original values restored

Author: prishajain1

src/maxdiffusion/configs/base_wan_27b.yml

@@ -305,10 +305,10 @@ boundary_ratio: 0.875
 # Diffusion CFG cache (FasterCache-style)
 use_cfg_cache: False
 use_magcache: True
-magcache_thresh: 0.12
+magcache_thresh: 0.06
 magcache_K: 2
-retention_ratio: 0.2
-mag_ratios_base: [1.0, 1.0, 0.99505, 0.99389, 0.99441, 0.9957, 0.99558, 0.99551, 0.99499, 0.9945, 0.99534, 0.99548, 0.99468, 0.9946, 0.99463, 0.99458, 0.9946, 0.99453, 0.99408, 0.99404, 0.9945, 0.99441, 0.99409, 0.99398, 0.99403, 0.99397, 0.99382, 0.99377, 0.99349, 0.99343, 0.99377, 0.99378, 0.9933, 0.99328, 0.99303, 0.99301, 0.99217, 0.99216, 0.992, 0.99201, 0.99201, 0.99202, 0.99133, 0.99132, 0.99112, 0.9911, 0.99155, 0.99155, 0.98958, 0.98957, 0.98959, 0.98958, 0.98838, 0.98835, 0.98826, 0.98825, 0.9883, 0.98828, 0.98711, 0.98709, 0.98562, 0.98561, 0.98511, 0.9851, 0.98414, 0.98412, 0.98284, 0.98282, 0.98104, 0.98101, 0.97981, 0.97979, 0.97849, 0.97849, 0.97557, 0.97554, 0.97398, 0.97395, 0.97171, 0.97166, 0.96917, 0.96913, 0.96511, 0.96507, 0.96263, 0.96257, 0.95839, 0.95835, 0.95483, 0.95475, 0.94942, 0.94936, 0.9468, 0.94678, 0.94583, 0.94594, 0.94843, 0.94872, 0.96949, 0.97015]
+retention_ratio: 0.4
+mag_ratios_base: [1.0, 1.0, 1.00124, 1.00155, 0.99822, 0.99851, 0.99696, 0.99687, 0.9977, 0.99732, 0.9966, 0.99679, 0.99602, 0.99658, 0.99578, 0.99664, 0.99484, 0.9949, 0.99633, 0.996, 0.99659, 0.99683, 0.99534, 0.99549, 0.99584, 0.99577, 0.99681, 0.99694, 0.99563, 0.99554, 0.9944, 0.99473, 0.99594, 0.9964, 0.99466, 0.99461, 0.99453, 0.99481, 0.99389, 0.99365, 0.99391, 0.99406, 0.99354, 0.99361, 0.99283, 0.99278, 0.99268, 0.99263, 0.99057, 0.99091, 0.99125, 0.99126, 0.65523, 0.65252, 0.98808, 0.98852, 0.98765, 0.98736, 0.9851, 0.98535, 0.98311, 0.98339, 0.9805, 0.9806, 0.97776, 0.97771, 0.97278, 0.97286, 0.96731, 0.96728, 0.95857, 0.95855, 0.94385, 0.94385, 0.92118, 0.921, 0.88108, 0.88076, 0.80263, 0.80181]
 
 # SenCache: Sensitivity-Aware Caching (arXiv:2602.24208) — skip forward pass
 # when predicted output change (based on accumulated latent/timestep drift) is small

src/maxdiffusion/pipelines/wan/wan_pipeline_i2v_2p2.py

@@ -531,6 +531,7 @@ def run_inference_2_2_i2v(
         cached_residual = residual_x_cur
 
       noise_pred = jnp.transpose(noise_pred, (0, 2, 3, 4, 1))
+      noise_pred, _ = jnp.split(noise_pred, [16], axis=-1)
       latents, scheduler_state = scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()
     return latents