diff --git a/diffsynth_engine/models/ace_step/ace_dit.py b/diffsynth_engine/models/ace_step/ace_dit.py
index 09a4092..c319e4e 100644
--- a/diffsynth_engine/models/ace_step/ace_dit.py
+++ b/diffsynth_engine/models/ace_step/ace_dit.py
@@ -10,30 +10,42 @@
 from diffsynth_engine.models.basic.timestep import TimestepEmbeddings
 from diffsynth_engine.models.basic.attention import attention
 from diffsynth_engine.models.basic.transformer_helper import RMSNorm
-from diffsynth_engine.models.wan.wan_dit import rope_apply, modulate
+from diffsynth_engine.models.wan.wan_dit import modulate
 from diffsynth_engine.models.ace_step.ace_lyric_encoder import ConformerEncoder
 from diffsynth_engine.utils.constants import ACE_DIT_CONFIG_FILE
 
 
-class Qwen2RotaryEmbedding(nn.Module):
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device="cuda:0"):
-        super().__init__() # TODO: how to deal with meta device issue?
-        device = "cuda:2"
-        self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device=device, dtype=torch.int64).float() / dim))
+def rope_apply(x, freqs): # TODO: edit this into complex calculation
+    cos, sin = freqs  # [1, S, 1, D]
+    x_real, x_imag = x.reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
+    x_rotated = torch.stack([-x_imag, x_real], dim=-1).flatten(3)
+    x_out = (x.float() * cos + x_rotated.float() * sin).to(x.dtype)
+    return x_out.to(x.dtype).flatten(3)
+
+
+class Qwen2RotaryEmbedding:
+    def __init__(self, dim, max_position_embeddings=2048, base=10000):
+        super().__init__()
+        self.inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
         self._set_cos_sin_cache(seq_len=max_position_embeddings)
 
     def _set_cos_sin_cache(self, seq_len):
         self.max_seq_len_cached = seq_len
-        t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.int64).float()
+        t = torch.arange(self.max_seq_len_cached, dtype=torch.float32)
         freqs = torch.outer(t, self.inv_freq)
-        self.freqs_cis_cached = torch.polar(torch.ones_like(freqs), freqs)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.cos_cached = emb.cos()
+        self.sin_cached = emb.sin()
 
-    def forward(self, x: torch.Tensor):
+    def __call__(self, x: torch.Tensor):
         seq_len = x.shape[1]
         if seq_len > self.max_seq_len_cached:
             self._set_cos_sin_cache(seq_len=seq_len)
 
-        return self.freqs_cis_cached[:seq_len][None, :, None, :].to(x.device)
+        return (
+            self.cos_cached[None, :seq_len, None, :].to(x),
+            self.sin_cached[None, :seq_len, None, :].to(x),
+        )
 
 
 class SelfAttention(nn.Module):
@@ -175,7 +187,7 @@ def __init__(
             kernel_size=3,
             groups=hidden_features * 2,
             use_bias=True,
-            act="silu",
+            act=None,
             device=device,
             dtype=dtype,
         )
@@ -194,7 +206,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         x = self.inverted_conv(x)
         x = self.depth_conv(x)
         x, gate = torch.chunk(x, 2, dim=1)
-        x *= self.glu_act(gate)
+        x = x * self.glu_act(gate)
         x = self.point_conv(x)
         x = x.transpose(1, 2)
         return x
@@ -285,7 +297,7 @@ def forward(self, x, t):
 
 
 class ACEStepDiTStateDictConverter(StateDictConverter):
-    def convert(self, state_dict):
+    def convert(self, state_dict): # TODO: can this be more elegant?
         for key in list(state_dict.keys()):
             # change all linear_q / linear_k / linear_v / linear_p to q / k / v / p
             if "linear_q" in key:
@@ -361,7 +373,6 @@ def __init__(
             dim=head_dim,
             max_position_embeddings=max_position,
             base=rope_theta,
-            device=device,
         )
 
         inner_dim = num_heads * head_dim
diff --git a/diffsynth_engine/pipelines/ace_step.py b/diffsynth_engine/pipelines/ace_step.py
index 4a8de83..9e94f70 100644
--- a/diffsynth_engine/pipelines/ace_step.py
+++ b/diffsynth_engine/pipelines/ace_step.py
@@ -1,5 +1,6 @@
 from typing import Tuple
 
+import math
 import torch
 import torch.nn.functional as F
 import torch.distributed as dist
@@ -51,11 +52,11 @@ def hook(module, input, output):
             handlers.append(handler)
 
     with torch.no_grad():
-        prompt_emb = model_fwd_func(**inputs)
+        output = model_fwd_func(**inputs)
 
     for handler in handlers:
         handler.remove()
-    return prompt_emb
+    return output
 
 
 class MomentumBuffer:
@@ -124,7 +125,7 @@ def __init__(
         )
         self.config = config
         # sampler
-        self.noise_scheduler = RecifitedFlowScheduler(shift=3.0)
+        self.noise_scheduler = RecifitedFlowScheduler(shift=config.shift)
         self.sampler = FlowMatchEulerSampler()
         # models
         self.lyric_tokenizer = VoiceBpeTokenizer()
@@ -300,6 +301,10 @@ def text2audio(
         guidance_interval: float = 0.5,
         progress_callback: Optional[Callable[[int, int, str], None]] = None,
     ):
+        def logistic(x, L=0.9, U=1.1, x_0=0.0, k=0.1):
+                return L + (U - L) / (1 + math.exp(-k * (x - x_0)))
+        omega = logistic(omega_scale)
+
         prompt_emb, prompt_attn_mask = self.encode_prompt(prompt)
         prompt_emb_null, prompt_attn_mask_null = self.encode_prompt_null(prompt)
         if len(lyrics.strip()) > 0:
@@ -319,7 +324,7 @@ def text2audio(
         )
         # Initialize sampler
         self.sampler.initialize(sigmas=sigmas)
-        # guidance interval
+        # Guidance interval
         cfg_start_step = int(num_inference_steps * ((1 - guidance_interval) / 2))
         cfg_end_step = int(num_inference_steps * (guidance_interval / 2 + 0.5))
         momentum_buffer = MomentumBuffer()
@@ -369,7 +374,7 @@ def text2audio(
             dx: torch.Tensor = noise_pred * (self.sampler.sigmas[i + 1] - self.sampler.sigmas[i])
             dx_mean = dx.mean(dim=(1, 2, 3), keepdim=True)
             latents = latents.to(dtype=torch.float32)
-            latents += (dx - dx_mean) * omega_scale + dx
+            latents += (dx - dx_mean) * omega + dx_mean
             latents = latents.to(dtype=noise_pred.dtype)
             if progress_callback is not None:
                 progress_callback(i + 1, len(timesteps), "DENOISING")
diff --git a/diffsynth_engine/pipelines/base.py b/diffsynth_engine/pipelines/base.py
index 37fbccb..5c2e2ae 100644
--- a/diffsynth_engine/pipelines/base.py
+++ b/diffsynth_engine/pipelines/base.py
@@ -210,7 +210,7 @@ def prepare_latents(
             sigmas, timesteps = self.noise_scheduler.schedule(num_inference_steps)
             # k-diffusion
             # if you have any questions about this, please ask @dizhipeng.dzp for more details
-            latents = latents * sigmas[0] / ((sigmas[0] ** 2 + 1) ** 0.5)
+            # latents = latents * sigmas[0] / ((sigmas[0] ** 2 + 1) ** 0.5)
             init_latents = latents.clone()
         sigmas, timesteps = (
             sigmas.to(device=self.device, dtype=self.dtype),
diff --git a/examples/ace_text_to_music.py b/examples/ace_text_to_music.py
index f817485..a6eac32 100644
--- a/examples/ace_text_to_music.py
+++ b/examples/ace_text_to_music.py
@@ -1,4 +1,5 @@
-# import random
+import random
+import argparse
 
 from diffsynth_engine.configs import ACEStepPipelineConfig
 from diffsynth_engine.pipelines.ace_step import ACEStepMusicPipeline
@@ -7,21 +8,30 @@
 
 
 if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=random.randint(0, 2**32 - 1),
+    )
+    parser.add_argument("--device", type=str, default="cuda")
+    args = parser.parse_args()
+
     config = ACEStepPipelineConfig(
         model_path=fetch_model(
             model_uri="ACE-Step/ACE-Step-v1-3.5B",
             path="ace_step_transformer/diffusion_pytorch_model.safetensors",
         ),
-        device="cuda:2",
+        device=args.device,
     )
-    seed = 3299954530
 
     pipe = ACEStepMusicPipeline.from_pretrained(config)
     audio = pipe.text2audio(
         prompt="pop, rap, electronic, blues, hip-house, rhythm and blues",
         lyrics="[verse]\n我走过深夜的街道\n冷风吹乱思念的漂亮外套\n你的微笑像星光很炫耀\n照亮了我孤独的每分每秒\n\n[chorus]\n愿你是风吹过我的脸\n带我飞过最远最遥远的山间\n愿你是风轻触我的梦\n停在心头不再飘散无迹无踪\n\n[verse]\n一起在喧哗避开世俗的骚动\n独自在天台探望月色的朦胧\n你说爱像音乐带点重节奏\n一拍一跳让我忘了心的温度多空洞\n\n[bridge]\n唱起对你的想念不隐藏\n像诗又像画写满藏不了的渴望\n你的影子挥不掉像风的倔强\n追着你飞扬穿越云海一样泛光\n\n[chorus]\n愿你是风吹过我的手\n暖暖的触碰像春日细雨温柔\n愿你是风盘绕我的身\n深情万万重不会有一天走远走\n\n[verse]\n深夜的钢琴弹起动人的旋律\n低音鼓砸进心底的每一次呼吸\n要是能将爱化作歌声传递\n你是否会听见我心里的真心实意",
         audio_duration=170.63997916666668,
+        seed=args.seed,
     )
-    save_audio(audio, f"tmp/ace_t2m_{seed}")
+    save_audio(audio, f"tmp/ace_t2m_{args.seed}")
 
     del pipe
diff --git a/pyproject.toml b/pyproject.toml
index 8b4c119..2c5dde9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -35,7 +35,7 @@ dependencies = [
     "moviepy",
     "librosa",
     "scikit-image",
-    "trimesh"
+    "trimesh",
     "py3langid",
     "pypinyin",
     "hangul_romanize",