# Copyright 2024 NVIDIA CORPORATION & AFFILIATES # # 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. # # SPDX-License-Identifier: Apache-2.0 # This file is modified from https://github.com/PixArt-alpha/PixArt-sigma import os import torch import torch.nn as nn from timm.models.layers import DropPath from diffusion.model.builder import MODELS from diffusion.model.nets.basic_modules import DWMlp, GLUMBConv, MBConvPreGLU, Mlp from diffusion.model.nets.fastlinear.modules import TritonLiteMLA from diffusion.model.nets.sana import Sana, get_2d_sincos_pos_embed from diffusion.model.nets.sana_blocks import ( Attention, CaptionEmbedder, FlashAttention, LiteLA, MultiHeadCrossAttention, PatchEmbed, T2IFinalLayer, TimestepEmbedder, t2i_modulate, ) from diffusion.model.norms import RMSNorm from diffusion.model.utils import auto_grad_checkpoint, to_2tuple from diffusion.utils.logger import get_root_logger class SanaUBlock(nn.Module): """ A SanaU block with global shared adaptive layer norm (adaLN-single) conditioning and U-shaped model. """ def __init__( self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0, input_size=None, qk_norm=False, attn_type="flash", ffn_type="mlp", mlp_acts=("silu", "silu", None), skip_linear=False, **block_kwargs, ): super().__init__() self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) if attn_type == "flash": # flash self attention self.attn = FlashAttention( hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm, **block_kwargs, ) elif attn_type == "linear": # linear self attention # TODO: Here the num_heads set to 36 for tmp used self_num_heads = hidden_size // 32 self.attn = LiteLA(hidden_size, hidden_size, heads=self_num_heads, eps=1e-8, qk_norm=qk_norm) elif attn_type == "triton_linear": # linear self attention with triton kernel fusion # TODO: Here the num_heads set to 36 for tmp used self_num_heads = hidden_size // 32 self.attn = TritonLiteMLA(hidden_size, num_heads=self_num_heads, eps=1e-8) elif attn_type == "vanilla": # vanilla self attention self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True) else: raise ValueError(f"{attn_type} type is not defined.") self.cross_attn = MultiHeadCrossAttention(hidden_size, num_heads, **block_kwargs) self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) # to be compatible with lower version pytorch if ffn_type == "dwmlp": approx_gelu = lambda: nn.GELU(approximate="tanh") self.mlp = DWMlp( in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0 ) elif ffn_type == "glumbconv": self.mlp = GLUMBConv( in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), use_bias=(True, True, False), norm=(None, None, None), act=mlp_acts, ) elif ffn_type == "mbconvpreglu": self.mlp = MBConvPreGLU( in_dim=hidden_size, out_dim=hidden_size, mid_dim=int(hidden_size * mlp_ratio), use_bias=(True, True, False), norm=None, act=("silu", "silu", None), ) elif ffn_type == "mlp": approx_gelu = lambda: nn.GELU(approximate="tanh") self.mlp = Mlp( in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu, drop=0 ) else: raise ValueError(f"{ffn_type} type is not defined.") self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size**0.5) # skip connection if skip_linear: self.skip_linear = nn.Linear(hidden_size * 2, hidden_size, bias=True) def forward(self, x, y, t, mask=None, skip_x=None, **kwargs): B, N, C = x.shape if skip_x is not None: x = self.skip_linear(torch.cat([x, skip_x], dim=-1)) shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( self.scale_shift_table[None] + t.reshape(B, 6, -1) ).chunk(6, dim=1) x = x + self.drop_path(gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa)).reshape(B, N, C)) x = x + self.cross_attn(x, y, mask) x = x + self.drop_path(gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp))) return x ############################################################################# # Core SanaU Model # ################################################################################# @MODELS.register_module() class SanaU(Sana): """ Diffusion model with a Transformer backbone. """ def __init__( self, input_size=32, patch_size=2, in_channels=4, hidden_size=1152, depth=29, num_heads=16, mlp_ratio=4.0, class_dropout_prob=0.1, learn_sigma=True, pred_sigma=True, drop_path: float = 0.0, caption_channels=2304, pe_interpolation=1.0, config=None, model_max_length=300, micro_condition=False, qk_norm=False, y_norm=False, norm_eps=1e-5, attn_type="flash", ffn_type="mlp", use_pe=True, y_norm_scale_factor=1.0, patch_embed_kernel=None, mlp_acts=("silu", "silu", None), **kwargs, ): super().__init__( input_size=input_size, patch_size=patch_size, in_channels=in_channels, hidden_size=hidden_size, depth=depth, num_heads=num_heads, mlp_ratio=mlp_ratio, class_dropout_prob=class_dropout_prob, learn_sigma=learn_sigma, pred_sigma=pred_sigma, drop_path=drop_path, caption_channels=caption_channels, pe_interpolation=pe_interpolation, config=config, model_max_length=model_max_length, micro_condition=micro_condition, qk_norm=qk_norm, y_norm=y_norm, norm_eps=norm_eps, attn_type=attn_type, ffn_type=ffn_type, use_pe=use_pe, y_norm_scale_factor=y_norm_scale_factor, patch_embed_kernel=patch_embed_kernel, mlp_acts=mlp_acts, **kwargs, ) kernel_size = patch_embed_kernel or patch_size self.x_embedder = PatchEmbed( input_size, patch_size, in_channels, hidden_size, kernel_size=kernel_size, bias=True ) self.t_embedder = TimestepEmbedder(hidden_size) num_patches = self.x_embedder.num_patches self.base_size = input_size // self.patch_size # Will use fixed sin-cos embedding: self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size)) approx_gelu = lambda: nn.GELU(approximate="tanh") self.t_block = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True)) self.y_embedder = CaptionEmbedder( in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob, act_layer=approx_gelu, token_num=model_max_length, ) if self.y_norm: self.attention_y_norm = RMSNorm(hidden_size, scale_factor=y_norm_scale_factor, eps=norm_eps) drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule self.blocks = nn.ModuleList( [ SanaUBlock( hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i], input_size=(input_size // patch_size, input_size // patch_size), qk_norm=qk_norm, attn_type=attn_type, ffn_type=ffn_type, mlp_acts=mlp_acts, skip_linear=i > depth // 2, ) for i in range(depth) ] ) self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels) self.initialize_weights() if config: logger = get_root_logger(os.path.join(config.work_dir, "train_log.log")) logger = logger.warning else: logger = print logger(f"use pe: {use_pe}, position embed interpolation: {self.pe_interpolation}, base size: {self.base_size}") logger( f"attention type: {attn_type}; ffn type: {ffn_type}; " f"autocast linear attn: {os.environ.get('AUTOCAST_LINEAR_ATTN', False)}" ) def forward(self, x, timestep, y, mask=None, data_info=None, **kwargs): """ Forward pass of SanaU. x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) t: (N,) tensor of diffusion timesteps y: (N, 1, 120, C) tensor of class labels """ x = x.to(self.dtype) timestep = timestep.to(self.dtype) y = y.to(self.dtype) pos_embed = self.pos_embed.to(self.dtype) self.h, self.w = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2 t = self.t_embedder(timestep.to(x.dtype)) # (N, D) t0 = self.t_block(t) y = self.y_embedder(y, self.training) # (N, 1, L, D) if self.y_norm: y = self.attention_y_norm(y) if mask is not None: if mask.shape[0] != y.shape[0]: mask = mask.repeat(y.shape[0] // mask.shape[0], 1) mask = mask.squeeze(1).squeeze(1) y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1]) y_lens = mask.sum(dim=1).tolist() else: y_lens = [y.shape[2]] * y.shape[0] y = y.squeeze(1).view(1, -1, x.shape[-1]) results_hooker = {} for i, block in enumerate(self.blocks): if i > len(self.blocks) // 2: x = auto_grad_checkpoint(block, x, y, t0, y_lens, skip_x=results_hooker[len(self.blocks) - i - 1]) else: x = auto_grad_checkpoint(block, x, y, t0, y_lens) # (N, T, D) #support grad checkpoint results_hooker[i] = x x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels) x = self.unpatchify(x) # (N, out_channels, H, W) return x def initialize_weights(self): # Initialize transformer layers: def _basic_init(module): if isinstance(module, nn.Linear): torch.nn.init.xavier_uniform_(module.weight) if module.bias is not None: nn.init.constant_(module.bias, 0) self.apply(_basic_init) if self.use_pe: # Initialize (and freeze) pos_embed by sin-cos embedding: pos_embed = get_2d_sincos_pos_embed( self.pos_embed.shape[-1], int(self.x_embedder.num_patches**0.5), pe_interpolation=self.pe_interpolation, base_size=self.base_size, ) self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) # Initialize patch_embed like nn.Linear (instead of nn.Conv2d): w = self.x_embedder.proj.weight.data nn.init.xavier_uniform_(w.view([w.shape[0], -1])) # Initialize timestep embedding MLP: nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02) nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02) nn.init.normal_(self.t_block[1].weight, std=0.02) # Initialize caption embedding MLP: nn.init.normal_(self.y_embedder.y_proj.fc1.weight, std=0.02) nn.init.normal_(self.y_embedder.y_proj.fc2.weight, std=0.02) @property def dtype(self): return next(self.parameters()).dtype ################################################################################# # SanaU Configs # ################################################################################# @MODELS.register_module() def SanaMSU_600M_P1_D28(**kwargs): return SanaU(depth=28, hidden_size=1152, patch_size=1, num_heads=16, **kwargs) @MODELS.register_module() def SanaMSU_600M_P2_D28(**kwargs): return SanaU(depth=28, hidden_size=1152, patch_size=2, num_heads=16, **kwargs) @MODELS.register_module() def SanaMSU_600M_P4_D28(**kwargs): return SanaU(depth=28, hidden_size=1152, patch_size=4, num_heads=16, **kwargs) @MODELS.register_module() def SanaMSU_P1_D20(**kwargs): # 20 layers, 1648.48M return SanaU(depth=20, hidden_size=2240, patch_size=1, num_heads=20, **kwargs) @MODELS.register_module() def SanaMSU_P2_D20(**kwargs): # 28 layers, 1648.48M return SanaU(depth=20, hidden_size=2240, patch_size=2, num_heads=20, **kwargs)