diff --git a/src/maxdiffusion/models/ltx2/attention_ltx2.py b/src/maxdiffusion/models/ltx2/attention_ltx2.py
index 7441a2038..398b0f473 100644
--- a/src/maxdiffusion/models/ltx2/attention_ltx2.py
+++ b/src/maxdiffusion/models/ltx2/attention_ltx2.py
@@ -359,13 +359,13 @@ def __init__(
     # 1. Define Partitioned Initializers (Logical Axes)
     # Q, K, V kernels: [in_features (embed), out_features (heads)]
     qkv_kernel_init = nnx.with_partitioning(nnx.initializers.lecun_normal(), ("embed", "heads"))
-    # Q, K, V biases: [out_features (embed)]
-    qkv_bias_init = nnx.with_partitioning(nnx.initializers.zeros_init(), ("embed",))
+    # Q, K, V biases: [out_features (heads)]
+    qkv_bias_init = nnx.with_partitioning(nnx.initializers.zeros_init(), ("heads",))
 
     # Out kernel: [in_features (heads), out_features (embed)]
     out_kernel_init = nnx.with_partitioning(nnx.initializers.lecun_normal(), ("heads", "embed"))
-    # Out bias: [out_features (heads)]
-    out_bias_init = nnx.with_partitioning(nnx.initializers.zeros_init(), ("heads",))
+    # Out bias: [out_features (embed)]
+    out_bias_init = nnx.with_partitioning(nnx.initializers.zeros_init(), ("embed",))
 
     # Norm scales
     norm_scale_init = nnx.with_partitioning(nnx.initializers.ones_init(), ("norm",))
diff --git a/src/maxdiffusion/models/ltx2/latent_upsampler_ltx2.py b/src/maxdiffusion/models/ltx2/latent_upsampler_ltx2.py
index 1b43457af..20436f42f 100644
--- a/src/maxdiffusion/models/ltx2/latent_upsampler_ltx2.py
+++ b/src/maxdiffusion/models/ltx2/latent_upsampler_ltx2.py
@@ -165,12 +165,12 @@ def __init__(self, in_channels: int, mid_channels: int = 1024, scale: float = 2.
         in_channels, (num**2) * self.mid_channels, kernel_size=(3, 3), padding=((1, 1), (1, 1)), rngs=rngs
     )
     self.pixel_shuffle = PixelShuffleND(dims=2, upscale_factors=(num, num))
-    self.blur = BlurDownsample(dims=2, stride=den)
+    self.blur_down = BlurDownsample(dims=2, stride=den)
 
   def __call__(self, x: jax.Array) -> jax.Array:
     x = self.conv(x)
     x = self.pixel_shuffle(x)
-    x = self.blur(x)
+    x = self.blur_down(x)
     return x