# Copyright Alpha VLLM/Lumina Image 2.0 and contributors # Copyright (c) Meta Platforms, Inc. and affiliates. # 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. # # 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 dataclasses import dataclass import torch from torch import Tensor from torch.utils.checkpoint import checkpoint import torch.nn as nn import torch.nn.functional as F from library import custom_offloading_utils try: from flash_attn import flash_attn_varlen_func from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa except ImportError: # flash_attn may not be available but it is not required pass try: from sageattention import sageattn except ImportError: pass try: from apex.normalization import FusedRMSNorm as RMSNorm except ImportError: import warnings warnings.warn("Cannot import apex RMSNorm, switch to vanilla implementation") ############################################################################# # RMSNorm # ############################################################################# 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) -> Tensor: """ 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.float().pow(2).mean(-1, keepdim=True) + self.eps) def forward(self, x: Tensor): """ Apply RMSNorm to the input tensor. Args: x (torch.Tensor): The input tensor. Returns: torch.Tensor: The normalized tensor. """ x_dtype = x.dtype # To handle float8 we need to convert the tensor to float x = x.float() rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + self.eps) return ((x * rrms) * self.weight.float()).to(dtype=x_dtype) @dataclass class LuminaParams: """Parameters for Lumina model configuration""" patch_size: int = 2 in_channels: int = 4 dim: int = 4096 n_layers: int = 30 n_refiner_layers: int = 2 n_heads: int = 24 n_kv_heads: int = 8 multiple_of: int = 256 axes_dims: List[int] = None axes_lens: List[int] = None qk_norm: bool = False ffn_dim_multiplier: Optional[float] = None norm_eps: float = 1e-5 scaling_factor: float = 1.0 cap_feat_dim: int = 32 def __post_init__(self): if self.axes_dims is None: self.axes_dims = [36, 36, 36] if self.axes_lens is None: self.axes_lens = [300, 512, 512] @classmethod def get_2b_config(cls) -> "LuminaParams": """Returns the configuration for the 2B parameter model""" return cls( patch_size=2, in_channels=16, # VAE channels dim=2304, n_layers=26, n_heads=24, n_kv_heads=8, axes_dims=[32, 32, 32], axes_lens=[300, 512, 512], qk_norm=True, cap_feat_dim=2304, # Gemma 2 hidden_size ) @classmethod def get_7b_config(cls) -> "LuminaParams": """Returns the configuration for the 7B parameter model""" return cls( patch_size=2, dim=4096, n_layers=32, n_heads=32, n_kv_heads=8, axes_dims=[64, 64, 64], axes_lens=[300, 512, 512], ) class GradientCheckpointMixin(nn.Module): def __init__(self, *args, **kwargs) -> None: super().__init__(*args, **kwargs) self.gradient_checkpointing = False self.cpu_offload_checkpointing = False def enable_gradient_checkpointing(self, cpu_offload: bool = False): self.gradient_checkpointing = True def disable_gradient_checkpointing(self, cpu_offload: bool = False): self.gradient_checkpointing = False def forward(self, *args, **kwargs): if self.training and self.gradient_checkpointing: return checkpoint(self._forward, *args, use_reentrant=False, **kwargs) else: return self._forward(*args, **kwargs) def modulate(x, scale): return x * (1 + scale.unsqueeze(1)) ############################################################################# # Embedding Layers for Timesteps and Class Labels # ############################################################################# class TimestepEmbedder(GradientCheckpointMixin): """ 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 def to_cuda(x): if isinstance(x, torch.Tensor): return x.cuda() elif isinstance(x, (list, tuple)): return [to_cuda(elem) for elem in x] elif isinstance(x, dict): return {k: to_cuda(v) for k, v in x.items()} else: return x def to_cpu(x): if isinstance(x, torch.Tensor): return x.cpu() elif isinstance(x, (list, tuple)): return [to_cpu(elem) for elem in x] elif isinstance(x, dict): return {k: to_cpu(v) for k, v in x.items()} else: return x ############################################################################# # 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, use_flash_attn=False, use_sage_attn=False, ): """ 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. qk_norm (bool): Whether to use normalization for queries and keys. """ 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() self.use_flash_attn = use_flash_attn self.use_sage_attn = use_sage_attn if use_sage_attn : self.attention_processor = self.sage_attn else: # self.attention_processor = xformers.ops.memory_efficient_attention self.attention_processor = F.scaled_dot_product_attention def set_attention_processor(self, attention_processor): self.attention_processor = attention_processor def get_attention_processor(self): return self.attention_processor def forward( self, x: Tensor, x_mask: Tensor, freqs_cis: Tensor, ) -> Tensor: """ Args: x: x_mask: freqs_cis: """ 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 = apply_rope(xq, freqs_cis=freqs_cis) xk = apply_rope(xk, freqs_cis=freqs_cis) xq, xk = xq.to(dtype), xk.to(dtype) softmax_scale = math.sqrt(1 / self.head_dim) if self.use_sage_attn: # Handle GQA (Grouped Query Attention) if needed 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 = self.sage_attn(xq, xk, xv, x_mask, softmax_scale) elif self.use_flash_attn: output = self.flash_attn(xq, xk, xv, x_mask, softmax_scale) 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 = ( self.attention_processor( 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) # 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 sage_attn(self, q: Tensor, k: Tensor, v: Tensor, x_mask: Tensor, softmax_scale: float): try: bsz = q.shape[0] seqlen = q.shape[1] # Transpose to SageAttention's expected HND layout: [batch, heads, seq_len, head_dim] q_transposed = q.permute(0, 2, 1, 3) k_transposed = k.permute(0, 2, 1, 3) v_transposed = v.permute(0, 2, 1, 3) # Fast path: if all tokens are valid, run batched SageAttention directly if x_mask.all(): output = sageattn( q_transposed, k_transposed, v_transposed, tensor_layout="HND", is_causal=False, sm_scale=softmax_scale, ) # output: [batch, heads, seq_len, head_dim] -> [batch, seq_len, heads, head_dim] output = output.permute(0, 2, 1, 3) else: # Slow path: per-batch loop to handle variable-length masking # SageAttention does not support attention masks natively outputs = [] for b in range(bsz): valid_indices = x_mask[b].nonzero(as_tuple=True)[0] if valid_indices.numel() == 0: outputs.append(torch.zeros( seqlen, self.n_local_heads, self.head_dim, device=q.device, dtype=q.dtype, )) continue batch_output_valid = sageattn( q_transposed[b:b+1, :, valid_indices, :], k_transposed[b:b+1, :, valid_indices, :], v_transposed[b:b+1, :, valid_indices, :], tensor_layout="HND", is_causal=False, sm_scale=softmax_scale, ) batch_output = torch.zeros( seqlen, self.n_local_heads, self.head_dim, device=q.device, dtype=q.dtype, ) batch_output[valid_indices] = batch_output_valid.squeeze(0).permute(1, 0, 2) outputs.append(batch_output) output = torch.stack(outputs, dim=0) except NameError as e: raise RuntimeError( f"Could not load Sage Attention. Please install https://github.com/thu-ml/SageAttention. / Sage Attention を読み込めませんでした。https://github.com/thu-ml/SageAttention をインストールしてください。 / {e}" ) return output def flash_attn( self, q: Tensor, k: Tensor, v: Tensor, x_mask: Tensor, softmax_scale, ) -> Tensor: bsz, seqlen, _, _ = q.shape try: # begin var_len flash attn ( query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens, ) = self._upad_input(q, k, v, 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 return output except NameError as e: raise RuntimeError( f"Could not load flash attention. Please install flash_attn. / フラッシュアテンションを読み込めませんでした。flash_attn をインストールしてください。 / {e}" ) def apply_rope( 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.autocast("cuda", 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) 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(GradientCheckpointMixin): def __init__( self, layer_id: int, dim: int, n_heads: int, n_kv_heads: Optional[int], multiple_of: int, ffn_dim_multiplier: Optional[float], norm_eps: float, qk_norm: bool, modulation=True, use_flash_attn=False, use_sage_attn=False, ) -> 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): Number of multiple of the hidden dimension. ffn_dim_multiplier (Optional[float]): Dimension multiplier for the feedforward layer. norm_eps (float): Epsilon value for normalization. qk_norm (bool): Whether to use normalization for queries and keys. modulation (bool): Whether to use modulation for the attention layer. """ super().__init__() self.dim = dim self.head_dim = dim // n_heads self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, use_flash_attn=use_flash_attn, use_sage_attn=use_sage_attn) 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, pe: torch.Tensor, adaln_input: Optional[torch.Tensor] = None, ): """ Perform a forward pass through the TransformerBlock. Args: x (Tensor): Input tensor. pe (Tensor): Rope position embedding. Returns: 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, pe, ) ) 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, pe, ) ) x = x + self.ffn_norm2( self.feed_forward( self.ffn_norm1(x), ) ) return x class FinalLayer(GradientCheckpointMixin): """ The final layer of NextDiT. """ def __init__(self, hidden_size, patch_size, out_channels): """ Initialize the FinalLayer. Args: hidden_size (int): Hidden size of the input features. patch_size (int): Patch size of the input features. out_channels (int): Number of output 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): device = ids.device self.freqs_cis = [freqs_cis.to(ids.device) for freqs_cis in self.freqs_cis] result = [] for i in range(len(self.axes_dims)): freqs = self.freqs_cis[i].to(ids.device) index = ids[:, :, i : i + 1].repeat(1, 1, freqs.shape[-1]).to(torch.int64) result.append(torch.gather(freqs.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], use_flash_attn=False, use_sage_attn=False, ) -> None: """ Initialize the NextDiT model. Args: patch_size (int): Patch size of the input features. in_channels (int): Number of input channels. dim (int): Hidden size of the input features. n_layers (int): Number of Transformer layers. n_refiner_layers (int): Number of refiner layers. 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): Multiple of the hidden size. ffn_dim_multiplier (Optional[float]): Dimension multiplier for the feedforward layer. norm_eps (float): Epsilon value for normalization. qk_norm (bool): Whether to use query key normalization. cap_feat_dim (int): Dimension of the caption features. axes_dims (List[int]): List of dimensions for the axes. axes_lens (List[int]): List of lengths for the axes. use_flash_attn (bool): Whether to use Flash Attention. use_sage_attn (bool): Whether to use Sage Attention. Sage Attention only supports inference. Returns: None """ super().__init__() self.in_channels = in_channels self.out_channels = in_channels self.patch_size = patch_size 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].bias) 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.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.layers = nn.ModuleList( [ JointTransformerBlock( layer_id, dim, n_heads, n_kv_heads, multiple_of, ffn_dim_multiplier, norm_eps, qk_norm, use_flash_attn=use_flash_attn, use_sage_attn=use_sage_attn, ) 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 self.gradient_checkpointing = False self.cpu_offload_checkpointing = False # TODO: not yet supported self.blocks_to_swap = None # TODO: not yet supported @property def device(self): return next(self.parameters()).device @property def dtype(self): return next(self.parameters()).dtype def enable_gradient_checkpointing(self, cpu_offload: bool = False): self.gradient_checkpointing = True self.cpu_offload_checkpointing = cpu_offload self.t_embedder.enable_gradient_checkpointing() for block in self.layers + self.context_refiner + self.noise_refiner: block.enable_gradient_checkpointing(cpu_offload=cpu_offload) self.final_layer.enable_gradient_checkpointing() print(f"Lumina: Gradient checkpointing enabled. CPU offload: {cpu_offload}") def disable_gradient_checkpointing(self): self.gradient_checkpointing = False self.cpu_offload_checkpointing = False self.t_embedder.disable_gradient_checkpointing() for block in self.layers + self.context_refiner + self.noise_refiner: block.disable_gradient_checkpointing() self.final_layer.disable_gradient_checkpointing() print("Lumina: Gradient checkpointing disabled.") def unpatchify( self, x: Tensor, width: int, height: int, encoder_seq_lengths: List[int], seq_lengths: List[int], ) -> Tensor: """ Unpatchify the input tensor and embed the caption features. x: (N, T, patch_size**2 * C) imgs: (N, H, W, C) Args: x (Tensor): Input tensor. width (int): Width of the input tensor. height (int): Height of the input tensor. encoder_seq_lengths (List[int]): List of encoder sequence lengths. seq_lengths (List[int]): List of sequence lengths Returns: output: (N, C, H, W) """ pH = pW = self.patch_size output = [] for i, (encoder_seq_len, seq_len) in enumerate(zip(encoder_seq_lengths, seq_lengths)): output.append( x[i][encoder_seq_len:seq_len] .view(height // pH, width // pW, pH, pW, self.out_channels) .permute(4, 0, 2, 1, 3) .flatten(3, 4) .flatten(1, 2) ) output = torch.stack(output, dim=0) return output def patchify_and_embed( self, x: Tensor, cap_feats: Tensor, cap_mask: Tensor, t: Tensor, ) -> Tuple[Tensor, Tensor, Tensor, List[int], List[int]]: """ Patchify and embed the input image and caption features. Args: x: (N, C, H, W) image latents cap_feats: (N, C, D) caption features cap_mask: (N, C, D) caption attention mask t: (N), T timesteps Returns: Tuple[Tensor, Tensor, Tensor, List[int], List[int]]: return x, attention_mask, freqs_cis, l_effective_cap_len, seq_lengths """ bsz, channels, height, width = x.shape pH = pW = self.patch_size device = x.device l_effective_cap_len = cap_mask.sum(dim=1).tolist() encoder_seq_len = cap_mask.shape[1] image_seq_len = (height // self.patch_size) * (width // self.patch_size) seq_lengths = [cap_seq_len + image_seq_len for cap_seq_len in l_effective_cap_len] max_seq_len = max(seq_lengths) position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.int32, device=device) for i, (cap_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)): H_tokens, W_tokens = height // pH, width // pW position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.int32, device=device) position_ids[i, cap_len:seq_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:seq_len, 1] = row_ids position_ids[i, cap_len:seq_len, 2] = col_ids # Get combined rotary embeddings freqs_cis = self.rope_embedder(position_ids) # Create separate rotary embeddings for captions and images cap_freqs_cis = torch.zeros( bsz, encoder_seq_len, freqs_cis.shape[-1], device=device, dtype=freqs_cis.dtype, ) img_freqs_cis = torch.zeros( bsz, image_seq_len, freqs_cis.shape[-1], device=device, dtype=freqs_cis.dtype, ) for i, (cap_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)): cap_freqs_cis[i, :cap_len] = freqs_cis[i, :cap_len] img_freqs_cis[i, :image_seq_len] = freqs_cis[i, cap_len:seq_len] # Refine caption context for layer in self.context_refiner: cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis) x = x.view(bsz, channels, height // pH, pH, width // pW, pW).permute(0, 2, 4, 3, 5, 1).flatten(3).flatten(1, 2) # x.shape[1] == image_seq_len after patchify, so this was assigning to itself. # The mask can be set without a loop since all samples have the same image_seq_len. x_mask = torch.ones(bsz, image_seq_len, dtype=torch.bool, device=device) x = self.x_embedder(x) # Refine image context for layer in self.noise_refiner: x = layer(x, x_mask, img_freqs_cis, t) joint_hidden_states = torch.zeros(bsz, max_seq_len, self.dim, device=device, dtype=x.dtype) attention_mask = torch.zeros(bsz, max_seq_len, dtype=torch.bool, device=device) for i, (cap_len, seq_len) in enumerate(zip(l_effective_cap_len, seq_lengths)): attention_mask[i, :seq_len] = True joint_hidden_states[i, :cap_len] = cap_feats[i, :cap_len] joint_hidden_states[i, cap_len:seq_len] = x[i] x = joint_hidden_states return x, attention_mask, freqs_cis, l_effective_cap_len, seq_lengths def forward(self, x: Tensor, t: Tensor, cap_feats: Tensor, cap_mask: Tensor) -> Tensor: """ Forward pass of NextDiT. Args: x: (N, C, H, W) image latents t: (N,) tensor of diffusion timesteps cap_feats: (N, L, D) caption features cap_mask: (N, L) caption attention mask Returns: x: (N, C, H, W) denoised latents """ _, _, height, width = x.shape # B, C, H, W t = self.t_embedder(t) # (N, D) cap_feats = self.cap_embedder(cap_feats) # (N, L, D) # todo check if able to batchify w.o. redundant compute x, mask, freqs_cis, l_effective_cap_len, seq_lengths = self.patchify_and_embed(x, cap_feats, cap_mask, t) if not self.blocks_to_swap: for layer in self.layers: x = layer(x, mask, freqs_cis, t) else: for block_idx, layer in enumerate(self.layers): self.offloader_main.wait_for_block(block_idx) x = layer(x, mask, freqs_cis, t) self.offloader_main.submit_move_blocks(self.layers, block_idx) x = self.final_layer(x, t) x = self.unpatchify(x, width, height, l_effective_cap_len, seq_lengths) return x def forward_with_cfg( self, x: Tensor, t: Tensor, cap_feats: Tensor, cap_mask: Tensor, cfg_scale: float, cfg_trunc: float = 0.25, renorm_cfg: float = 1.0, ): """ 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] assert ( cap_mask.shape[0] == combined.shape[0] ), f"caption attention mask shape: {cap_mask.shape[0]} latents shape: {combined.shape[0]}" model_out = self.forward(x, 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, ) -> List[Tensor]: """ 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: List[torch.Tensor]: Precomputed frequency tensor with complex exponentials. """ freqs_cis = [] freqs_dtype = torch.float32 if torch.backends.mps.is_available() else torch.float64 for i, (d, e) in enumerate(zip(dim, end)): pos = torch.arange(e, dtype=freqs_dtype, device="cpu") freqs = 1.0 / (theta ** (torch.arange(0, d, 2, dtype=freqs_dtype, device="cpu") / d)) freqs = torch.outer(pos, freqs) freqs_cis_i = torch.polar(torch.ones_like(freqs), freqs) # [S, D/2] 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) def enable_block_swap(self, blocks_to_swap: int, device: torch.device): """ Enable block swapping to reduce memory usage during inference. Args: num_blocks (int): Number of blocks to swap between CPU and device device (torch.device): Device to use for computation """ self.blocks_to_swap = blocks_to_swap # Calculate how many blocks to swap from main layers assert blocks_to_swap <= len(self.layers) - 2, ( f"Cannot swap more than {len(self.layers) - 2} main blocks. " f"Requested {blocks_to_swap} blocks." ) self.offloader_main = custom_offloading_utils.ModelOffloader( self.layers, blocks_to_swap, device, debug=False ) def move_to_device_except_swap_blocks(self, device: torch.device): """ Move the model to the device except for blocks that will be swapped. This reduces temporary memory usage during model loading. Args: device (torch.device): Device to move the model to """ if self.blocks_to_swap: save_layers = self.layers self.layers = nn.ModuleList([]) self.to(device) if self.blocks_to_swap: self.layers = save_layers def prepare_block_swap_before_forward(self): """ Prepare blocks for swapping before forward pass. """ if self.blocks_to_swap is None or self.blocks_to_swap == 0: return self.offloader_main.prepare_block_devices_before_forward(self.layers) ############################################################################# # NextDiT Configs # ############################################################################# def NextDiT_2B_GQA_patch2_Adaln_Refiner(params: Optional[LuminaParams] = None, **kwargs): if params is None: params = LuminaParams.get_2b_config() return NextDiT( patch_size=params.patch_size, in_channels=params.in_channels, dim=params.dim, n_layers=params.n_layers, n_heads=params.n_heads, n_kv_heads=params.n_kv_heads, axes_dims=params.axes_dims, axes_lens=params.axes_lens, qk_norm=params.qk_norm, ffn_dim_multiplier=params.ffn_dim_multiplier, norm_eps=params.norm_eps, cap_feat_dim=params.cap_feat_dim, **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, )