model-code

diffsynth/models/ace_step_dit.py

@@ -0,0 +1,908 @@
+# 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 ..core.attention.attention import attention_forward
+from ..core import gradient_checkpoint_forward
+
+from transformers.cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
+from transformers.modeling_outputs import BaseModelOutput
+from transformers.processing_utils import Unpack
+from transformers.utils import 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,  # [Batch, Seq_Len]
+    sliding_window: Optional[int] = None,
+    is_sliding_window: bool = False,
+    is_causal: bool = True,
+) -> torch.Tensor:
+    """
+    General 4D Attention Mask generator compatible with CPU/Mac/SDPA and Eager mode.
+    Supports use cases:
+    1. Causal Full: is_causal=True, is_sliding_window=False (standard GPT)
+    2. Causal Sliding: is_causal=True, is_sliding_window=True (Mistral/Qwen local window)
+    3. Bidirectional Full: is_causal=False, is_sliding_window=False (BERT/Encoder)
+    4. Bidirectional Sliding: is_causal=False, is_sliding_window=True (Longformer local)
+
+    Returns:
+        [Batch, 1, Seq_Len, Seq_Len] additive mask (0.0 for keep, -inf for mask)
+    """
+    # ------------------------------------------------------
+    # 1. Construct basic geometry mask [Seq_Len, Seq_Len]
+    # ------------------------------------------------------
+
+    # Build index matrices
+    # i (Query): [0, 1, ..., L-1]
+    # j (Key):   [0, 1, ..., L-1]
+    indices = torch.arange(seq_len, device=device)
+    # diff = i - j
+    diff = indices.unsqueeze(1) - indices.unsqueeze(0)
+
+    # Initialize all True (all positions visible)
+    valid_mask = torch.ones((seq_len, seq_len), device=device, dtype=torch.bool)
+
+    # (A) Handle causality (Causal)
+    if is_causal:
+        # i >= j  =>  diff >= 0
+        valid_mask = valid_mask & (diff >= 0)
+
+    # (B) Handle sliding window
+    if is_sliding_window and sliding_window is not None:
+        if is_causal:
+            # Causal sliding: only attend to past window steps
+            # i - j <= window  =>  diff <= window
+            # (diff >= 0 already handled above)
+            valid_mask = valid_mask & (diff <= sliding_window)
+        else:
+            # Bidirectional sliding: attend past and future window steps
+            # |i - j| <= window  =>  abs(diff) <= sliding_window
+            valid_mask = valid_mask & (torch.abs(diff) <= sliding_window)
+
+    # Expand dimensions to [1, 1, Seq_Len, Seq_Len] for broadcasting
+    valid_mask = valid_mask.unsqueeze(0).unsqueeze(0)
+
+    # ------------------------------------------------------
+    # 2. Apply padding mask (Key Masking)
+    # ------------------------------------------------------
+    if attention_mask is not None:
+        # attention_mask shape: [Batch, Seq_Len] (1=valid, 0=padding)
+        # We want to mask out invalid keys (columns)
+        # Expand shape: [Batch, 1, 1, Seq_Len]
+        padding_mask_4d = attention_mask.view(attention_mask.shape[0], 1, 1, seq_len).to(torch.bool)
+
+        # Broadcasting: Geometry Mask [1, 1, L, L] & Padding Mask [B, 1, 1, L]
+        # Result shape: [B, 1, L, L]
+        valid_mask = valid_mask & padding_mask_4d
+
+    # ------------------------------------------------------
+    # 3. Convert to additive mask
+    # ------------------------------------------------------
+    # Get the minimal value for current dtype
+    min_dtype = torch.finfo(dtype).min
+
+    # Create result tensor filled with -inf by default
+    mask_tensor = torch.full(valid_mask.shape, min_dtype, dtype=dtype, device=device)
+
+    # Set valid positions to 0.0
+    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):
+    """
+    Pack two sequences by concatenating and sorting them based on mask values.
+
+    Args:
+        hidden1: First hidden states tensor of shape [B, L1, D]
+        hidden2: Second hidden states tensor of shape [B, L2, D]
+        mask1: First mask tensor of shape [B, L1]
+        mask2: Second mask tensor of shape [B, L2]
+
+    Returns:
+        Tuple of (packed_hidden_states, new_mask) where:
+        - packed_hidden_states: Packed hidden states with valid tokens (mask=1) first, shape [B, L1+L2, D]
+        - new_mask: New mask tensor indicating valid positions, shape [B, L1+L2]
+    """
+    # Step 1: Concatenate hidden states and masks along sequence dimension
+    hidden_cat = torch.cat([hidden1, hidden2], dim=1)  # [B, L, D]
+    mask_cat = torch.cat([mask1, mask2], dim=1)  # [B, L]
+
+    B, L, D = hidden_cat.shape
+
+    # Step 2: Sort indices so that mask values of 1 come before 0
+    sort_idx = mask_cat.argsort(dim=1, descending=True, stable=True)  # [B, L]
+
+    # Step 3: Reorder hidden states using sorted indices
+    hidden_left = torch.gather(hidden_cat, 1, sort_idx.unsqueeze(-1).expand(B, L, D))
+
+    # Step 4: Create new mask based on valid sequence lengths
+    lengths = mask_cat.sum(dim=1)  # [B]
+    new_mask = (torch.arange(L, dtype=torch.long, device=hidden_cat.device).unsqueeze(0) < lengths.unsqueeze(1))
+
+    return hidden_left, new_mask
+
+
+class TimestepEmbedding(nn.Module):
+    """
+    Timestep embedding module for diffusion models.
+
+    Converts timestep values into high-dimensional embeddings using sinusoidal
+    positional encoding, followed by MLP layers. Used for conditioning diffusion
+    models on timestep information.
+    """
+    def __init__(
+        self,
+        in_channels: int,
+        time_embed_dim: int,
+        scale: float = 1000,
+    ):
+        super().__init__()
+
+        self.linear_1 = nn.Linear(in_channels, time_embed_dim, bias=True)
+        self.act1 = nn.SiLU()
+        self.linear_2 = nn.Linear(time_embed_dim, time_embed_dim, bias=True)
+        self.in_channels = in_channels
+
+        self.act2 = nn.SiLU()
+        self.time_proj = nn.Linear(time_embed_dim, time_embed_dim * 6)
+        self.scale = scale
+
+    def timestep_embedding(self, t, dim, max_period=10000):
+        """
+        Create sinusoidal timestep embeddings.
+
+        Args:
+            t: A 1-D tensor of N indices, one per batch element. These may be fractional.
+            dim: The dimension of the output embeddings.
+            max_period: Controls the minimum frequency of the embeddings.
+
+        Returns:
+            An (N, D) tensor of positional embeddings.
+        """
+        t = t * self.scale
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+        ).to(device=t.device)
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+        return embedding
+
+    def forward(self, t):
+        t_freq = self.timestep_embedding(t, self.in_channels)
+        temb = self.linear_1(t_freq.to(t.dtype))
+        temb = self.act1(temb)
+        temb = self.linear_2(temb)
+        timestep_proj = self.time_proj(self.act2(temb)).unflatten(1, (6, -1))
+        return temb, timestep_proj
+
+
+class AceStepAttention(nn.Module):
+    """
+    Multi-headed attention module for AceStep model.
+
+    Implements the attention mechanism from 'Attention Is All You Need' paper,
+    with support for both self-attention and cross-attention modes. Uses RMSNorm
+    for query and key normalization, and supports sliding window attention for
+    efficient long-sequence processing.
+    """
+
+    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)
+
+        # Project and normalize query states
+        query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+
+        # Determine if this is cross-attention (requires encoder_hidden_states)
+        is_cross_attention = self.is_cross_attention and encoder_hidden_states is not None
+
+        # Cross-attention path: attend to encoder hidden states
+        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)
+                # After the first generated token, we can reuse all key/value states from cache
+                curr_past_key_value = past_key_value.cross_attention_cache
+
+                # Conditions for calculating key and value states
+                if not is_updated:
+                    # Compute and cache K/V for the first time
+                    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)
+                    # Update cache: save all key/value states to cache for fast auto-regressive generation
+                    key_states, value_states = curr_past_key_value.update(key_states, value_states, self.layer_idx)
+                    # Set flag that this layer's cross-attention cache is updated
+                    past_key_value.is_updated[self.layer_idx] = True
+                else:
+                    # Reuse cached key/value states for subsequent tokens
+                    key_states = curr_past_key_value.layers[self.layer_idx].keys
+                    value_states = curr_past_key_value.layers[self.layer_idx].values
+            else:
+                # No cache used, compute K/V directly
+                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)
+
+        # Self-attention path: attend to the same sequence
+        else:
+            # Project and normalize key/value states for self-attention
+            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)
+            # Apply rotary position embeddings (RoPE) if provided
+            if position_embeddings is not None:
+                cos, sin = position_embeddings
+                query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+            # Update cache for auto-regressive generation
+            if past_key_value is not None:
+                # Sin and cos are specific to RoPE models; cache_position needed for the static cache
+                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)
+
+        # GGA expansion: if num_key_value_heads < num_attention_heads
+        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)
+
+        # Use DiffSynth unified attention
+        # Tensors are already in (batch, heads, seq, dim) format -> "b n s d"
+        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  # attention_forward doesn't return weights
+
+        # Flatten and project output: (B, n_heads, seq, dim) -> (B, seq, n_heads*dim)
+        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):
+    """
+    Encoder layer for AceStep model.
+
+    Consists of self-attention and MLP (feed-forward) sub-layers with residual connections.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        num_key_value_heads: int,
+        intermediate_size: int = 6144,
+        rms_norm_eps: float = 1e-6,
+        attention_bias: bool = False,
+        attention_dropout: float = 0.0,
+        layer_types: list = None,
+        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 (feed-forward) sub-layer
+        self.mlp = Qwen3MLP(
+            config=type('Config', (), {
+                'hidden_size': hidden_size,
+                'intermediate_size': intermediate_size,
+                'hidden_act': 'silu',
+            })()
+        )
+        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]],
+    ]:
+        # Self-attention with residual connection
+        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,
+            # Encoders don't use cache
+            use_cache=False,
+            past_key_value=None,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # MLP with residual connection
+        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 AceStepDiTLayer(nn.Module):
+    """
+    DiT (Diffusion Transformer) layer for AceStep model.
+
+    Implements a transformer layer with three main components:
+    1. Self-attention with adaptive layer norm (AdaLN)
+    2. Cross-attention (optional) for conditioning on encoder outputs
+    3. Feed-forward MLP with adaptive layer norm
+
+    Uses scale-shift modulation from timestep embeddings for adaptive normalization.
+    """
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        num_key_value_heads: int,
+        intermediate_size: 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,
+        use_cross_attention: bool = True,
+    ):
+        super().__init__()
+
+        self.self_attn_norm = Qwen3RMSNorm(hidden_size, eps=rms_norm_eps)
+        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,
+        )
+
+        self.use_cross_attention = use_cross_attention
+        if self.use_cross_attention:
+            self.cross_attn_norm = Qwen3RMSNorm(hidden_size, eps=rms_norm_eps)
+            self.cross_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=True,
+            )
+
+        self.mlp_norm = Qwen3RMSNorm(hidden_size, eps=rms_norm_eps)
+        self.mlp = Qwen3MLP(
+            config=type('Config', (), {
+                'hidden_size': hidden_size,
+                'intermediate_size': intermediate_size,
+                'hidden_act': 'silu',
+            })()
+        )
+
+        self.scale_shift_table = nn.Parameter(torch.randn(1, 6, hidden_size) / hidden_size**0.5)
+        self.attention_type = layer_types[layer_idx]
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        temb: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[EncoderDecoderCache] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        cache_position: Optional[torch.LongTensor] = None,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+
+        # Extract scale-shift parameters for adaptive layer norm from timestep embeddings
+        # 6 values: (shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa)
+        shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
+            self.scale_shift_table + temb
+        ).chunk(6, dim=1)
+
+        # Step 1: Self-attention with adaptive layer norm (AdaLN)
+        # Apply adaptive normalization: norm(x) * (1 + scale) + shift
+        norm_hidden_states = (self.self_attn_norm(hidden_states) * (1 + scale_msa) + shift_msa).type_as(hidden_states)
+        attn_output, self_attn_weights = self.self_attn(
+            hidden_states=norm_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,
+        )
+        # Apply gated residual connection: x = x + attn_output * gate
+        hidden_states = (hidden_states + attn_output * gate_msa).type_as(hidden_states)
+
+        # Step 2: Cross-attention (if enabled) for conditioning on encoder outputs
+        if self.use_cross_attention:
+            norm_hidden_states = self.cross_attn_norm(hidden_states).type_as(hidden_states)
+            attn_output, cross_attn_weights = self.cross_attn(
+                hidden_states=norm_hidden_states,
+                encoder_hidden_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                **kwargs,
+            )
+            # Standard residual connection for cross-attention
+            hidden_states = hidden_states + attn_output
+
+        # Step 3: Feed-forward (MLP) with adaptive layer norm
+        # Apply adaptive normalization for MLP: norm(x) * (1 + scale) + shift
+        norm_hidden_states = (self.mlp_norm(hidden_states) * (1 + c_scale_msa) + c_shift_msa).type_as(hidden_states)
+        ff_output = self.mlp(norm_hidden_states)
+        # Apply gated residual connection: x = x + mlp_output * gate
+        hidden_states = (hidden_states + ff_output * c_gate_msa).type_as(hidden_states)
+
+        outputs = (hidden_states,)
+        if output_attentions:
+            outputs += (self_attn_weights, cross_attn_weights)
+
+        return outputs
+
+
+
+class Lambda(nn.Module):
+    """
+    Wrapper module for arbitrary lambda functions.
+
+    Allows using lambda functions in nn.Sequential by wrapping them in a Module.
+    Useful for simple transformations like transpose operations.
+    """
+    def __init__(self, func):
+        super().__init__()
+        self.func = func
+
+    def forward(self, x):
+        return self.func(x)
+
+
+class AceStepDiTModel(nn.Module):
+    """
+    DiT (Diffusion Transformer) model for AceStep.
+
+    Main diffusion model that generates audio latents conditioned on text, lyrics,
+    and timbre. Uses patch-based processing with transformer layers, timestep
+    conditioning, and cross-attention to encoder outputs.
+    """
+    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,
+        patch_size: int = 2,
+        in_channels: int = 192,
+        audio_acoustic_hidden_dim: int = 64,
+        encoder_hidden_size: Optional[int] = None,
+        **kwargs,
+    ):
+        super().__init__()
+
+        self.layer_types = layer_types or (["sliding_attention", "full_attention"] * (num_hidden_layers // 2))
+        self.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window
+        self.use_cache = use_cache
+        encoder_hidden_size = encoder_hidden_size or hidden_size
+
+        # Rotary position embeddings for transformer layers
+        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,
+        })()
+        self.rotary_emb = Qwen3RotaryEmbedding(rope_config)
+
+        # Stack of DiT transformer layers
+        self.layers = nn.ModuleList([
+            AceStepDiTLayer(
+                hidden_size=hidden_size,
+                num_attention_heads=num_attention_heads,
+                num_key_value_heads=num_key_value_heads,
+                intermediate_size=intermediate_size,
+                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_hidden_layers)
+        ])
+
+        self.patch_size = patch_size
+
+        # Input projection: patch embedding using 1D convolution
+        self.proj_in = nn.Sequential(
+            Lambda(lambda x: x.transpose(1, 2)),
+            nn.Conv1d(
+                in_channels=in_channels,
+                out_channels=hidden_size,
+                kernel_size=patch_size,
+                stride=patch_size,
+                padding=0,
+            ),
+            Lambda(lambda x: x.transpose(1, 2)),
+        )
+
+        # Timestep embeddings for diffusion conditioning
+        self.time_embed = TimestepEmbedding(in_channels=256, time_embed_dim=hidden_size)
+        self.time_embed_r = TimestepEmbedding(in_channels=256, time_embed_dim=hidden_size)
+
+        # Project encoder hidden states to model dimension
+        self.condition_embedder = nn.Linear(encoder_hidden_size, hidden_size, bias=True)
+
+        # Output normalization and projection
+        self.norm_out = Qwen3RMSNorm(hidden_size, eps=rms_norm_eps)
+        self.proj_out = nn.Sequential(
+            Lambda(lambda x: x.transpose(1, 2)),
+            nn.ConvTranspose1d(
+                in_channels=hidden_size,
+                out_channels=audio_acoustic_hidden_dim,
+                kernel_size=patch_size,
+                stride=patch_size,
+                padding=0,
+            ),
+            Lambda(lambda x: x.transpose(1, 2)),
+        )
+        self.scale_shift_table = nn.Parameter(torch.randn(1, 2, hidden_size) / hidden_size**0.5)
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        timestep: torch.Tensor,
+        timestep_r: torch.Tensor,
+        attention_mask: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        encoder_attention_mask: torch.Tensor,
+        context_latents: torch.Tensor,
+        use_cache: Optional[bool] = None,
+        past_key_values: Optional[EncoderDecoderCache] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = False,
+        return_hidden_states: int = None,
+        custom_layers_config: Optional[dict] = None,
+        enable_early_exit: bool = False,
+        use_gradient_checkpointing: bool = False,
+        use_gradient_checkpointing_offload: bool = False,
+        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
+    ):
+
+        use_cache = use_cache if use_cache is not None else self.use_cache
+
+        # Disable cache during training or when gradient checkpointing is enabled
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+        if self.training:
+            use_cache = False
+
+        # Initialize cache if needed (only during inference for auto-regressive generation)
+        if not self.training and use_cache and past_key_values is None:
+            past_key_values = EncoderDecoderCache(DynamicCache(), DynamicCache())
+
+        # Compute timestep embeddings for diffusion conditioning
+        # Two embeddings: one for timestep t, one for timestep difference (t - r)
+        temb_t, timestep_proj_t = self.time_embed(timestep)
+        temb_r, timestep_proj_r = self.time_embed_r(timestep - timestep_r)
+        # Combine embeddings
+        temb = temb_t + temb_r
+        timestep_proj = timestep_proj_t + timestep_proj_r
+
+        # Concatenate context latents (source latents + chunk masks) with hidden states
+        hidden_states = torch.cat([context_latents, hidden_states], dim=-1)
+        # Record original sequence length for later restoration after padding
+        original_seq_len = hidden_states.shape[1]
+        # Apply padding if sequence length is not divisible by patch_size
+        # This ensures proper patch extraction
+        pad_length = 0
+        if hidden_states.shape[1] % self.patch_size != 0:
+            pad_length = self.patch_size - (hidden_states.shape[1] % self.patch_size)
+            hidden_states = F.pad(hidden_states, (0, 0, 0, pad_length), mode='constant', value=0)
+
+        # Project input to patches and project encoder states
+        hidden_states = self.proj_in(hidden_states)
+        encoder_hidden_states = self.condition_embedder(encoder_hidden_states)
+
+        # Cache positions
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + hidden_states.shape[1], device=hidden_states.device
+            )
+
+        # Position IDs
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        seq_len = hidden_states.shape[1]
+        encoder_seq_len = encoder_hidden_states.shape[1]
+        dtype = hidden_states.dtype
+        device = hidden_states.device
+
+        # Initialize Mask variables
+        full_attn_mask = None
+        sliding_attn_mask = None
+        encoder_attn_mask = None
+        decoder_attn_mask = None
+        # Target library discards the passed-in attention_mask for 4D mask
+        # construction (line 1384: attention_mask = None)
+        attention_mask = None
+
+        # 1. Full Attention (Bidirectional, Global)
+        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
+        )
+        max_len = max(seq_len, encoder_seq_len)
+
+        encoder_attn_mask = create_4d_mask(
+            seq_len=max_len,
+            dtype=dtype,
+            device=device,
+            attention_mask=attention_mask,
+            sliding_window=None,
+            is_sliding_window=False,
+            is_causal=False
+        )
+        encoder_attn_mask = encoder_attn_mask[:, :, :seq_len, :encoder_seq_len]
+
+        # 2. Sliding Attention (Bidirectional, Local)
+        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
+            )
+
+        # Build mask mapping
+        self_attn_mask_mapping = {
+            "full_attention": full_attn_mask,
+            "sliding_attention": sliding_attn_mask,
+            "encoder_attention_mask": encoder_attn_mask,
+        }
+
+        # Create position embeddings to be shared across all decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+        all_cross_attentions = () if output_attentions else None
+
+        # Handle early exit for custom layer configurations
+        max_needed_layer = float('inf')
+        if custom_layers_config is not None and enable_early_exit:
+            max_needed_layer = max(custom_layers_config.keys())
+            output_attentions = True
+            if all_cross_attentions is None:
+                all_cross_attentions = ()
+
+        # Process through transformer layers
+        for index_block, layer_module in enumerate(self.layers):
+            # Early exit optimization
+            if index_block > max_needed_layer:
+                break
+
+            # Prepare layer arguments
+            layer_args = (
+                hidden_states,
+                position_embeddings,
+                timestep_proj,
+                self_attn_mask_mapping[layer_module.attention_type],
+                position_ids,
+                past_key_values,
+                output_attentions,
+                use_cache,
+                cache_position,
+                encoder_hidden_states,
+                self_attn_mask_mapping["encoder_attention_mask"],
+            )
+            layer_kwargs = flash_attn_kwargs
+
+            # Use gradient checkpointing if enabled
+            if use_gradient_checkpointing or use_gradient_checkpointing_offload:
+                layer_outputs = gradient_checkpoint_forward(
+                    layer_module,
+                    use_gradient_checkpointing,
+                    use_gradient_checkpointing_offload,
+                    *layer_args,
+                    **layer_kwargs,
+                )
+            else:
+                layer_outputs = layer_module(
+                    *layer_args,
+                    **layer_kwargs,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions and self.layers[index_block].use_cross_attention:
+                # layer_outputs structure: (hidden_states, self_attn_weights, cross_attn_weights)
+                if len(layer_outputs) >= 3:
+                    all_cross_attentions += (layer_outputs[2],)
+
+        if return_hidden_states:
+            return hidden_states
+
+        # Extract scale-shift parameters for adaptive output normalization
+        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)
+        shift = shift.to(hidden_states.device)
+        scale = scale.to(hidden_states.device)
+
+        # Apply adaptive layer norm: norm(x) * (1 + scale) + shift
+        hidden_states = (self.norm_out(hidden_states) * (1 + scale) + shift).type_as(hidden_states)
+        # Project output: de-patchify back to original sequence format
+        hidden_states = self.proj_out(hidden_states)
+
+        # Crop back to original sequence length to ensure exact length match (remove padding)
+        hidden_states = hidden_states[:, :original_seq_len, :]
+
+        outputs = (hidden_states, past_key_values)
+
+        if output_attentions:
+            outputs += (all_cross_attentions,)
+        return outputs