model-code

diffsynth/models/ace_step_conditioner.py

@@ -0,0 +1,709 @@
+# Copyright 2025 The ACESTEO Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from einops import rearrange
+
+from ..core.attention import attention_forward
+from ..core.gradient import gradient_checkpoint_forward
+
+from transformers.cache_utils import Cache
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_outputs import BaseModelOutput
+from transformers.processing_utils import Unpack
+from transformers.utils import can_return_tuple, logging
+from transformers.models.qwen3.modeling_qwen3 import (
+    Qwen3MLP,
+    Qwen3RMSNorm,
+    Qwen3RotaryEmbedding,
+    apply_rotary_pos_emb,
+)
+
+logger = logging.get_logger(__name__)
+
+
+def create_4d_mask(
+    seq_len: int,
+    dtype: torch.dtype,
+    device: torch.device,
+    attention_mask: Optional[torch.Tensor] = None,
+    sliding_window: Optional[int] = None,
+    is_sliding_window: bool = False,
+    is_causal: bool = True,
+) -> torch.Tensor:
+    indices = torch.arange(seq_len, device=device)
+    diff = indices.unsqueeze(1) - indices.unsqueeze(0)
+    valid_mask = torch.ones((seq_len, seq_len), device=device, dtype=torch.bool)
+    if is_causal:
+        valid_mask = valid_mask & (diff >= 0)
+    if is_sliding_window and sliding_window is not None:
+        if is_causal:
+            valid_mask = valid_mask & (diff <= sliding_window)
+        else:
+            valid_mask = valid_mask & (torch.abs(diff) <= sliding_window)
+    valid_mask = valid_mask.unsqueeze(0).unsqueeze(0)
+    if attention_mask is not None:
+        padding_mask_4d = attention_mask.view(attention_mask.shape[0], 1, 1, seq_len).to(torch.bool)
+        valid_mask = valid_mask & padding_mask_4d
+    min_dtype = torch.finfo(dtype).min
+    mask_tensor = torch.full(valid_mask.shape, min_dtype, dtype=dtype, device=device)
+    mask_tensor.masked_fill_(valid_mask, 0.0)
+    return mask_tensor
+
+
+def pack_sequences(hidden1: torch.Tensor, hidden2: torch.Tensor, mask1: torch.Tensor, mask2: torch.Tensor):
+    hidden_cat = torch.cat([hidden1, hidden2], dim=1)
+    mask_cat = torch.cat([mask1, mask2], dim=1)
+    B, L, D = hidden_cat.shape
+    sort_idx = mask_cat.argsort(dim=1, descending=True, stable=True)
+    hidden_left = torch.gather(hidden_cat, 1, sort_idx.unsqueeze(-1).expand(B, L, D))
+    lengths = mask_cat.sum(dim=1)
+    new_mask = (torch.arange(L, dtype=torch.long, device=hidden_cat.device).unsqueeze(0) < lengths.unsqueeze(1))
+    return hidden_left, new_mask
+
+
+class Lambda(nn.Module):
+    def __init__(self, func):
+        super().__init__()
+        self.func = func
+
+    def forward(self, x):
+        return self.func(x)
+
+
+class AceStepAttention(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        num_key_value_heads: int,
+        rms_norm_eps: float,
+        attention_bias: bool,
+        attention_dropout: float,
+        layer_types: list,
+        head_dim: Optional[int] = None,
+        sliding_window: Optional[int] = None,
+        layer_idx: int = 0,
+        is_cross_attention: bool = False,
+        is_causal: bool = False,
+    ):
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.head_dim = head_dim or hidden_size // num_attention_heads
+        self.num_key_value_groups = num_attention_heads // num_key_value_heads
+        self.scaling = self.head_dim ** -0.5
+        self.attention_dropout = attention_dropout
+        if is_cross_attention:
+            is_causal = False
+        self.is_causal = is_causal
+        self.is_cross_attention = is_cross_attention
+
+        self.q_proj = nn.Linear(hidden_size, num_attention_heads * self.head_dim, bias=attention_bias)
+        self.k_proj = nn.Linear(hidden_size, num_key_value_heads * self.head_dim, bias=attention_bias)
+        self.v_proj = nn.Linear(hidden_size, num_key_value_heads * self.head_dim, bias=attention_bias)
+        self.o_proj = nn.Linear(num_attention_heads * self.head_dim, hidden_size, bias=attention_bias)
+        self.q_norm = Qwen3RMSNorm(self.head_dim, eps=rms_norm_eps)
+        self.k_norm = Qwen3RMSNorm(self.head_dim, eps=rms_norm_eps)
+        self.attention_type = layer_types[layer_idx]
+        self.sliding_window = sliding_window if layer_types[layer_idx] == "sliding_attention" else None
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor],
+        past_key_value: Optional[Cache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+
+        is_cross_attention = self.is_cross_attention and encoder_hidden_states is not None
+
+        if is_cross_attention:
+            encoder_hidden_shape = (*encoder_hidden_states.shape[:-1], -1, self.head_dim)
+            if past_key_value is not None:
+                is_updated = past_key_value.is_updated.get(self.layer_idx)
+                curr_past_key_value = past_key_value.cross_attention_cache
+                if not is_updated:
+                    key_states = self.k_norm(self.k_proj(encoder_hidden_states).view(encoder_hidden_shape)).transpose(1, 2)
+                    value_states = self.v_proj(encoder_hidden_states).view(encoder_hidden_shape).transpose(1, 2)
+                    key_states, value_states = curr_past_key_value.update(key_states, value_states, self.layer_idx)
+                    past_key_value.is_updated[self.layer_idx] = True
+                else:
+                    key_states = curr_past_key_value.layers[self.layer_idx].keys
+                    value_states = curr_past_key_value.layers[self.layer_idx].values
+            else:
+                key_states = self.k_norm(self.k_proj(encoder_hidden_states).view(encoder_hidden_shape)).transpose(1, 2)
+                value_states = self.v_proj(encoder_hidden_states).view(encoder_hidden_shape).transpose(1, 2)
+
+        else:
+            key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+            value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+            if position_embeddings is not None:
+                cos, sin = position_embeddings
+                query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+            if past_key_value is not None:
+                cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+                key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        if self.num_key_value_groups > 1:
+            key_states = key_states.unsqueeze(2).expand(-1, -1, self.num_key_value_groups, -1, -1).flatten(1, 2)
+            value_states = value_states.unsqueeze(2).expand(-1, -1, self.num_key_value_groups, -1, -1).flatten(1, 2)
+
+        attn_output = attention_forward(
+            query_states, key_states, value_states,
+            q_pattern="b n s d", k_pattern="b n s d", v_pattern="b n s d", out_pattern="b n s d",
+            attn_mask=attention_mask,
+        )
+        attn_weights = None
+
+        attn_output = attn_output.transpose(1, 2).flatten(2, 3).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class AceStepEncoderLayer(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        num_attention_heads: int,
+        num_key_value_heads: int,
+        rms_norm_eps: float,
+        attention_bias: bool,
+        attention_dropout: float,
+        layer_types: list,
+        head_dim: Optional[int] = None,
+        sliding_window: Optional[int] = None,
+        layer_idx: int = 0,
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.layer_idx = layer_idx
+
+        self.self_attn = AceStepAttention(
+            hidden_size=hidden_size,
+            num_attention_heads=num_attention_heads,
+            num_key_value_heads=num_key_value_heads,
+            rms_norm_eps=rms_norm_eps,
+            attention_bias=attention_bias,
+            attention_dropout=attention_dropout,
+            layer_types=layer_types,
+            head_dim=head_dim,
+            sliding_window=sliding_window,
+            layer_idx=layer_idx,
+            is_cross_attention=False,
+            is_causal=False,
+        )
+        self.input_layernorm = Qwen3RMSNorm(hidden_size, eps=rms_norm_eps)
+        self.post_attention_layernorm = Qwen3RMSNorm(hidden_size, eps=rms_norm_eps)
+
+        mlp_config = type('Config', (), {
+            'hidden_size': hidden_size,
+            'intermediate_size': intermediate_size,
+            'hidden_act': 'silu',
+        })()
+        self.mlp = Qwen3MLP(mlp_config)
+        self.attention_type = layer_types[layer_idx]
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = False,
+        **kwargs,
+    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states, self_attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            position_embeddings=position_embeddings,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            output_attentions=output_attentions,
+            use_cache=False,
+            past_key_value=None,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+        if output_attentions:
+            outputs += (self_attn_weights,)
+        return outputs
+
+
+class AceStepLyricEncoder(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int = 2048,
+        intermediate_size: int = 6144,
+        num_hidden_layers: int = 24,
+        num_attention_heads: int = 16,
+        num_key_value_heads: int = 8,
+        rms_norm_eps: float = 1e-6,
+        attention_bias: bool = False,
+        attention_dropout: float = 0.0,
+        layer_types: Optional[list] = None,
+        head_dim: Optional[int] = None,
+        sliding_window: Optional[int] = 128,
+        use_sliding_window: bool = True,
+        use_cache: bool = True,
+        rope_theta: float = 1000000,
+        max_position_embeddings: int = 32768,
+        initializer_range: float = 0.02,
+        text_hidden_dim: int = 1024,
+        num_lyric_encoder_hidden_layers: int = 8,
+        **kwargs,
+    ):
+        super().__init__()
+        self.num_lyric_encoder_hidden_layers = num_lyric_encoder_hidden_layers
+        self.text_hidden_dim = text_hidden_dim
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.rms_norm_eps = rms_norm_eps
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.layer_types = layer_types or (["sliding_attention", "full_attention"] * (num_hidden_layers // 2))
+        self.head_dim = head_dim or hidden_size // num_attention_heads
+        self.sliding_window = sliding_window
+        self.use_sliding_window = use_sliding_window
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self._attn_implementation = kwargs.get("_attn_implementation", "sdpa")
+
+        self.embed_tokens = nn.Linear(text_hidden_dim, hidden_size)
+        self.norm = Qwen3RMSNorm(hidden_size, eps=rms_norm_eps)
+        rope_config = type('RopeConfig', (), {
+            'hidden_size': hidden_size,
+            'num_attention_heads': num_attention_heads,
+            'num_key_value_heads': num_key_value_heads,
+            'head_dim': head_dim,
+            'max_position_embeddings': max_position_embeddings,
+            'rope_theta': rope_theta,
+            'rope_parameters': {'rope_type': 'default', 'rope_theta': rope_theta},
+            'rms_norm_eps': rms_norm_eps,
+            'attention_bias': attention_bias,
+            'attention_dropout': attention_dropout,
+            'hidden_act': 'silu',
+            'intermediate_size': intermediate_size,
+            'layer_types': self.layer_types,
+            'sliding_window': sliding_window,
+            '_attn_implementation': self._attn_implementation,
+        })()
+        self.rotary_emb = Qwen3RotaryEmbedding(rope_config)
+        self.gradient_checkpointing = False
+
+        self.layers = nn.ModuleList([
+            AceStepEncoderLayer(
+                hidden_size=hidden_size,
+                intermediate_size=intermediate_size,
+                num_attention_heads=num_attention_heads,
+                num_key_value_heads=num_key_value_heads,
+                rms_norm_eps=rms_norm_eps,
+                attention_bias=attention_bias,
+                attention_dropout=attention_dropout,
+                layer_types=self.layer_types,
+                head_dim=head_dim,
+                sliding_window=sliding_window,
+                layer_idx=layer_idx,
+            )
+            for layer_idx in range(num_lyric_encoder_hidden_layers)
+        ])
+
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
+    ) -> BaseModelOutput:
+        output_attentions = output_attentions if output_attentions is not None else False
+        output_hidden_states = output_hidden_states if output_hidden_states is not None else False
+
+        assert input_ids is None, "Only `inputs_embeds` is supported for the lyric encoder."
+        assert attention_mask is not None, "Attention mask must be provided for the lyric encoder."
+        assert inputs_embeds is not None, "Inputs embeddings must be provided for the lyric encoder."
+
+        inputs_embeds = self.embed_tokens(inputs_embeds)
+        cache_position = torch.arange(0, inputs_embeds.shape[1], device=inputs_embeds.device)
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        seq_len = inputs_embeds.shape[1]
+        dtype = inputs_embeds.dtype
+        device = inputs_embeds.device
+
+        full_attn_mask = create_4d_mask(
+            seq_len=seq_len, dtype=dtype, device=device,
+            attention_mask=attention_mask, sliding_window=None,
+            is_sliding_window=False, is_causal=False
+        )
+        sliding_attn_mask = None
+        if self.use_sliding_window:
+            sliding_attn_mask = create_4d_mask(
+                seq_len=seq_len, dtype=dtype, device=device,
+                attention_mask=attention_mask, sliding_window=self.sliding_window,
+                is_sliding_window=True, is_causal=False
+            )
+
+        self_attn_mask_mapping = {
+            "full_attention": full_attn_mask,
+            "sliding_attention": sliding_attn_mask,
+        }
+
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for layer_module in self.layers[: self.num_lyric_encoder_hidden_layers]:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            layer_outputs = layer_module(
+                hidden_states, position_embeddings,
+                self_attn_mask_mapping[layer_module.attention_type],
+                position_ids, output_attentions,
+                **flash_attn_kwargs,
+            )
+            hidden_states = layer_outputs[0]
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        return BaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class AceStepTimbreEncoder(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int = 2048,
+        intermediate_size: int = 6144,
+        num_hidden_layers: int = 24,
+        num_attention_heads: int = 16,
+        num_key_value_heads: int = 8,
+        rms_norm_eps: float = 1e-6,
+        attention_bias: bool = False,
+        attention_dropout: float = 0.0,
+        layer_types: Optional[list] = None,
+        head_dim: Optional[int] = None,
+        sliding_window: Optional[int] = 128,
+        use_sliding_window: bool = True,
+        use_cache: bool = True,
+        rope_theta: float = 1000000,
+        max_position_embeddings: int = 32768,
+        initializer_range: float = 0.02,
+        timbre_hidden_dim: int = 64,
+        num_timbre_encoder_hidden_layers: int = 4,
+        **kwargs,
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.rms_norm_eps = rms_norm_eps
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.layer_types = layer_types or (["sliding_attention", "full_attention"] * (num_hidden_layers // 2))
+        self.head_dim = head_dim or hidden_size // num_attention_heads
+        self.sliding_window = sliding_window
+        self.use_sliding_window = use_sliding_window
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.timbre_hidden_dim = timbre_hidden_dim
+        self.num_timbre_encoder_hidden_layers = num_timbre_encoder_hidden_layers
+        self._attn_implementation = kwargs.get("_attn_implementation", "sdpa")
+
+        self.embed_tokens = nn.Linear(timbre_hidden_dim, hidden_size)
+        self.norm = Qwen3RMSNorm(hidden_size, eps=rms_norm_eps)
+        rope_config = type('RopeConfig', (), {
+            'hidden_size': hidden_size,
+            'num_attention_heads': num_attention_heads,
+            'num_key_value_heads': num_key_value_heads,
+            'head_dim': head_dim,
+            'max_position_embeddings': max_position_embeddings,
+            'rope_theta': rope_theta,
+            'rope_parameters': {'rope_type': 'default', 'rope_theta': rope_theta},
+            'rms_norm_eps': rms_norm_eps,
+            'attention_bias': attention_bias,
+            'attention_dropout': attention_dropout,
+            'hidden_act': 'silu',
+            'intermediate_size': intermediate_size,
+            'layer_types': self.layer_types,
+            'sliding_window': sliding_window,
+            '_attn_implementation': self._attn_implementation,
+        })()
+        self.rotary_emb = Qwen3RotaryEmbedding(rope_config)
+        self.gradient_checkpointing = False
+        self.special_token = nn.Parameter(torch.randn(1, 1, hidden_size))
+        self.layers = nn.ModuleList([
+            AceStepEncoderLayer(
+                hidden_size=hidden_size,
+                intermediate_size=intermediate_size,
+                num_attention_heads=num_attention_heads,
+                num_key_value_heads=num_key_value_heads,
+                rms_norm_eps=rms_norm_eps,
+                attention_bias=attention_bias,
+                attention_dropout=attention_dropout,
+                layer_types=self.layer_types,
+                head_dim=head_dim,
+                sliding_window=sliding_window,
+                layer_idx=layer_idx,
+            )
+            for layer_idx in range(num_timbre_encoder_hidden_layers)
+        ])
+
+
+    def unpack_timbre_embeddings(self, timbre_embs_packed, refer_audio_order_mask):
+        N, d = timbre_embs_packed.shape
+        device = timbre_embs_packed.device
+        dtype = timbre_embs_packed.dtype
+        B = int(refer_audio_order_mask.max().item() + 1)
+        counts = torch.bincount(refer_audio_order_mask, minlength=B)
+        max_count = counts.max().item()
+        sorted_indices = torch.argsort(refer_audio_order_mask * N + torch.arange(N, device=device), stable=True)
+        sorted_batch_ids = refer_audio_order_mask[sorted_indices]
+        positions = torch.arange(N, device=device)
+        batch_starts = torch.cat([torch.tensor([0], device=device), torch.cumsum(counts, dim=0)[:-1]])
+        positions_in_sorted = positions - batch_starts[sorted_batch_ids]
+        inverse_indices = torch.empty_like(sorted_indices)
+        inverse_indices[sorted_indices] = torch.arange(N, device=device)
+        positions_in_batch = positions_in_sorted[inverse_indices]
+        indices_2d = refer_audio_order_mask * max_count + positions_in_batch
+        one_hot = F.one_hot(indices_2d, num_classes=B * max_count).to(dtype)
+        timbre_embs_flat = one_hot.t() @ timbre_embs_packed
+        timbre_embs_unpack = timbre_embs_flat.reshape(B, max_count, d)
+        mask_flat = (one_hot.sum(dim=0) > 0).long()
+        new_mask = mask_flat.reshape(B, max_count)
+        return timbre_embs_unpack, new_mask
+
+    @can_return_tuple
+    def forward(
+        self,
+        refer_audio_acoustic_hidden_states_packed: Optional[torch.FloatTensor] = None,
+        refer_audio_order_mask: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
+    ) -> BaseModelOutput:
+        inputs_embeds = refer_audio_acoustic_hidden_states_packed
+        inputs_embeds = self.embed_tokens(inputs_embeds)
+        # Handle 2D (packed) or 3D (batched) input
+        is_packed = inputs_embeds.dim() == 2
+        if is_packed:
+            seq_len = inputs_embeds.shape[0]
+            cache_position = torch.arange(0, seq_len, device=inputs_embeds.device)
+            position_ids = cache_position.unsqueeze(0)
+            inputs_embeds = inputs_embeds.unsqueeze(0)
+        else:
+            seq_len = inputs_embeds.shape[1]
+            cache_position = torch.arange(0, seq_len, device=inputs_embeds.device)
+            position_ids = cache_position.unsqueeze(0)
+
+        dtype = inputs_embeds.dtype
+        device = inputs_embeds.device
+
+        full_attn_mask = create_4d_mask(
+            seq_len=seq_len, dtype=dtype, device=device,
+            attention_mask=attention_mask, sliding_window=None,
+            is_sliding_window=False, is_causal=False
+        )
+        sliding_attn_mask = None
+        if self.use_sliding_window:
+            sliding_attn_mask = create_4d_mask(
+                seq_len=seq_len, dtype=dtype, device=device,
+                attention_mask=attention_mask, sliding_window=self.sliding_window,
+                is_sliding_window=True, is_causal=False
+            )
+
+        self_attn_mask_mapping = {
+            "full_attention": full_attn_mask,
+            "sliding_attention": sliding_attn_mask,
+        }
+
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        for layer_module in self.layers[: self.num_timbre_encoder_hidden_layers]:
+            layer_outputs = layer_module(
+                hidden_states, position_embeddings,
+                self_attn_mask_mapping[layer_module.attention_type],
+                position_ids,
+                **flash_attn_kwargs,
+            )
+            hidden_states = layer_outputs[0]
+
+        hidden_states = self.norm(hidden_states)
+        # For packed input: reshape [1, T, D] -> [T, D] for unpacking
+        if is_packed:
+            hidden_states = hidden_states.squeeze(0)
+        timbre_embs_unpack, timbre_embs_mask = self.unpack_timbre_embeddings(hidden_states, refer_audio_order_mask)
+        return timbre_embs_unpack, timbre_embs_mask
+
+
+class AceStepConditionEncoder(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int = 2048,
+        intermediate_size: int = 6144,
+        num_hidden_layers: int = 24,
+        num_attention_heads: int = 16,
+        num_key_value_heads: int = 8,
+        rms_norm_eps: float = 1e-6,
+        attention_bias: bool = False,
+        attention_dropout: float = 0.0,
+        layer_types: Optional[list] = None,
+        head_dim: Optional[int] = None,
+        sliding_window: Optional[int] = 128,
+        use_sliding_window: bool = True,
+        use_cache: bool = True,
+        rope_theta: float = 1000000,
+        max_position_embeddings: int = 32768,
+        initializer_range: float = 0.02,
+        text_hidden_dim: int = 1024,
+        timbre_hidden_dim: int = 64,
+        num_lyric_encoder_hidden_layers: int = 8,
+        num_timbre_encoder_hidden_layers: int = 4,
+        **kwargs,
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.rms_norm_eps = rms_norm_eps
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.layer_types = layer_types or (["sliding_attention", "full_attention"] * (num_hidden_layers // 2))
+        self.head_dim = head_dim or hidden_size // num_attention_heads
+        self.sliding_window = sliding_window
+        self.use_sliding_window = use_sliding_window
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.text_hidden_dim = text_hidden_dim
+        self.timbre_hidden_dim = timbre_hidden_dim
+        self.num_lyric_encoder_hidden_layers = num_lyric_encoder_hidden_layers
+        self.num_timbre_encoder_hidden_layers = num_timbre_encoder_hidden_layers
+        self._attn_implementation = kwargs.get("_attn_implementation", "sdpa")
+
+        self.text_projector = nn.Linear(text_hidden_dim, hidden_size, bias=False)
+        self.null_condition_emb = nn.Parameter(torch.randn(1, 1, hidden_size))
+        self.lyric_encoder = AceStepLyricEncoder(
+            hidden_size=hidden_size,
+            intermediate_size=intermediate_size,
+            num_attention_heads=num_attention_heads,
+            num_key_value_heads=num_key_value_heads,
+            rms_norm_eps=rms_norm_eps,
+            attention_bias=attention_bias,
+            attention_dropout=attention_dropout,
+            layer_types=layer_types,
+            head_dim=head_dim,
+            sliding_window=sliding_window,
+            use_sliding_window=use_sliding_window,
+            rope_theta=rope_theta,
+            max_position_embeddings=max_position_embeddings,
+            initializer_range=initializer_range,
+            text_hidden_dim=text_hidden_dim,
+            num_lyric_encoder_hidden_layers=num_lyric_encoder_hidden_layers,
+        )
+        self.timbre_encoder = AceStepTimbreEncoder(
+            hidden_size=hidden_size,
+            intermediate_size=intermediate_size,
+            num_attention_heads=num_attention_heads,
+            num_key_value_heads=num_key_value_heads,
+            rms_norm_eps=rms_norm_eps,
+            attention_bias=attention_bias,
+            attention_dropout=attention_dropout,
+            layer_types=layer_types,
+            head_dim=head_dim,
+            sliding_window=sliding_window,
+            use_sliding_window=use_sliding_window,
+            rope_theta=rope_theta,
+            max_position_embeddings=max_position_embeddings,
+            initializer_range=initializer_range,
+            timbre_hidden_dim=timbre_hidden_dim,
+            num_timbre_encoder_hidden_layers=num_timbre_encoder_hidden_layers,
+        )
+
+    def forward(
+        self,
+        text_hidden_states: Optional[torch.FloatTensor] = None,
+        text_attention_mask: Optional[torch.Tensor] = None,
+        lyric_hidden_states: Optional[torch.LongTensor] = None,
+        lyric_attention_mask: Optional[torch.Tensor] = None,
+        refer_audio_acoustic_hidden_states_packed: Optional[torch.Tensor] = None,
+        refer_audio_order_mask: Optional[torch.LongTensor] = None,
+    ):
+        text_hidden_states = self.text_projector(text_hidden_states)
+        lyric_encoder_outputs = self.lyric_encoder(
+            inputs_embeds=lyric_hidden_states,
+            attention_mask=lyric_attention_mask,
+        )
+        lyric_hidden_states = lyric_encoder_outputs.last_hidden_state
+        timbre_embs_unpack, timbre_embs_mask = self.timbre_encoder(
+            refer_audio_acoustic_hidden_states_packed,
+            refer_audio_order_mask
+        )
+
+        encoder_hidden_states, encoder_attention_mask = pack_sequences(
+            lyric_hidden_states, timbre_embs_unpack, lyric_attention_mask, timbre_embs_mask
+        )
+        encoder_hidden_states, encoder_attention_mask = pack_sequences(
+            encoder_hidden_states, text_hidden_states, encoder_attention_mask, text_attention_mask
+        )
+        return encoder_hidden_states, encoder_attention_mask