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models/__init__.py

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+from .model import NextDiT_2B_GQA_patch2_Adaln_Refiner, NextDiT_3B_GQA_patch2_Adaln_Refiner, NextDiT_4B_GQA_patch2_Adaln_Refiner, NextDiT_7B_GQA_patch2_Adaln_Refiner

models/components.py

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+import warnings
+
+import torch
+import torch.nn as nn
+
+try:
+    from apex.normalization import FusedRMSNorm as RMSNorm
+except ImportError:
+    warnings.warn("Cannot import apex RMSNorm, switch to vanilla implementation")
+
+    class RMSNorm(torch.nn.Module):
+        def __init__(self, dim: int, eps: float = 1e-6):
+            """
+            Initialize the RMSNorm normalization layer.
+
+            Args:
+                dim (int): The dimension of the input tensor.
+                eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+
+            Attributes:
+                eps (float): A small value added to the denominator for numerical stability.
+                weight (nn.Parameter): Learnable scaling parameter.
+
+            """
+            super().__init__()
+            self.eps = eps
+            self.weight = nn.Parameter(torch.ones(dim))
+
+        def _norm(self, x):
+            """
+            Apply the RMSNorm normalization to the input tensor.
+
+            Args:
+                x (torch.Tensor): The input tensor.
+
+            Returns:
+                torch.Tensor: The normalized tensor.
+
+            """
+            return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+        def forward(self, x):
+            """
+            Forward pass through the RMSNorm layer.
+
+            Args:
+                x (torch.Tensor): The input tensor.
+
+            Returns:
+                torch.Tensor: The output tensor after applying RMSNorm.
+
+            """
+            output = self._norm(x.float()).type_as(x)
+            return output * self.weight

models/model.py

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+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+# --------------------------------------------------------
+# References:
+# GLIDE: https://github.com/openai/glide-text2im
+# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
+# --------------------------------------------------------
+
+import math
+from typing import List, Optional, Tuple
+
+from flash_attn import flash_attn_varlen_func
+from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .components import RMSNorm
+
+
+def modulate(x, scale):
+    return x * (1 + scale.unsqueeze(1))
+
+
+#############################################################################
+#             Embedding Layers for Timesteps and Class Labels               #
+#############################################################################
+
+
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+
+    def __init__(self, hidden_size, frequency_embedding_size=256):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            nn.Linear(
+                frequency_embedding_size,
+                hidden_size,
+                bias=True,
+            ),
+            nn.SiLU(),
+            nn.Linear(
+                hidden_size,
+                hidden_size,
+                bias=True,
+            ),
+        )
+        nn.init.normal_(self.mlp[0].weight, std=0.02)
+        nn.init.zeros_(self.mlp[0].bias)
+        nn.init.normal_(self.mlp[2].weight, std=0.02)
+        nn.init.zeros_(self.mlp[2].bias)
+
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        """
+        Create sinusoidal timestep embeddings.
+        :param t: a 1-D Tensor of N indices, one per batch element.
+                          These may be fractional.
+        :param dim: the dimension of the output.
+        :param max_period: controls the minimum frequency of the embeddings.
+        :return: an (N, D) Tensor of positional embeddings.
+        """
+        # https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
+        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.frequency_embedding_size)
+        t_emb = self.mlp(t_freq.to(self.mlp[0].weight.dtype))
+        return t_emb
+
+
+#############################################################################
+#                               Core NextDiT Model                              #
+#############################################################################
+
+
+class JointAttention(nn.Module):
+    """Multi-head attention module."""
+
+    def __init__(
+        self,
+        dim: int,
+        n_heads: int,
+        n_kv_heads: Optional[int],
+        qk_norm: bool,
+    ):
+        """
+        Initialize the Attention module.
+
+        Args:
+            dim (int): Number of input dimensions.
+            n_heads (int): Number of heads.
+            n_kv_heads (Optional[int]): Number of kv heads, if using GQA.
+
+        """
+        super().__init__()
+        self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads
+        self.n_local_heads = n_heads
+        self.n_local_kv_heads = self.n_kv_heads
+        self.n_rep = self.n_local_heads // self.n_local_kv_heads
+        self.head_dim = dim // n_heads
+
+        self.qkv = nn.Linear(
+            dim,
+            (n_heads + self.n_kv_heads + self.n_kv_heads) * self.head_dim,
+            bias=False,
+        )
+        nn.init.xavier_uniform_(self.qkv.weight)
+
+        self.out = nn.Linear(
+            n_heads * self.head_dim,
+            dim,
+            bias=False,
+        )
+        nn.init.xavier_uniform_(self.out.weight)
+
+        if qk_norm:
+            self.q_norm = RMSNorm(self.head_dim)
+            self.k_norm = RMSNorm(self.head_dim)
+        else:
+            self.q_norm = self.k_norm = nn.Identity()
+
+    @staticmethod
+    def apply_rotary_emb(
+        x_in: torch.Tensor,
+        freqs_cis: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Apply rotary embeddings to input tensors using the given frequency
+        tensor.
+
+        This function applies rotary embeddings to the given query 'xq' and
+        key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The
+        input tensors are reshaped as complex numbers, and the frequency tensor
+        is reshaped for broadcasting compatibility. The resulting tensors
+        contain rotary embeddings and are returned as real tensors.
+
+        Args:
+            x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings.
+            freqs_cis (torch.Tensor): Precomputed frequency tensor for complex
+                exponentials.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor
+                and key tensor with rotary embeddings.
+        """
+        with torch.cuda.amp.autocast(enabled=False):
+            x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2))
+            freqs_cis = freqs_cis.unsqueeze(2)
+            x_out = torch.view_as_real(x * freqs_cis).flatten(3)
+            return x_out.type_as(x_in)
+
+    # copied from huggingface modeling_llama.py
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        def _get_unpad_data(attention_mask):
+            seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+            indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+            max_seqlen_in_batch = seqlens_in_batch.max().item()
+            cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+            return (
+                indices,
+                cu_seqlens,
+                max_seqlen_in_batch,
+            )
+
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+        key_layer = index_first_axis(
+            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
+            indices_k,
+        )
+        value_layer = index_first_axis(
+            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
+            indices_k,
+        )
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, self.n_local_heads, head_dim),
+                indices_k,
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        freqs_cis: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+
+        Args:
+            x:
+            x_mask:
+            freqs_cis:
+
+        Returns:
+
+        """
+        bsz, seqlen, _ = x.shape
+        dtype = x.dtype
+
+        xq, xk, xv = torch.split(
+            self.qkv(x),
+            [
+                self.n_local_heads * self.head_dim,
+                self.n_local_kv_heads * self.head_dim,
+                self.n_local_kv_heads * self.head_dim,
+            ],
+            dim=-1,
+        )
+        xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+        xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
+        xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
+        xq = self.q_norm(xq)
+        xk = self.k_norm(xk)
+        xq = JointAttention.apply_rotary_emb(xq, freqs_cis=freqs_cis)
+        xk = JointAttention.apply_rotary_emb(xk, freqs_cis=freqs_cis)
+        xq, xk = xq.to(dtype), xk.to(dtype)
+
+        softmax_scale = math.sqrt(1 / self.head_dim)
+
+        if dtype in [torch.float16, torch.bfloat16]:
+            # begin var_len flash attn
+            (
+                query_states,
+                key_states,
+                value_states,
+                indices_q,
+                cu_seq_lens,
+                max_seq_lens,
+            ) = self._upad_input(xq, xk, xv, x_mask, seqlen)
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            attn_output_unpad = flash_attn_varlen_func(
+                query_states,
+                key_states,
+                value_states,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_in_batch_q,
+                max_seqlen_k=max_seqlen_in_batch_k,
+                dropout_p=0.0,
+                causal=False,
+                softmax_scale=softmax_scale,
+            )
+            output = pad_input(attn_output_unpad, indices_q, bsz, seqlen)
+            # end var_len_flash_attn
+
+        else:
+            n_rep = self.n_local_heads // self.n_local_kv_heads
+            if n_rep >= 1:
+                xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
+                xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
+            output = (
+                F.scaled_dot_product_attention(
+                    xq.permute(0, 2, 1, 3),
+                    xk.permute(0, 2, 1, 3),
+                    xv.permute(0, 2, 1, 3),
+                    attn_mask=x_mask.bool().view(bsz, 1, 1, seqlen).expand(-1, self.n_local_heads, seqlen, -1),
+                    scale=softmax_scale,
+                )
+                .permute(0, 2, 1, 3)
+                .to(dtype)
+            )
+
+        output = output.flatten(-2)
+
+        return self.out(output)
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        multiple_of: int,
+        ffn_dim_multiplier: Optional[float],
+    ):
+        """
+        Initialize the FeedForward module.
+
+        Args:
+            dim (int): Input dimension.
+            hidden_dim (int): Hidden dimension of the feedforward layer.
+            multiple_of (int): Value to ensure hidden dimension is a multiple
+                of this value.
+            ffn_dim_multiplier (float, optional): Custom multiplier for hidden
+                dimension. Defaults to None.
+
+        """
+        super().__init__()
+        # custom dim factor multiplier
+        if ffn_dim_multiplier is not None:
+            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        self.w1 = nn.Linear(
+            dim,
+            hidden_dim,
+            bias=False,
+        )
+        nn.init.xavier_uniform_(self.w1.weight)
+        self.w2 = nn.Linear(
+            hidden_dim,
+            dim,
+            bias=False,
+        )
+        nn.init.xavier_uniform_(self.w2.weight)
+        self.w3 = nn.Linear(
+            dim,
+            hidden_dim,
+            bias=False,
+        )
+        nn.init.xavier_uniform_(self.w3.weight)
+
+    # @torch.compile
+    def _forward_silu_gating(self, x1, x3):
+        return F.silu(x1) * x3
+
+    def forward(self, x):
+        return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))
+
+
+class JointTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        layer_id: int,
+        dim: int,
+        n_heads: int,
+        n_kv_heads: int,
+        multiple_of: int,
+        ffn_dim_multiplier: float,
+        norm_eps: float,
+        qk_norm: bool,
+        modulation=True
+    ) -> None:
+        """
+        Initialize a TransformerBlock.
+
+        Args:
+            layer_id (int): Identifier for the layer.
+            dim (int): Embedding dimension of the input features.
+            n_heads (int): Number of attention heads.
+            n_kv_heads (Optional[int]): Number of attention heads in key and
+                value features (if using GQA), or set to None for the same as
+                query.
+            multiple_of (int):
+            ffn_dim_multiplier (float):
+            norm_eps (float):
+
+        """
+        super().__init__()
+        self.dim = dim
+        self.head_dim = dim // n_heads
+        self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm)
+        self.feed_forward = FeedForward(
+            dim=dim,
+            hidden_dim=4 * dim,
+            multiple_of=multiple_of,
+            ffn_dim_multiplier=ffn_dim_multiplier,
+        )
+        self.layer_id = layer_id
+        self.attention_norm1 = RMSNorm(dim, eps=norm_eps)
+        self.ffn_norm1 = RMSNorm(dim, eps=norm_eps)
+
+        self.attention_norm2 = RMSNorm(dim, eps=norm_eps)
+        self.ffn_norm2 = RMSNorm(dim, eps=norm_eps)
+
+        self.modulation = modulation
+        if modulation:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(),
+                nn.Linear(
+                    min(dim, 1024),
+                    4 * dim,
+                    bias=True,
+                ),
+            )
+            nn.init.zeros_(self.adaLN_modulation[1].weight)
+            nn.init.zeros_(self.adaLN_modulation[1].bias)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        freqs_cis: torch.Tensor,
+        adaln_input: Optional[torch.Tensor]=None,
+    ):
+        """
+        Perform a forward pass through the TransformerBlock.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+            freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies.
+
+        Returns:
+            torch.Tensor: Output tensor after applying attention and
+                feedforward layers.
+
+        """
+        if self.modulation:
+            assert adaln_input is not None
+            scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1)
+
+            x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2(
+                self.attention(
+                    modulate(self.attention_norm1(x), scale_msa),
+                    x_mask,
+                    freqs_cis,
+                )
+            )
+            x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(
+                self.feed_forward(
+                    modulate(self.ffn_norm1(x), scale_mlp),
+                )
+            )
+        else:
+            assert adaln_input is None
+            x = x + self.attention_norm2(
+                self.attention(
+                    self.attention_norm1(x),
+                    x_mask,
+                    freqs_cis,
+                )
+            )
+            x = x + self.ffn_norm2(
+                self.feed_forward(
+                    self.ffn_norm1(x),
+                )
+            )
+        return x
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of NextDiT.
+    """
+
+    def __init__(self, hidden_size, patch_size, out_channels):
+        super().__init__()
+        self.norm_final = nn.LayerNorm(
+            hidden_size,
+            elementwise_affine=False,
+            eps=1e-6,
+        )
+        self.linear = nn.Linear(
+            hidden_size,
+            patch_size * patch_size * out_channels,
+            bias=True,
+        )
+        nn.init.zeros_(self.linear.weight)
+        nn.init.zeros_(self.linear.bias)
+
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            nn.Linear(
+                min(hidden_size, 1024),
+                hidden_size,
+                bias=True,
+            ),
+        )
+        nn.init.zeros_(self.adaLN_modulation[1].weight)
+        nn.init.zeros_(self.adaLN_modulation[1].bias)
+
+    def forward(self, x, c):
+        scale = self.adaLN_modulation(c)
+        x = modulate(self.norm_final(x), scale)
+        x = self.linear(x)
+        return x
+
+
+class RopeEmbedder:
+    def __init__(
+        self, theta: float = 10000.0, axes_dims: List[int] = (16, 56, 56), axes_lens: List[int] = (1, 512, 512)
+    ):
+        super().__init__()
+        self.theta = theta
+        self.axes_dims = axes_dims
+        self.axes_lens = axes_lens
+        self.freqs_cis = NextDiT.precompute_freqs_cis(self.axes_dims, self.axes_lens, theta=self.theta)
+
+    def __call__(self, ids: torch.Tensor):
+        self.freqs_cis = [freqs_cis.to(ids.device) for freqs_cis in self.freqs_cis]
+        result = []
+        for i in range(len(self.axes_dims)):
+            # import torch.distributed as dist
+            # if not dist.is_initialized() or dist.get_rank() == 0:
+            #     import pdb
+            #     pdb.set_trace()
+            index = ids[:, :, i:i+1].repeat(1, 1, self.freqs_cis[i].shape[-1]).to(torch.int64)
+            result.append(torch.gather(self.freqs_cis[i].unsqueeze(0).repeat(index.shape[0], 1, 1), dim=1, index=index))
+        return torch.cat(result, dim=-1)
+
+
+class NextDiT(nn.Module):
+    """
+    Diffusion model with a Transformer backbone.
+    """
+
+    def __init__(
+        self,
+        patch_size: int = 2,
+        in_channels: int = 4,
+        dim: int = 4096,
+        n_layers: int = 32,
+        n_refiner_layers: int = 2,
+        n_heads: int = 32,
+        n_kv_heads: Optional[int] = None,
+        multiple_of: int = 256,
+        ffn_dim_multiplier: Optional[float] = None,
+        norm_eps: float = 1e-5,
+        qk_norm: bool = False,
+        cap_feat_dim: int = 5120,
+        axes_dims: List[int] = (16, 56, 56),
+        axes_lens: List[int] = (1, 512, 512),
+    ) -> None:
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = in_channels
+        self.patch_size = patch_size
+
+        self.x_embedder = nn.Linear(
+            in_features=patch_size * patch_size * in_channels,
+            out_features=dim,
+            bias=True,
+        )
+        nn.init.xavier_uniform_(self.x_embedder.weight)
+        nn.init.constant_(self.x_embedder.bias, 0.0)
+
+        self.noise_refiner = nn.ModuleList(
+            [
+                JointTransformerBlock(
+                    layer_id,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    multiple_of,
+                    ffn_dim_multiplier,
+                    norm_eps,
+                    qk_norm,
+                    modulation=True,
+                )
+                for layer_id in range(n_refiner_layers)
+            ]
+        )
+        self.context_refiner = nn.ModuleList(
+            [
+                JointTransformerBlock(
+                    layer_id,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    multiple_of,
+                    ffn_dim_multiplier,
+                    norm_eps,
+                    qk_norm,
+                    modulation=False,
+                )
+                for layer_id in range(n_refiner_layers)
+            ]
+        )
+
+        self.t_embedder = TimestepEmbedder(min(dim, 1024))
+        self.cap_embedder = nn.Sequential(
+            RMSNorm(cap_feat_dim, eps=norm_eps),
+            nn.Linear(
+                cap_feat_dim,
+                dim,
+                bias=True,
+            ),
+        )
+        nn.init.trunc_normal_(self.cap_embedder[1].weight, std=0.02)
+        # nn.init.zeros_(self.cap_embedder[1].weight)
+        nn.init.zeros_(self.cap_embedder[1].bias)
+
+        self.layers = nn.ModuleList(
+            [
+                JointTransformerBlock(
+                    layer_id,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    multiple_of,
+                    ffn_dim_multiplier,
+                    norm_eps,
+                    qk_norm,
+                )
+                for layer_id in range(n_layers)
+            ]
+        )
+        self.norm_final = RMSNorm(dim, eps=norm_eps)
+        self.final_layer = FinalLayer(dim, patch_size, self.out_channels)
+
+        assert (dim // n_heads) == sum(axes_dims)
+        self.axes_dims = axes_dims
+        self.axes_lens = axes_lens
+        self.rope_embedder = RopeEmbedder(axes_dims=axes_dims, axes_lens=axes_lens)
+        self.dim = dim
+        self.n_heads = n_heads
+
+    def unpatchify(
+        self, x: torch.Tensor, img_size: List[Tuple[int, int]], cap_size: List[int], return_tensor=False
+    ) -> List[torch.Tensor]:
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        pH = pW = self.patch_size
+        imgs = []
+        for i in range(x.size(0)):
+            H, W = img_size[i]
+            begin = cap_size[i]
+            end = begin + (H // pH) * (W // pW)
+            imgs.append(
+                x[i][begin:end]
+                .view(H // pH, W // pW, pH, pW, self.out_channels)
+                .permute(4, 0, 2, 1, 3)
+                .flatten(3, 4)
+                .flatten(1, 2)
+            )
+
+        if return_tensor:
+            imgs = torch.stack(imgs, dim=0)
+        return imgs
+
+    def patchify_and_embed(
+        self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]:
+        bsz = len(x)
+        pH = pW = self.patch_size
+        device = x[0].device
+
+        l_effective_cap_len = cap_mask.sum(dim=1).tolist()
+        img_sizes = [(img.size(1), img.size(2)) for img in x]
+        l_effective_img_len = [(H // pH) * (W // pW) for (H, W) in img_sizes]
+
+        max_seq_len = max(
+            (cap_len+img_len for cap_len, img_len in zip(l_effective_cap_len, l_effective_img_len))
+        )
+        max_cap_len = max(l_effective_cap_len)
+        max_img_len = max(l_effective_img_len)
+
+        position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.int32, device=device)
+
+        for i in range(bsz):
+            cap_len = l_effective_cap_len[i]
+            img_len = l_effective_img_len[i]
+            H, W = img_sizes[i]
+            H_tokens, W_tokens = H // pH, W // pW
+            assert H_tokens * W_tokens == img_len
+
+            position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.int32, device=device)
+            position_ids[i, cap_len:cap_len+img_len, 0] = cap_len
+            row_ids = torch.arange(H_tokens, dtype=torch.int32, device=device).view(-1, 1).repeat(1, W_tokens).flatten()
+            col_ids = torch.arange(W_tokens, dtype=torch.int32, device=device).view(1, -1).repeat(H_tokens, 1).flatten()
+            position_ids[i, cap_len:cap_len+img_len, 1] = row_ids
+            position_ids[i, cap_len:cap_len+img_len, 2] = col_ids
+
+        freqs_cis = self.rope_embedder(position_ids)
+
+        # build freqs_cis for cap and image individually
+        cap_freqs_cis_shape = list(freqs_cis.shape)
+        # cap_freqs_cis_shape[1] = max_cap_len
+        cap_freqs_cis_shape[1] = cap_feats.shape[1]
+        cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
+
+        img_freqs_cis_shape = list(freqs_cis.shape)
+        img_freqs_cis_shape[1] = max_img_len
+        img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
+
+        for i in range(bsz):
+            cap_len = l_effective_cap_len[i]
+            img_len = l_effective_img_len[i]
+            cap_freqs_cis[i, :cap_len] = freqs_cis[i, :cap_len]
+            img_freqs_cis[i, :img_len] = freqs_cis[i, cap_len:cap_len+img_len]
+
+        # refine context
+        for layer in self.context_refiner:
+            cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis)
+
+        # refine image
+        flat_x = []
+        for i in range(bsz):
+            img = x[i]
+            C, H, W = img.size()
+            img = img.view(C, H // pH, pH, W // pW, pW).permute(1, 3, 2, 4, 0).flatten(2).flatten(0, 1)
+            flat_x.append(img)
+        x = flat_x
+        padded_img_embed = torch.zeros(bsz, max_img_len, x[0].shape[-1], device=device, dtype=x[0].dtype)
+        padded_img_mask = torch.zeros(bsz, max_img_len, dtype=torch.bool, device=device)
+        for i in range(bsz):
+            padded_img_embed[i, :l_effective_img_len[i]] = x[i]
+            padded_img_mask[i, :l_effective_img_len[i]] = True
+
+        padded_img_embed = self.x_embedder(padded_img_embed)
+        for layer in self.noise_refiner:
+            padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t)
+
+        mask = torch.zeros(bsz, max_seq_len, dtype=torch.bool, device=device)
+        padded_full_embed = torch.zeros(bsz, max_seq_len, self.dim, device=device, dtype=x[0].dtype)
+        for i in range(bsz):
+            cap_len = l_effective_cap_len[i]
+            img_len = l_effective_img_len[i]
+
+            mask[i, :cap_len+img_len] = True
+            padded_full_embed[i, :cap_len] = cap_feats[i, :cap_len]
+            padded_full_embed[i, cap_len:cap_len+img_len] = padded_img_embed[i, :img_len]
+
+        return padded_full_embed, mask, img_sizes, l_effective_cap_len, freqs_cis
+
+
+    def forward(self, x, t, cap_feats, cap_mask):
+        """
+        Forward pass of NextDiT.
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of text tokens/features
+        """
+
+        # import torch.distributed as dist
+        # if not dist.is_initialized() or dist.get_rank() == 0:
+        #     import pdb
+        #     pdb.set_trace()
+            # torch.save([x, t, cap_feats, cap_mask], "./fake_input.pt")
+        t = self.t_embedder(t)  # (N, D)
+        adaln_input = t
+
+        cap_feats = self.cap_embedder(cap_feats)  # (N, L, D)  # todo check if able to batchify w.o. redundant compute
+
+        x_is_tensor = isinstance(x, torch.Tensor)
+        x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t)
+        freqs_cis = freqs_cis.to(x.device)
+
+        for layer in self.layers:
+            x = layer(x, mask, freqs_cis, adaln_input)
+
+        x = self.final_layer(x, adaln_input)
+        x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)
+
+        return x
+
+    def forward_with_cfg(
+        self,
+        x,
+        t,
+        cap_feats,
+        cap_mask,
+        cfg_scale,
+        cfg_trunc=1,
+        renorm_cfg=1
+    ):
+        """
+        Forward pass of NextDiT, but also batches the unconditional forward pass
+        for classifier-free guidance.
+        """
+        # # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb
+        half = x[: len(x) // 2]
+        if t[0] < cfg_trunc:
+            combined = torch.cat([half, half], dim=0) # [2, 16, 128, 128]
+            model_out = self.forward(combined, t, cap_feats, cap_mask) # [2, 16, 128, 128]
+            # For exact reproducibility reasons, we apply classifier-free guidance on only
+            # three channels by default. The standard approach to cfg applies it to all channels.
+            # This can be done by uncommenting the following line and commenting-out the line following that.
+            eps, rest = model_out[:, : self.in_channels], model_out[:, self.in_channels :]
+            cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
+            half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)  
+            if float(renorm_cfg) > 0.0: 
+                ori_pos_norm = torch.linalg.vector_norm(cond_eps
+                        , dim=tuple(range(1, len(cond_eps.shape))), keepdim=True
+                )
+                max_new_norm = ori_pos_norm * float(renorm_cfg)
+                new_pos_norm = torch.linalg.vector_norm(
+                        half_eps, dim=tuple(range(1, len(half_eps.shape))), keepdim=True
+                    )
+                if new_pos_norm >= max_new_norm:
+                    half_eps = half_eps * (max_new_norm / new_pos_norm)
+        else:
+            combined = half
+            model_out = self.forward(combined, t[:len(x) // 2], cap_feats[:len(x) // 2], cap_mask[:len(x) // 2])
+            eps, rest = model_out[:, : self.in_channels], model_out[:, self.in_channels :]
+            half_eps = eps
+
+        output = torch.cat([half_eps, half_eps], dim=0)
+        return output
+
+    @staticmethod
+    def precompute_freqs_cis(
+        dim: List[int],
+        end: List[int],
+        theta: float = 10000.0,
+    ):
+        """
+        Precompute the frequency tensor for complex exponentials (cis) with
+        given dimensions.
+
+        This function calculates a frequency tensor with complex exponentials
+        using the given dimension 'dim' and the end index 'end'. The 'theta'
+        parameter scales the frequencies. The returned tensor contains complex
+        values in complex64 data type.
+
+        Args:
+            dim (list): Dimension of the frequency tensor.
+            end (list): End index for precomputing frequencies.
+            theta (float, optional): Scaling factor for frequency computation.
+                Defaults to 10000.0.
+
+        Returns:
+            torch.Tensor: Precomputed frequency tensor with complex
+                exponentials.
+        """
+        freqs_cis = []
+        for i, (d, e) in enumerate(zip(dim, end)):
+            freqs = 1.0 / (theta ** (torch.arange(0, d, 2, dtype=torch.float64, device="cpu") / d))
+            timestep = torch.arange(e, device=freqs.device, dtype=torch.float64)
+            freqs = torch.outer(timestep, freqs).float()
+            freqs_cis_i = torch.polar(torch.ones_like(freqs), freqs).to(torch.complex64)  # complex64
+            freqs_cis.append(freqs_cis_i)
+
+        return freqs_cis
+
+    def parameter_count(self) -> int:
+        total_params = 0
+
+        def _recursive_count_params(module):
+            nonlocal total_params
+            for param in module.parameters(recurse=False):
+                total_params += param.numel()
+            for submodule in module.children():
+                _recursive_count_params(submodule)
+
+        _recursive_count_params(self)
+        return total_params
+
+    def get_fsdp_wrap_module_list(self) -> List[nn.Module]:
+        return list(self.layers)
+
+    def get_checkpointing_wrap_module_list(self) -> List[nn.Module]:
+        return list(self.layers)
+
+
+#############################################################################
+#                                 NextDiT Configs                               #
+#############################################################################
+
+def NextDiT_2B_GQA_patch2_Adaln_Refiner(**kwargs):
+    return NextDiT(
+        patch_size=2,
+        dim=2304,
+        n_layers=26,
+        n_heads=24,
+        n_kv_heads=8,
+        axes_dims=[32, 32, 32],
+        axes_lens=[300, 512, 512],
+        **kwargs
+    )
+
+def NextDiT_3B_GQA_patch2_Adaln_Refiner(**kwargs):
+    return NextDiT(
+        patch_size=2,
+        dim=2592,
+        n_layers=30,
+        n_heads=24,
+        n_kv_heads=8,
+        axes_dims=[36, 36, 36],
+        axes_lens=[300, 512, 512],
+        **kwargs,
+    )
+
+def NextDiT_4B_GQA_patch2_Adaln_Refiner(**kwargs):
+    return NextDiT(
+        patch_size=2,
+        dim=2880,
+        n_layers=32,
+        n_heads=24,
+        n_kv_heads=8,
+        axes_dims=[40, 40, 40],
+        axes_lens=[300, 512, 512],
+        **kwargs,
+    )
+
+def NextDiT_7B_GQA_patch2_Adaln_Refiner(**kwargs):
+    return NextDiT(
+        patch_size=2,
+        dim=3840,
+        n_layers=32,
+        n_heads=32,
+        n_kv_heads=8,
+        axes_dims=[40, 40, 40],
+        axes_lens=[300, 512, 512],
+        **kwargs,
+    )