# 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 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, TritonMBConvPreGLU from diffusion.model.nets.sana import Sana, get_2d_sincos_pos_embed from diffusion.model.nets.sana_blocks import ( Attention, CaptionEmbedder, FlashAttention, LiteLA, MultiHeadCrossAttention, PatchEmbedMS, T2IFinalLayer, t2i_modulate, ) from diffusion.model.utils import auto_grad_checkpoint class SanaMSBlock(nn.Module): """ A Sana block with global shared adaptive layer norm zero (adaLN-Zero) conditioning. """ def __init__( self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0.0, input_size=None, qk_norm=False, attn_type="flash", ffn_type="mlp", mlp_acts=("silu", "silu", None), linear_head_dim=32, cross_norm=False, **block_kwargs, ): super().__init__() self.hidden_size = hidden_size 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 // linear_head_dim 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 self_num_heads = hidden_size // linear_head_dim 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, qk_norm=cross_norm, **block_kwargs) self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) 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 == "glumbconv_dilate": 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, dilation=2, ) elif ffn_type == "triton_mbconvpreglu": self.mlp = TritonMBConvPreGLU( 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 ) 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=mlp_acts, ) 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) def forward(self, x, y, t, mask=None, HW=None, **kwargs): B, N, C = x.shape 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), HW=HW)) 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), HW=HW)) return x ############################################################################# # Core Sana Model # ################################################################################# @MODELS.register_module() class SanaMS(Sana): """ Diffusion model with a Transformer backbone. """ def __init__( self, input_size=32, patch_size=2, in_channels=4, hidden_size=1152, depth=28, 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, 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), linear_head_dim=32, cross_norm=False, **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, 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, linear_head_dim=linear_head_dim, **kwargs, ) self.h = self.w = 0 approx_gelu = lambda: nn.GELU(approximate="tanh") self.t_block = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True)) self.pos_embed_ms = None kernel_size = patch_embed_kernel or patch_size self.x_embedder = PatchEmbedMS(patch_size, in_channels, hidden_size, kernel_size=kernel_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, ) drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)] # stochastic depth decay rule self.blocks = nn.ModuleList( [ SanaMSBlock( 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, linear_head_dim=linear_head_dim, cross_norm=cross_norm, ) for i in range(depth) ] ) self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels) self.initialize() def forward(self, x, timestep, y, mask=None, data_info=None, **kwargs): """ Forward pass of Sana. 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 """ bs = x.shape[0] dtype = x.dtype timestep = timestep.to(dtype) y = y.to(dtype) self.h, self.w = x.shape[-2] // self.patch_size, x.shape[-1] // self.patch_size if self.use_pe: x = self.x_embedder(x) if self.pos_embed_ms is None or self.pos_embed_ms.shape[1:] != x.shape[1:]: self.pos_embed_ms = ( torch.from_numpy( get_2d_sincos_pos_embed( self.pos_embed.shape[-1], (self.h, self.w), pe_interpolation=self.pe_interpolation, base_size=self.base_size, ) ) .unsqueeze(0) .to(x.device) .to(dtype) ) x += self.pos_embed_ms # (N, T, D), where T = H * W / patch_size ** 2 else: x = self.x_embedder(x) t = self.t_embedder(timestep) # (N, D) t0 = self.t_block(t) y = self.y_embedder(y, self.training, mask=mask) # (N, 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]) for block in self.blocks: x = auto_grad_checkpoint( block, x, y, t0, y_lens, (self.h, self.w), **kwargs ) # (N, T, D) #support grad checkpoint 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 __call__(self, *args, **kwargs): """ This method allows the object to be called like a function. It simply calls the forward method. """ return self.forward(*args, **kwargs) def forward_with_dpmsolver(self, x, timestep, y, data_info, **kwargs): """ dpm solver donnot need variance prediction """ # https://github.com/openai/glide-text2im/blob/main/notebooks/text2im.ipynb model_out = self.forward(x, timestep, y, data_info=data_info, **kwargs) return model_out.chunk(2, dim=1)[0] if self.pred_sigma else model_out def unpatchify(self, x): """ x: (N, T, patch_size**2 * C) imgs: (N, H, W, C) """ c = self.out_channels p = self.x_embedder.patch_size[0] assert self.h * self.w == x.shape[1] x = x.reshape(shape=(x.shape[0], self.h, self.w, p, p, c)) x = torch.einsum("nhwpqc->nchpwq", x) imgs = x.reshape(shape=(x.shape[0], c, self.h * p, self.w * p)) return imgs def initialize(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) # 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) ################################################################################# # Sana Multi-scale Configs # ################################################################################# @MODELS.register_module() def SanaMS_600M_P1_D28(**kwargs): return SanaMS(depth=28, hidden_size=1152, patch_size=1, num_heads=16, **kwargs) @MODELS.register_module() def SanaMS_600M_P2_D28(**kwargs): return SanaMS(depth=28, hidden_size=1152, patch_size=2, num_heads=16, **kwargs) @MODELS.register_module() def SanaMS_600M_P4_D28(**kwargs): return SanaMS(depth=28, hidden_size=1152, patch_size=4, num_heads=16, **kwargs) @MODELS.register_module() def SanaMS_1600M_P1_D20(**kwargs): # 20 layers, 1648.48M return SanaMS(depth=20, hidden_size=2240, patch_size=1, num_heads=20, **kwargs) @MODELS.register_module() def SanaMS_1600M_P2_D20(**kwargs): # 28 layers, 1648.48M return SanaMS(depth=20, hidden_size=2240, patch_size=2, num_heads=20, **kwargs)