diff --git a/opensora/models/vae/vae_3d_v2.py b/opensora/models/vae/vae_3d_v2.py
index ff376a1..8a81e2c 100644
--- a/opensora/models/vae/vae_3d_v2.py
+++ b/opensora/models/vae/vae_3d_v2.py
@@ -691,7 +691,7 @@ class VAE_3D_V2(nn.Module):
         # # Perceptual Loss
         # self.vgg = None
         # self.perceptual_loss_weight = perceptual_loss_weight
-        # if perceptual_loss_weight is not None and perceptual_loss_weight > 0:
+        # if perceptual_loss_weight is not None and perceptual_loss_weight > 0.0:
         #     # self.lpips = LPIPS().eval()
         #     if not exists(vgg):
         #         vgg = torchvision.models.vgg16(
@@ -704,7 +704,7 @@ class VAE_3D_V2(nn.Module):
         # self.discriminator_factor = discriminator_factor
         # self.discriminator_start = discriminator_start
         # self.discriminator = None
-        # if discriminator_factor is not None and discriminator_factor > 0:
+        # if discriminator_factor is not None and discriminator_factor > 0.0:
         #     self.discriminator = StyleGANDiscriminator(
         #         image_size = image_size,
         #         num_frames = num_frames,
@@ -842,7 +842,7 @@ class VAE_3D_V2(nn.Module):
 
         # # KL Loss
         # weighted_kl_loss = 0
-        # if self.kl_loss_weight is not None and self.kl_loss_weight > 0:
+        # if self.kl_loss_weight is not None and self.kl_loss_weight > 0.0:
         #     kl_loss = posterior.kl()
         #     # NOTE: since we use MSE, here use mean as well, else use sum
         #     kl_loss = torch.mean(kl_loss) / kl_loss.shape[0]
@@ -852,7 +852,7 @@ class VAE_3D_V2(nn.Module):
         # # Perceptual Loss
         # # SCH: NOTE: if mse can pick single frame, if use sum of errors, need to calc for all frames!
         # weighted_perceptual_loss = 0
-        # if self.perceptual_loss_weight is not None and self.perceptual_loss_weight > 0:
+        # if self.perceptual_loss_weight is not None and self.perceptual_loss_weight > 0.0:
         #     frame_indices = torch.randn((batch, frames)).topk(1, dim = -1).indices
         #     input_vgg_input = pick_video_frame(video, frame_indices)
         #     recon_vgg_input = pick_video_frame(recon_video, frame_indices)
@@ -954,7 +954,7 @@ class VEALoss(nn.Module):
 
         # KL Loss
         weighted_kl_loss = 0
-        if self.kl_loss_weight is not None and self.kl_loss_weight > 0:
+        if self.kl_loss_weight is not None and self.kl_loss_weight > 0.0:
             kl_loss = posterior.kl()
             # NOTE: since we use MSE, here use mean as well, else use sum
             kl_loss = torch.mean(kl_loss) / kl_loss.shape[0]
@@ -963,7 +963,7 @@ class VEALoss(nn.Module):
 
         # perceptual loss
         weighted_perceptual_loss = 0
-        if self.perceptual_loss_weight is not None and self.perceptual_loss_weight > 0:
+        if self.perceptual_loss_weight is not None and self.perceptual_loss_weight > 0.0:
             assert exists(self.vgg)
             batch, channels, frames = video.shape[:3]
             frame_indices = torch.randn((batch, frames)).topk(1, dim = -1).indices