# 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 torch import torch.nn as nn import numpy as np import math from timm.models.vision_transformer import Attention, Mlp # PatchEmbed from torch.nn import TransformerEncoder, TransformerEncoderLayer from .helpers import SinusoidalPosEmb class PatchEmbed(nn.Module): """ 2D Image to Patch Embedding """ def __init__( self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, norm_layer=None, flatten=True, bias=True, ): super().__init__() img_size = (img_size, 1) patch_size = (patch_size, 1) self.img_size = img_size self.patch_size = patch_size self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1]) self.num_patches = self.grid_size[0] * self.grid_size[1] self.flatten = flatten self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias) self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() def forward(self, x): B, C, H, W = x.shape #_assert(H == self.img_size[0], f"Input image height ({H}) doesn't match model ({self.img_size[0]}).") #_assert(W == self.img_size[1], f"Input image width ({W}) doesn't match model ({self.img_size[1]}).") x = self.proj(x) if self.flatten: x = x.flatten(2).transpose(1, 2) # BCHW -> BNC x = self.norm(x) return x def modulate(x, shift, scale): return x * (1 + scale.unsqueeze(1)) + shift.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), ) 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) return t_emb class LabelEmbedder(nn.Module): """ Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance. """ def __init__(self, num_classes, hidden_size, dropout_prob): super().__init__() use_cfg_embedding = dropout_prob > 0 self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size) self.num_classes = num_classes self.dropout_prob = dropout_prob def token_drop(self, labels, force_drop_ids=None): """ Drops labels to enable classifier-free guidance. """ if force_drop_ids is None: drop_ids = torch.rand(labels.shape[0], device=labels.device) < self.dropout_prob else: drop_ids = force_drop_ids == 1 labels = torch.where(drop_ids, self.num_classes, labels) return labels def forward(self, labels, train, force_drop_ids=None): use_dropout = self.dropout_prob > 0 if (train and use_dropout) or (force_drop_ids is not None): labels = self.token_drop(labels, force_drop_ids) embeddings = self.embedding_table(labels) return embeddings ################################################################################# # Core DiT Model # ################################################################################# class DiTBlock(nn.Module): """ A DiT block with adaptive layer norm zero (adaLN-Zero) conditioning. """ def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, **block_kwargs): super().__init__() self.norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.attn = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, **block_kwargs) self.norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) mlp_hidden_dim = int(hidden_size * mlp_ratio) approx_gelu = lambda: nn.GELU(approximate="tanh") self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0) self.adaLN_modulation = nn.Sequential( nn.SiLU(), nn.Linear(hidden_size, 6 * hidden_size, bias=True) ) def forward(self, x, c): shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.adaLN_modulation(c).chunk(6, dim=1) x = x + gate_msa.unsqueeze(1) * self.attn(modulate(self.norm1(x), shift_msa, scale_msa)) x = x + gate_mlp.unsqueeze(1) * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp)) return x class FinalLayer(nn.Module): """ The final layer of DiT. """ 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 * 1 * out_channels, bias=True) self.adaLN_modulation = nn.Sequential( nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True) ) def forward(self, x, c): shift, scale = self.adaLN_modulation(c).chunk(2, dim=1) x = modulate(self.norm_final(x), shift, scale) x = self.linear(x) return x class DiT(nn.Module): """ Diffusion model with a Transformer backbone. """ def __init__( self, input_size=32, patch_size=2, in_channels=4, hidden_size=384, depth=12, num_heads=6, mlp_ratio=4.0, class_dropout_prob=0.1, num_classes=1000, learn_sigma=False, ): super().__init__() self.learn_sigma = learn_sigma self.in_channels = in_channels self.out_channels = in_channels * 2 if learn_sigma else in_channels self.patch_size = patch_size self.num_heads = num_heads self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, bias=True) self.t_embedder = TimestepEmbedder(hidden_size) #self.y_embedder = LabelEmbedder(num_classes, hidden_size, class_dropout_prob) num_patches = self.x_embedder.num_patches # Will use fixed sin-cos embedding: self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, hidden_size), requires_grad=False) self.blocks = nn.ModuleList([ DiTBlock(hidden_size, num_heads, mlp_ratio=mlp_ratio) for _ in range(depth) ]) self.final_layer = FinalLayer(hidden_size, patch_size, self.out_channels) self.initialize_weights() 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) # 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 #print('pos_embed', pos_embed.shape, self.x_embedder.num_patches) 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])) nn.init.constant_(self.x_embedder.proj.bias, 0) # Initialize label embedding table: #nn.init.normal_(self.y_embedder.embedding_table.weight, std=0.02) # 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) # Zero-out adaLN modulation layers in DiT blocks: for block in self.blocks: nn.init.constant_(block.adaLN_modulation[-1].weight, 0) nn.init.constant_(block.adaLN_modulation[-1].bias, 0) # Zero-out output layers: nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0) nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0) nn.init.constant_(self.final_layer.linear.weight, 0) nn.init.constant_(self.final_layer.linear.bias, 0) 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] '''h = w = int(x.shape[1] ** 0.5) assert h * w == x.shape[1]''' h = x.shape[1] w = 1 #print(x.shape) x = x.reshape(shape=(x.shape[0], h, w, p, 1, c)) x = torch.einsum('nhwpqc->nchpwq', x) imgs = x.reshape(shape=(x.shape[0], c, h * p, w * 1)) return imgs def forward(self, x, t): """ Forward pass of DiT. x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images) t: (N,) tensor of diffusion timesteps y: (N,) tensor of class labels """ #print('x', x.shape, 'x_embedder', self.x_embedder(x).shape, 'pos_embed', self.pos_embed.shape) x = self.x_embedder(x) + self.pos_embed # (N, T, D), where T = H * W / patch_size ** 2 t = self.t_embedder(t) # (N, D) #y = self.y_embedder(y, self.training) # (N, D) #c = t + y # (N, D) c = t for block in self.blocks: x = block(x, c) # (N, T, D) x = self.final_layer(x, c) # (N, T, patch_size ** 2 * out_channels) x = self.unpatchify(x) # (N, out_channels, H, W) return x def forward_with_cfg(self, x, t, y, cfg_scale): """ Forward pass of DiT, 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] combined = torch.cat([half, half], dim=0) model_out = self.forward(combined, t, y) # 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:] eps, rest = model_out[:, :3], model_out[:, 3:] cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0) half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps) eps = torch.cat([half_eps, half_eps], dim=0) return torch.cat([eps, rest], dim=1) ################################################################################# # Sine/Cosine Positional Embedding Functions # ################################################################################# # https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False, extra_tokens=0): """ grid_size: int of the grid height and width return: pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token) """ grid_h = np.arange(grid_size, dtype=np.float32) grid_w = np.arange(1, dtype=np.float32) grid = np.meshgrid(grid_w, grid_h) # here w goes first grid = np.stack(grid, axis=0) grid = grid.reshape([2, 1, grid_size, 1]) pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid) if cls_token and extra_tokens > 0: pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0) return pos_embed def get_2d_sincos_pos_embed_from_grid(embed_dim, grid): assert embed_dim % 2 == 0 # use half of dimensions to encode grid_h emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2) emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2) emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D) return emb def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): """ embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D) """ assert embed_dim % 2 == 0 omega = np.arange(embed_dim // 2, dtype=np.float64) omega /= embed_dim / 2. omega = 1. / 10000**omega # (D/2,) pos = pos.reshape(-1) # (M,) out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product emb_sin = np.sin(out) # (M, D/2) emb_cos = np.cos(out) # (M, D/2) emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D) return emb def get_emb(sin_inp): """ Gets a base embedding for one dimension with sin and cos intertwined """ emb = torch.stack((sin_inp.sin(), sin_inp.cos()), dim=-1) return torch.flatten(emb, -2, -1) class PositionalEncoding1D(nn.Module): def __init__(self, channels, dtype_override=None): """ :param channels: The last dimension of the tensor you want to apply pos emb to. :param dtype_override: If set, overrides the dtype of the output embedding. """ super(PositionalEncoding1D, self).__init__() self.org_channels = channels channels = int(np.ceil(channels / 2) * 2) inv_freq = 1.0 / (10000 ** (torch.arange(0, channels, 2).float() / channels)) self.register_buffer("inv_freq", inv_freq) self.register_buffer("cached_penc", None, persistent=False) self.channels = channels self.dtype_override = dtype_override def forward(self, tensor): """ :param tensor: A 3d tensor of size (batch_size, ch, x) :return: Positional Encoding Matrix of size (batch_size, ch, x) """ if len(tensor.shape) != 3: raise RuntimeError("The input tensor has to be 3d!") if self.cached_penc is not None and self.cached_penc.shape == tensor.shape: return self.cached_penc self.cached_penc = None batch_size, orig_ch, x = tensor.shape pos_x = torch.arange(x, device=tensor.device, dtype=self.inv_freq.dtype) sin_inp_x = torch.einsum("i,j->ij", pos_x, self.inv_freq) emb_x = get_emb(sin_inp_x) #print('emb_x', emb_x.shape) emb = torch.zeros( (self.channels, x), device=tensor.device, dtype=( self.dtype_override if self.dtype_override is not None else tensor.dtype ), ) emb[:self.channels, :] = emb_x.permute(1,0) self.cached_penc = emb[None, :orig_ch, :].repeat(batch_size, 1, 1) return self.cached_penc class TransformerModel(nn.Module): def __init__(self, ntoken, ninp, nhead, nhid, nlayers=6, dropout=0.0): super(TransformerModel, self).__init__() self.model_type = 'Transformer' self.pos_encoder = PositionalEncoding1D(ninp) encoder_layers = TransformerEncoderLayer(ninp, nhead, nhid, dropout, batch_first=True) self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers) #self.encoder = nn.Embedding(ntoken, ninp) self.ninp = ninp #self.decoder = nn.Linear(ninp, ntoken) self.time_mlp = nn.Sequential( # should be removed for Noise and Deterministic Baselines SinusoidalPosEmb(ninp), nn.Linear(ninp-1, ninp * 4), nn.Mish(), nn.Linear(ninp * 4, ninp), ) #self.init_weights() def generate_square_subsequent_mask(self, sz): mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1) mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0)) return mask '''def init_weights(self): initrange = 0.1 self.encoder.weight.data.uniform_(-initrange, initrange) self.decoder.bias.data.zero_() self.decoder.weight.data.uniform_(-initrange, initrange)''' def forward(self, src, t): #print('self.ninp', self.ninp) #src = self.encoder(src) * math.sqrt(self.ninp) #print('src', src.shape) #t = self.time_mlp(t).unsqueeze(1) emb = self.pos_encoder(src) #time = torch.cat((t,t,t), dim=1) #print('time', time.shape) output = self.transformer_encoder(src+emb) #print('shape after transformer', output.shape) #output = self.decoder(output) return output ################################################################################# # DiT Configs # ################################################################################# def DiT_XL_2(**kwargs): return DiT(depth=28, hidden_size=1152, patch_size=2, num_heads=16, **kwargs) def DiT_XL_4(**kwargs): return DiT(depth=28, hidden_size=1152, patch_size=4, num_heads=16, **kwargs) def DiT_XL_8(**kwargs): return DiT(depth=28, hidden_size=1152, patch_size=8, num_heads=16, **kwargs) def DiT_L_2(**kwargs): return DiT(depth=24, hidden_size=1024, patch_size=2, num_heads=16, **kwargs) def DiT_L_4(**kwargs): return DiT(depth=24, hidden_size=1024, patch_size=4, num_heads=16, **kwargs) def DiT_L_8(**kwargs): return DiT(depth=24, hidden_size=1024, patch_size=8, num_heads=16, **kwargs) def DiT_B_2(**kwargs): return DiT(depth=12, hidden_size=768, patch_size=2, num_heads=12, **kwargs) def DiT_B_4(**kwargs): return DiT(depth=12, hidden_size=768, patch_size=4, num_heads=12, **kwargs) def DiT_B_8(**kwargs): return DiT(depth=12, hidden_size=768, patch_size=8, num_heads=12, **kwargs) def DiT_S_2(**kwargs): return DiT(depth=12, hidden_size=384, patch_size=2, num_heads=6, **kwargs) def DiT_S_4(**kwargs): return DiT(depth=12, hidden_size=384, patch_size=4, num_heads=6, **kwargs) def DiT_S_8(**kwargs): return DiT(depth=12, hidden_size=384, patch_size=8, num_heads=6, **kwargs) DiT_models = { 'DiT-XL/2': DiT_XL_2, 'DiT-XL/4': DiT_XL_4, 'DiT-XL/8': DiT_XL_8, 'DiT-L/2': DiT_L_2, 'DiT-L/4': DiT_L_4, 'DiT-L/8': DiT_L_8, 'DiT-B/2': DiT_B_2, 'DiT-B/4': DiT_B_4, 'DiT-B/8': DiT_B_8, 'DiT-S/2': DiT_S_2, 'DiT-S/4': DiT_S_4, 'DiT-S/8': DiT_S_8, }