# 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, )