Fix SDE inference: batch size mismatch with CFG and incorrect timestep schedule

modeling_acestep_v15_base.py

@@ -1949,8 +1949,7 @@ class AceStepConditionGenerationModel(AceStepPreTrainedModel):
                     # Stochastic Differential Equation: predict clean, then re-add noise
                     t_curr_bsz = t_curr * torch.ones((bsz,), device=device, dtype=dtype)
                     pred_clean = self.get_x0_from_noise(xt, vt, t_curr_bsz)
-                    next_timestep = 1.0 - (float(step_idx + 1) / infer_steps)
-                    xt = self.renoise(pred_clean, next_timestep)
+                    xt = self.renoise(pred_clean, t_prev)
                 elif infer_method == "ode":
                     # Ordinary Differential Equation: Euler method
                     # dx/dt = -v, so x_{t+1} = x_t - v_t * dt