from typing import * import torch import torch.nn as nn import torch.nn.functional as F import numpy as np from ..modules.utils import convert_module_to_f16, convert_module_to_f32 from ..modules.transformer import AbsolutePositionEmbedder, ModulatedTransformerCrossBlock from ..modules.spatial import patchify, unpatchify 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. Args: t: a 1-D Tensor of N indices, one per batch element. These may be fractional. dim: the dimension of the output. max_period: controls the minimum frequency of the embeddings. Returns: 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( -np.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 SparseStructureFlowModel(nn.Module): def __init__( self, resolution: int, in_channels: int, model_channels: int, cond_channels: int, out_channels: int, num_blocks: int, num_heads: Optional[int] = None, num_head_channels: Optional[int] = 64, mlp_ratio: float = 4, patch_size: int = 2, pe_mode: Literal["ape", "rope"] = "ape", use_fp16: bool = False, use_checkpoint: bool = False, share_mod: bool = False, qk_rms_norm: bool = False, qk_rms_norm_cross: bool = False, ): super().__init__() self.resolution = resolution self.in_channels = in_channels self.model_channels = model_channels self.cond_channels = cond_channels self.out_channels = out_channels self.num_blocks = num_blocks self.num_heads = num_heads or model_channels // num_head_channels self.mlp_ratio = mlp_ratio self.patch_size = patch_size self.pe_mode = pe_mode self.use_fp16 = use_fp16 self.use_checkpoint = use_checkpoint self.share_mod = share_mod self.qk_rms_norm = qk_rms_norm self.qk_rms_norm_cross = qk_rms_norm_cross self.dtype = torch.float16 if use_fp16 else torch.float32 self.t_embedder = TimestepEmbedder(model_channels) if share_mod: self.adaLN_modulation = nn.Sequential( nn.SiLU(), nn.Linear(model_channels, 6 * model_channels, bias=True) ) if pe_mode == "ape": pos_embedder = AbsolutePositionEmbedder(model_channels, 3) coords = torch.meshgrid(*[torch.arange(res, device=self.device) for res in [resolution // patch_size] * 3], indexing='ij') coords = torch.stack(coords, dim=-1).reshape(-1, 3) pos_emb = pos_embedder(coords) self.register_buffer("pos_emb", pos_emb) self.input_layer = nn.Linear(in_channels * patch_size**3, model_channels) self.blocks = nn.ModuleList([ ModulatedTransformerCrossBlock( model_channels, cond_channels, num_heads=self.num_heads, mlp_ratio=self.mlp_ratio, attn_mode='full', use_checkpoint=self.use_checkpoint, use_rope=(pe_mode == "rope"), share_mod=share_mod, qk_rms_norm=self.qk_rms_norm, qk_rms_norm_cross=self.qk_rms_norm_cross, ) for _ in range(num_blocks) ]) self.out_layer = nn.Linear(model_channels, out_channels * patch_size**3) self.initialize_weights() if use_fp16: self.convert_to_fp16() @property def device(self) -> torch.device: """ Return the device of the model. """ return next(self.parameters()).device def convert_to_fp16(self) -> None: """ Convert the torso of the model to float16. """ self.blocks.apply(convert_module_to_f16) def convert_to_fp32(self) -> None: """ Convert the torso of the model to float32. """ self.blocks.apply(convert_module_to_f32) def initialize_weights(self) -> None: # 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 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: if self.share_mod: nn.init.constant_(self.adaLN_modulation[-1].weight, 0) nn.init.constant_(self.adaLN_modulation[-1].bias, 0) else: 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.out_layer.weight, 0) nn.init.constant_(self.out_layer.bias, 0) def forward(self, x: torch.Tensor, t: torch.Tensor, cond: torch.Tensor) -> torch.Tensor: assert [*x.shape] == [x.shape[0], self.in_channels, *[self.resolution] * 3], \ f"Input shape mismatch, got {x.shape}, expected {[x.shape[0], self.in_channels, *[self.resolution] * 3]}" h = patchify(x, self.patch_size) h = h.view(*h.shape[:2], -1).permute(0, 2, 1).contiguous() h = self.input_layer(h) h = h + self.pos_emb[None] t_emb = self.t_embedder(t) if self.share_mod: t_emb = self.adaLN_modulation(t_emb) t_emb = t_emb.type(self.dtype) h = h.type(self.dtype) cond = cond.type(self.dtype) for block in self.blocks: h = block(h, t_emb, cond) h = h.type(x.dtype) h = F.layer_norm(h, h.shape[-1:]) h = self.out_layer(h) h = h.permute(0, 2, 1).view(h.shape[0], h.shape[2], *[self.resolution // self.patch_size] * 3) h = unpatchify(h, self.patch_size).contiguous() return h