
def extra_repr(self) -> str:
return f'dim={self.dim}, eps={self.eps}, elementwise_affine={self.elementwise_affine}'
def modulate(x, shift, scale):
"""Apply modulation to input tensor."""
return x * (1 + scale) + shift
class TimestepEmbedder(nn.Module):
"""
Embeds scalar timesteps into vector representations.
Args:
hidden_size (`int`): Size of the output embedding
frequency_embedding_size (`int`, optional): Size of the intermediate frequency embedding
"""
def __init__(self, hidden_size, frequency_embedding_size=256):
super().__init__()
self.mlp = nn.Sequential(
nn.Linear(frequency_embedding_size, hidden_size, bias=False),
# nn.SiLU(),
ACT2FN['silu'],
nn.Linear(hidden_size, hidden_size, bias=False),
)
self.frequency_embedding_size = frequency_embedding_size
@staticmethod
def timestep_embedding(t, dim, max_period=10000):
"""
Create sinusoidal timestep embeddings.
Args:
t (`torch.Tensor`): A 1-D Tensor of N indices, one per batch element.
These may be fractional.
dim (`int`): The dimension of the output.
max_period (`int`, optional): Controls the minimum frequency of the embeddings.
Returns:
`torch.Tensor`: An [N, D] Tensor of positional embeddings.
"""
half = dim // 2
freqs = torch.exp(
-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
).to(t.device)
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.to(t.dtype)
def forward(self, t):
t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
t_emb = self.mlp(t_freq)
return t_emb
class FeedForwardNetwork(nn.Module):
"""
Standard feed-forward network with SwiGLU activation.
Args:
embed_dim (`int`): Input dimension
ffn_dim (`int`): Hidden dimension
"""
def __init__(
self,
embed_dim,
ffn_dim,
):
super().__init__()
self.embed_dim = embed_dim
self.gate_proj = nn.Linear(self.embed_dim, ffn_dim, bias=False)
self.up_proj = nn.Linear(self.embed_dim, ffn_dim, bias=False)
self.down_proj = nn.Linear(ffn_dim, self.embed_dim, bias=False)
self.act_fn = ACT2FN['silu'] # Using SiLU as the activation function
def forward(self, x):
gate = self.gate_proj(x)
up = self.up_proj(x)
# SwiGLU activation
# gate = F.silu(gate)
gate = self.act_fn(gate)
return self.down_proj(gate * up)
class HeadLayer(nn.Module):
"""
A layer in the diffusion head.
Args:
embed_dim (`int`): Input dimension
ffn_dim (`int`): Hidden dimension
cond_dim (`int`): Condition embedding dimension
norm_eps (`float`, optional): Epsilon for normalization
"""
def __init__(
self,
embed_dim,
ffn_dim,
cond_dim,
norm_eps=1e-5,
):
super().__init__()
self.embed_dim = embed_dim
self.cond_dim = cond_dim
self.ffn_dim = ffn_dim
self.ffn = FeedForwardNetwork(
self.embed_dim,
self.ffn_dim,
)
self.norm = RMSNorm(self.embed_dim, eps=norm_eps)
self.adaLN_modulation = nn.Sequential(
# nn.SiLU(),
ACT2FN['silu'],
nn.Linear(cond_dim, 3 * self.embed_dim, bias=False)
)
def forward(self, x, c):
shift_ffn, scale_ffn, gate_ffn = self.adaLN_modulation(c).chunk(3, dim=-1)
x = x + gate_ffn * self.ffn(modulate(self.norm(x), shift_ffn, scale_ffn))
return x
class FinalLayer(nn.Module):
"""
Final layer in the diffusion head.
Args:
hidden_size (`int`): Input dimension
output_size (`int`): Output dimension
cond_size (`int`): Condition embedding dimension
norm_eps (`float`, optional): Epsilon for normalization
"""
def __init__(self, hidden_size, output_size, cond_size, norm_eps=1e-5):
super().__init__()
self.norm_final = RMSNorm(hidden_size, eps=norm_eps, elementwise_affine=False)
self.linear = nn.Linear(hidden_size, output_size, bias=False)
self.adaLN_modulation = nn.Sequential(
# nn.SiLU(),
ACT2FN['silu'],
nn.Linear(cond_size, 2 * hidden_size, bias=False)
)
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 VibeVoiceDiffusionHead(PreTrainedModel):
"""
Diffusion head model for vibevoice.
Args:
config (`VibeVoiceDiffusionHeadConfig`): Model configuration
latent_size (`int`, optional): Size of the latent space. If not provided, uses `config.latent_size`.
"""
config_class = VibeVoiceDiffusionHeadConfig
supports_gradient_checkpointing = True
_supports_flash_attn_2 = True
_supports_sdpa = True
def __init__(
self,
config,
):
super().__init__(config)
self.config = config
self.cond_dim = config.hidden_size
latent_size = config.latent_size
self.noisy_images_proj = nn.Linear(latent_size, config.hidden_size, bias=False)
self.cond_proj = nn.Linear(config.hidden_size, self.cond_dim, bias=False)
self.t_embedder = TimestepEmbedder(self.cond_dim)
ffn_dim = int(config.hidden_size * config.head_ffn_ratio)
# Create the intermediate layers
self.layers = nn.ModuleList([
HeadLayer(
embed_dim=config.hidden_size,
ffn_dim=ffn_dim,
cond_dim=self.cond_dim,
norm_eps=config.rms_norm_eps
)
for _ in range(config.head_layers)
])
# Final layer for output
self.final_layer = FinalLayer(
hidden_size=config.hidden_size,
output_size=latent_size,
cond_size=self.cond_dim,
norm_eps=config.rms_norm_eps
)
self.initialize_weights()
def initialize_weights(self):
"""Initialize the weights of the model."""
# Initialize timestep embedder
nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
# Zero-out adaLN modulation layers
for layer in self.layers:
nn.init.constant_(layer.adaLN_modulation[-1].weight, 0)
# Zero-out output layers
nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
nn.init.constant_(self.final_layer.linear.weight, 0)
def forward(
self,
noisy_images,
timesteps,
condition,
):
"""
Forward pass of the prediction head.
Args:
noisy_images (`torch.Tensor`): Noisy images/latents to denoise
timesteps (`torch.Tensor`): Timesteps for diffusion
condition (`torch.Tensor`): Conditioning information
Returns:
`torch.Tensor`: The predicted noise/velocity
"""
x = self.noisy_images_proj(noisy_images)
t = self.t_embedder(timesteps)
condition = self.cond_proj(condition)
c = condition + t
for layer in self.layers:
x = layer(x, c)
x = self.final_layer(x, c)
return x
AutoModel.register(VibeVoiceDiffusionHeadConfig, VibeVoiceDiffusionHead)
__all__ = [
"VibeVoiceDiffusionHead",
] |