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torch==2.4.1 #35

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8 changes: 4 additions & 4 deletions mmaudio/ext/autoencoder/vae.py
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Original file line number Diff line number Diff line change
Expand Up @@ -75,11 +75,11 @@ def __init__(
super().__init__()

if data_dim == 80:
self.data_mean = nn.Buffer(torch.tensor(DATA_MEAN_80D, dtype=torch.float32))
self.data_std = nn.Buffer(torch.tensor(DATA_STD_80D, dtype=torch.float32))
self.register_buffer("data_mean", torch.tensor(DATA_MEAN_80D, dtype=torch.float32))
self.register_buffer("data_std", torch.tensor(DATA_STD_80D, dtype=torch.float32))
elif data_dim == 128:
self.data_mean = nn.Buffer(torch.tensor(DATA_MEAN_128D, dtype=torch.float32))
self.data_std = nn.Buffer(torch.tensor(DATA_STD_128D, dtype=torch.float32))
self.register_buffer("data_mean", torch.tensor(DATA_MEAN_128D, dtype=torch.float32))
self.register_buffer("data_std", torch.tensor(DATA_STD_128D, dtype=torch.float32))

self.data_mean = self.data_mean.view(1, -1, 1)
self.data_std = self.data_std.view(1, -1, 1)
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14 changes: 7 additions & 7 deletions mmaudio/model/embeddings.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,13 +20,13 @@ def __init__(self, dim, frequency_embedding_size, max_period):
self.max_period = max_period
assert dim % 2 == 0, 'dim must be even.'

with torch.autocast('cuda', enabled=False):
self.freqs = nn.Buffer(
1.0 / (10000**(torch.arange(0, frequency_embedding_size, 2, dtype=torch.float32) /
frequency_embedding_size)),
persistent=False)
freq_scale = 10000 / max_period
self.freqs = freq_scale * self.freqs
self.register_buffer('freqs',
1.0 / (10000**(torch.arange(0, frequency_embedding_size, 2, dtype=torch.float32) /
frequency_embedding_size)),
persistent=False
)
freq_scale = 10000 / max_period
self.freqs = freq_scale * self.freqs

def timestep_embedding(self, t):
"""
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8 changes: 4 additions & 4 deletions mmaudio/model/networks.py
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Expand Up @@ -166,8 +166,8 @@ def initialize_rotations(self):
self._clip_seq_len,
device=self.device)

self.latent_rot = nn.Buffer(latent_rot, persistent=False)
self.clip_rot = nn.Buffer(clip_rot, persistent=False)
self.register_buffer('latent_rot', latent_rot, persistent=False)
self.register_buffer('clip_rot', clip_rot, persistent=False)

def update_seq_lengths(self, latent_seq_len: int, clip_seq_len: int, sync_seq_len: int) -> None:
self._latent_seq_len = latent_seq_len
Expand Down Expand Up @@ -238,9 +238,9 @@ def preprocess_conditions(self, clip_f: torch.Tensor, sync_f: torch.Tensor,
sync_f = sync_f.flatten(1, 2) # (B, VN, D)

# extend vf to match x
clip_f = self.clip_input_proj(clip_f) # (B, VN, D)
clip_f = self.clip_input_proj(clip_f.clone()) # (B, VN, D)
sync_f = self.sync_input_proj(sync_f) # (B, VN, D)
text_f = self.text_input_proj(text_f) # (B, VN, D)
text_f = self.text_input_proj(text_f.clone()) # (B, VN, D)

# upsample the sync features to match the audio
sync_f = sync_f.transpose(1, 2) # (B, D, VN)
Expand Down