format

mlx_video/models/ltx_2/samplers.py

@@ -5,15 +5,14 @@ noise injection, ported from the LTX-2 PyTorch implementation.
 """
 
 import math
-from typing import Optional
 
 import mlx.core as mx
 
-
 # ---------------------------------------------------------------------------
 # Phi functions and RK coefficients (pure Python math, no MLX needed)
 # ---------------------------------------------------------------------------
 
+
 def phi(j: int, neg_h: float) -> float:
     """Compute phi_j(z) where z = -h (negative step size in log-space).
 
@@ -43,6 +42,7 @@ def get_res2s_coefficients(
     Returns:
         (a21, b1, b2): RK coefficients.
     """
+
     def get_phi(j: int, neg_h: float) -> float:
         cache_key = (j, neg_h)
         if cache_key in phi_cache:
@@ -69,6 +69,7 @@ def get_res2s_coefficients(
 # SDE noise injection
 # ---------------------------------------------------------------------------
 
+
 def get_sde_coeff(
     sigma_next: float,
 ) -> tuple[float, float, float]:
@@ -139,7 +140,9 @@ def sde_noise_step(
     denoised_next = sample_f32 - sigma * eps_next
 
     # Mix deterministic and stochastic components
-    x_noised = alpha_ratio * (denoised_next + sigma_down * eps_next) + sigma_up * noise_f32
+    x_noised = (
+        alpha_ratio * (denoised_next + sigma_down * eps_next) + sigma_up * noise_f32
+    )
 
     return x_noised
 
@@ -148,6 +151,7 @@ def sde_noise_step(
 # Noise generation
 # ---------------------------------------------------------------------------
 
+
 def channelwise_normalize(x: mx.array) -> mx.array:
     """Normalize each channel to zero mean and unit variance over spatial dims.