From 66e10910dc7c18078c3effe8aecb1c2ed966af6d Mon Sep 17 00:00:00 2001 From: kijai <40791699+kijai@users.noreply.github.com> Date: Sat, 30 Aug 2025 21:06:54 +0300 Subject: [PATCH] trim trailing whitespace --- comfy/audio_encoders/audio_encoders.py | 2 +- comfy/audio_encoders/wav2vec2.py | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/comfy/audio_encoders/audio_encoders.py b/comfy/audio_encoders/audio_encoders.py index 15c946c3a..b452ff447 100644 --- a/comfy/audio_encoders/audio_encoders.py +++ b/comfy/audio_encoders/audio_encoders.py @@ -43,7 +43,7 @@ def load_audio_encoder_from_sd(sd, prefix=""): sd = comfy.utils.state_dict_prefix_replace(sd, {"wav2vec2.": ""}) embed_dim = sd["encoder.layer_norm.bias"].shape[0] if embed_dim == 1024:# large - config = { + config = { "embed_dim": 1024, "num_heads": 16, "num_layers": 24, diff --git a/comfy/audio_encoders/wav2vec2.py b/comfy/audio_encoders/wav2vec2.py index 16e24b3c4..4933b7501 100644 --- a/comfy/audio_encoders/wav2vec2.py +++ b/comfy/audio_encoders/wav2vec2.py @@ -12,7 +12,7 @@ class LayerNormConv(nn.Module): def forward(self, x): x = self.conv(x) return torch.nn.functional.gelu(self.layer_norm(x.transpose(-2, -1)).transpose(-2, -1)) - + class LayerGroupNormConv(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None): super().__init__() @@ -22,7 +22,7 @@ class LayerGroupNormConv(nn.Module): def forward(self, x): x = self.conv(x) return torch.nn.functional.gelu(self.layer_norm(x)) - + class ConvNoNorm(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None): super().__init__() @@ -250,13 +250,13 @@ class Wav2Vec2Model(nn.Module): audio_duration = x.shape[1] / sr target_seq_len = int(audio_duration * fps) features = self.linear_interpolation(features, target_seq_len) - + features = self.feature_projection(features) batch_size, seq_len, _ = features.shape x, all_x = self.encoder(features) return x, all_x - + def linear_interpolation(self, features, target_seq_len): features = features.transpose(1, 2) output_features = torch.nn.functional.interpolate(features, size=target_seq_len, align_corners=True, mode='linear')