修复混合精度vae相关的配置错误，确保在推理阶段正确使用了混合精度模型，并且导出了正确精度的检查点文件。

src/unifolm_wma/models/ddpms.py

@@ -1105,6 +1105,10 @@ class LatentDiffusion(DDPM):
         else:
             reshape_back = False
 
+        # Align input dtype with VAE weights (e.g. fp32 samples → bf16 VAE)
+        vae_dtype = next(self.first_stage_model.parameters()).dtype
+        z = z.to(dtype=vae_dtype)
+
         if not self.perframe_ae:
             z = 1. / self.scale_factor * z
             results = self.first_stage_model.decode(z, **kwargs)
@@ -2457,7 +2461,6 @@ class DiffusionWrapper(pl.LightningModule):
         Returns:
             Output from the inner diffusion model (tensor or tuple, depending on the model).
         """
-
         if self.conditioning_key is None:
             out = self.diffusion_model(x, t)
         elif self.conditioning_key == 'concat':