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import torch
import torch.nn as nn
from diffusers import UNet2DModel, UNet2DConditionModel
import yaml
from einops import repeat, rearrange
from typing import Any
from torch import Tensor
def rand_bool(shape: Any, proba: float, device: Any = None) -> Tensor:
if proba == 1:
return torch.ones(shape, device=device, dtype=torch.bool)
elif proba == 0:
return torch.zeros(shape, device=device, dtype=torch.bool)
else:
return torch.bernoulli(torch.full(shape, proba, device=device)).to(torch.bool)
class FixedEmbedding(nn.Module):
def __init__(self, features=128):
super().__init__()
self.embedding = nn.Embedding(1, features)
def forward(self, y):
B, L, C, device = y.shape[0], y.shape[-2], y.shape[-1], y.device
embed = self.embedding(torch.zeros(B, device=device).long())
fixed_embedding = repeat(embed, "b c -> b l c", l=L)
return fixed_embedding
class DiffVC_Cross(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.unet = UNet2DConditionModel(**self.config['unet'])
self.unet.set_use_memory_efficient_attention_xformers(True)
self.cfg_embedding = FixedEmbedding(self.config['unet']['cross_attention_dim'])
self.context_embedding = nn.Sequential(
nn.Linear(self.config['unet']['cross_attention_dim'], self.config['unet']['cross_attention_dim']),
nn.SiLU(),
nn.Linear(self.config['unet']['cross_attention_dim'], self.config['unet']['cross_attention_dim']))
self.content_embedding = nn.Sequential(
nn.Linear(self.config['cls_embedding']['content_dim'], self.config['cls_embedding']['content_hidden']),
nn.SiLU(),
nn.Linear(self.config['cls_embedding']['content_hidden'], self.config['cls_embedding']['content_hidden']))
if self.config['cls_embedding']['use_pitch']:
self.pitch_control = True
self.pitch_embedding = nn.Sequential(
nn.Linear(self.config['cls_embedding']['pitch_dim'], self.config['cls_embedding']['pitch_hidden']),
nn.SiLU(),
nn.Linear(self.config['cls_embedding']['pitch_hidden'],
self.config['cls_embedding']['pitch_hidden']))
self.pitch_uncond = nn.Parameter(torch.randn(self.config['cls_embedding']['pitch_hidden']) /
self.config['cls_embedding']['pitch_hidden'] ** 0.5)
else:
print('no pitch module')
self.pitch_control = False
def forward(self, target, t, content, prompt, prompt_mask=None, pitch=None,
train_cfg=False, speaker_cfg=0.0, pitch_cfg=0.0):
B, C, M, L = target.shape
content = self.content_embedding(content)
content = repeat(content, "b t c-> b c m t", m=M)
target = target.to(content.dtype)
x = torch.cat([target, content], dim=1)
if self.pitch_control:
if pitch is not None:
pitch = self.pitch_embedding(pitch)
else:
pitch = repeat(self.pitch_uncond, "c-> b t c", b=B, t=L).to(target.dtype)
if train_cfg:
# Randomly mask embedding
batch_mask = rand_bool(shape=(B, 1, 1), proba=speaker_cfg, device=target.device)
fixed_embedding = self.cfg_embedding(prompt).to(target.dtype)
prompt = torch.where(batch_mask, fixed_embedding, prompt)
if self.pitch_control:
batch_mask = rand_bool(shape=(B, 1, 1), proba=pitch_cfg, device=target.device)
pitch_uncond = repeat(self.pitch_uncond, "c-> b t c", b=B, t=L).to(target.dtype)
pitch = torch.where(batch_mask, pitch_uncond, pitch)
prompt = self.context_embedding(prompt)
if self.pitch_control:
pitch = repeat(pitch, "b t c-> b c m t", m=M)
x = torch.cat([x, pitch], dim=1)
output = self.unet(sample=x, timestep=t,
encoder_hidden_states=prompt,
encoder_attention_mask=prompt_mask)['sample']
return output
if __name__ == "__main__":
with open('diffvc_cross_pitch.yaml', 'r') as fp:
config = yaml.safe_load(fp)
device = 'cuda'
model = DiffVC_Cross(config['diffwrap']).to(device)
x = torch.rand((2, 1, 100, 256)).to(device)
y = torch.rand((2, 256, 768)).to(device)
t = torch.randint(0, 1000, (2,)).long().to(device)
prompt = torch.rand(2, 64, 768).to(device)
prompt_mask = torch.ones(2, 64).to(device)
p = torch.rand(2, 256, 1).to(device)
output = model(x, t, y, prompt, prompt_mask, p, train_cfg=True, speaker_cfg=0.25, pitch_cfg=0.5) |