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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 AniGenSparseStructureFlowModel(nn.Module):
def __init__(
self,
resolution: int,
in_channels: int,
in_channels_skl: int,
model_channels: int,
model_channels_skl: int,
cond_channels: int,
out_channels: int,
out_channels_skl: 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,
use_pretrain_branch: bool = True,
freeze_pretrain_branch: bool = True,
use_lora_ss: bool = False,
lora_lr_rate_ss: float = 0.1,
modules_to_freeze: Optional[List[str]] = ["blocks", "input_layer", "out_layer", "pos_emb", "t_embedder"],
adapter_ss_to_skl: bool = True,
adapter_skl_to_ss: bool = True,
predict_x0: bool = False,
predict_x0_skl: bool = False,
t_eps: float = 5e-2,
t_scale: float = 1e3,
z_is_global: bool = False,
z_skl_is_global: bool = False,
global_token_num: int = 1024,
global_token_num_skl: int = 1024,
cross_adapter_every: int = 4,
skl_cross_from_ss: bool = False,
):
super().__init__()
self.resolution = resolution
self.in_channels = in_channels
self.in_channels_skl = in_channels_skl
self.model_channels = model_channels
self.model_channels_skl = model_channels_skl
self.cond_channels = cond_channels
self.out_channels = out_channels
self.out_channels_skl = out_channels_skl
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.use_pretrain_branch = use_pretrain_branch
self.freeze_pretrain_branch = freeze_pretrain_branch or use_lora_ss
self.use_lora_ss = use_lora_ss
self.modules_to_freeze = modules_to_freeze
self.adapter_ss_to_skl = adapter_ss_to_skl
self.adapter_skl_to_ss = adapter_skl_to_ss
self.predict_x0 = predict_x0
self.predict_x0_skl = predict_x0_skl
self.t_eps = t_eps
self.t_scale = t_scale
self.z_is_global = z_is_global
self.z_skl_is_global = z_skl_is_global
self.global_token_num = global_token_num
self.global_token_num_skl = global_token_num_skl
self.cross_adapter_every = int(cross_adapter_every)
self.skl_cross_from_ss = skl_cross_from_ss
self.t_embedder = TimestepEmbedder(model_channels)
self.t_embedder_skl = TimestepEmbedder(model_channels_skl)
if share_mod:
self.adaLN_modulation = nn.Sequential(
nn.SiLU(),
nn.Linear(model_channels, 6 * model_channels, bias=True)
)
self.adaLN_modulation_skl = nn.Sequential(
nn.SiLU(),
nn.Linear(model_channels_skl, 6 * model_channels_skl, bias=True)
)
if pe_mode == "ape":
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)
if self.z_is_global:
pos_embedder = AbsolutePositionEmbedder(model_channels, 1)
pos_emb = pos_embedder(torch.arange(self.global_token_num, device=self.device)[:, None])
else:
pos_embedder = AbsolutePositionEmbedder(model_channels, 3)
pos_emb = pos_embedder(coords)
self.register_buffer("pos_emb", pos_emb)
if self.z_skl_is_global:
pos_embedder_skl = AbsolutePositionEmbedder(model_channels_skl, 1)
pos_emb_skl = pos_embedder_skl(torch.arange(self.global_token_num_skl, device=self.device)[:, None])
else:
pos_embedder_skl = AbsolutePositionEmbedder(model_channels_skl, 3)
pos_emb_skl = pos_embedder_skl(coords)
self.register_buffer("pos_emb_skl", pos_emb_skl)
self.input_layer = nn.Linear(in_channels * patch_size**3, model_channels)
self.input_layer_skl = nn.Linear(in_channels_skl * patch_size**3, model_channels_skl)
shallow = max(1, num_blocks // 3)
middle = max(1, num_blocks // 3 * 2)
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,
use_lora_self=self.use_lora_ss and idx >= middle,
lora_rank_self=8,
use_lora_cross=self.use_lora_ss,
lora_rank_cross=8+(idx // shallow)*8,
lora_lr_rate=lora_lr_rate_ss,
)
for idx in range(num_blocks)
])
self.blocks_skl = nn.ModuleList([
ModulatedTransformerCrossBlock(
model_channels_skl,
cond_channels if not self.skl_cross_from_ss else model_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,
use_context_norm=self.skl_cross_from_ss,
)
for _ in range(num_blocks)
])
# When using global tokens, ss and skl token counts may differ, so we use cross-attention
# for information exchange at a configurable frequency.
self.use_cross_adapter = (self.z_is_global or self.z_skl_is_global) and (
self.adapter_ss_to_skl or self.adapter_skl_to_ss
)
if self.adapter_ss_to_skl and not self.use_cross_adapter:
self.adapter_ss_to_skl_layers = nn.ModuleList([
nn.Linear(model_channels, model_channels_skl) for _ in range(num_blocks)
])
if self.adapter_skl_to_ss and not self.use_cross_adapter:
self.adapter_skl_to_ss_layers = nn.ModuleList([
nn.Linear(model_channels_skl, model_channels) for _ in range(num_blocks)
])
self.cross_adapter_every = max(1, self.cross_adapter_every)
self.cross_block_indices: List[int] = [
idx for idx in range(num_blocks) if (idx + 1) % self.cross_adapter_every == 0
]
if self.use_cross_adapter and len(self.cross_block_indices) == 0 and num_blocks > 0:
self.cross_block_indices = [num_blocks - 1]
if self.use_cross_adapter and len(self.cross_block_indices) > 0:
if self.adapter_ss_to_skl:
self.cross_blocks_ss_to_skl = nn.ModuleList([
ModulatedTransformerCrossBlock(
model_channels_skl,
model_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 self.cross_block_indices
])
self.cross_blocks_ss_to_skl_out = nn.ModuleList([
nn.Linear(model_channels_skl, model_channels_skl, bias=True)
for _ in self.cross_block_indices
])
if self.adapter_skl_to_ss:
self.cross_blocks_skl_to_ss = nn.ModuleList([
ModulatedTransformerCrossBlock(
model_channels,
model_channels_skl,
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 self.cross_block_indices
])
self.cross_blocks_skl_to_ss_out = nn.ModuleList([
nn.Linear(model_channels, model_channels, bias=True)
for _ in self.cross_block_indices
])
self.out_layer = nn.Linear(model_channels, out_channels * patch_size**3)
self.out_layer_skl = nn.Linear(model_channels_skl, out_channels_skl * patch_size**3)
self.initialize_weights()
if use_fp16:
self.convert_to_fp16()
if self.use_pretrain_branch and self.freeze_pretrain_branch:
for module in modules_to_freeze:
if hasattr(self, module):
mod = getattr(self, module)
if isinstance(mod, nn.ModuleList):
for m in mod:
for name, param in m.named_parameters():
if 'lora' not in name:
param.requires_grad = False
elif isinstance(mod, nn.Module):
for name, param in mod.named_parameters():
if 'lora' not in name:
param.requires_grad = False
elif isinstance(mod, torch.Tensor):
if mod.requires_grad:
mod.requires_grad = False
@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)
self.blocks_skl.apply(convert_module_to_f16)
if hasattr(self, "adapter_ss_to_skl_layers"):
self.adapter_ss_to_skl_layers.apply(convert_module_to_f16)
if hasattr(self, "adapter_skl_to_ss_layers"):
self.adapter_skl_to_ss_layers.apply(convert_module_to_f16)
if getattr(self, "use_cross_adapter", False):
if hasattr(self, "cross_blocks_ss_to_skl"):
self.cross_blocks_ss_to_skl.apply(convert_module_to_f16)
self.cross_blocks_ss_to_skl_out.apply(convert_module_to_f16)
if hasattr(self, "cross_blocks_skl_to_ss"):
self.cross_blocks_skl_to_ss.apply(convert_module_to_f16)
self.cross_blocks_skl_to_ss_out.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)
self.blocks_skl.apply(convert_module_to_f32)
if hasattr(self, "adapter_ss_to_skl_layers"):
self.adapter_ss_to_skl_layers.apply(convert_module_to_f32)
if hasattr(self, "adapter_skl_to_ss_layers"):
self.adapter_skl_to_ss_layers.apply(convert_module_to_f32)
if getattr(self, "use_cross_adapter", False):
if hasattr(self, "cross_blocks_ss_to_skl"):
self.cross_blocks_ss_to_skl.apply(convert_module_to_f32)
self.cross_blocks_ss_to_skl_out.apply(convert_module_to_f32)
if hasattr(self, "cross_blocks_skl_to_ss"):
self.cross_blocks_skl_to_ss.apply(convert_module_to_f32)
self.cross_blocks_skl_to_ss_out.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)
nn.init.normal_(self.t_embedder_skl.mlp[0].weight, std=0.02)
nn.init.normal_(self.t_embedder_skl.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)
nn.init.constant_(self.adaLN_modulation_skl[-1].weight, 0)
nn.init.constant_(self.adaLN_modulation_skl[-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)
for block in self.blocks_skl:
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)
nn.init.constant_(self.out_layer_skl.weight, 0)
nn.init.constant_(self.out_layer_skl.bias, 0)
# Zero-out adapter layers if exist
if hasattr(self, "adapter_ss_to_skl_layers"):
for layer in self.adapter_ss_to_skl_layers:
nn.init.constant_(layer.weight, 0)
nn.init.constant_(layer.bias, 0)
if hasattr(self, "adapter_skl_to_ss_layers"):
for layer in self.adapter_skl_to_ss_layers:
nn.init.constant_(layer.weight, 0)
nn.init.constant_(layer.bias, 0)
# Zero-out cross adapter output projections (so we can safely finetune from pretrained ckpt)
if getattr(self, "use_cross_adapter", False):
if hasattr(self, "cross_blocks_ss_to_skl_out"):
for layer in self.cross_blocks_ss_to_skl_out:
nn.init.constant_(layer.weight, 0)
nn.init.constant_(layer.bias, 0)
if hasattr(self, "cross_blocks_skl_to_ss_out"):
for layer in self.cross_blocks_skl_to_ss_out:
nn.init.constant_(layer.weight, 0)
nn.init.constant_(layer.bias, 0)
def forward(self, x: torch.Tensor, x_skl: torch.Tensor, t: torch.Tensor, cond: torch.Tensor, **kwargs) -> torch.Tensor:
if not self.z_is_global:
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]}"
if not self.z_skl_is_global:
assert [*x_skl.shape] == [x_skl.shape[0], self.in_channels_skl, *[self.resolution] * 3], \
f"Input shape mismatch, got {x_skl.shape}, expected {[x_skl.shape[0], self.in_channels_skl, *[self.resolution] * 3]}"
if self.predict_x0:
xt = x.clone()
if self.predict_x0_skl:
xt_skl = x_skl.clone()
if not self.z_is_global:
h = patchify(x, self.patch_size)
h = h.view(*h.shape[:2], -1).permute(0, 2, 1).contiguous()
else:
h = x
if not self.z_skl_is_global:
h_skl = patchify(x_skl, self.patch_size)
h_skl = h_skl.view(*h_skl.shape[:2], -1).permute(0, 2, 1).contiguous()
else:
h_skl = x_skl
h = self.input_layer(h)
h = h + self.pos_emb[None]
h_skl = self.input_layer_skl(h_skl)
h_skl = h_skl + self.pos_emb_skl[None]
t_emb = self.t_embedder(t)
t_emb_skl = self.t_embedder_skl(t)
if self.share_mod:
t_emb = self.adaLN_modulation(t_emb)
t_emb_skl = self.adaLN_modulation_skl(t_emb_skl)
t_emb = t_emb.type(self.dtype)
t_emb_skl = t_emb_skl.type(self.dtype)
h = h.type(self.dtype)
h_skl = h_skl.type(self.dtype)
cond = cond.type(self.dtype)
cross_pos_to_idx = None
if self.use_cross_adapter and len(self.cross_block_indices) > 0:
cross_pos_to_idx = {bidx: cidx for cidx, bidx in enumerate(self.cross_block_indices)}
for idx, block, block_skl in zip(range(len(self.blocks)), self.blocks, self.blocks_skl):
f = block(h, t_emb, cond)
f_skl = block_skl(h_skl, t_emb_skl, h if self.skl_cross_from_ss else cond)
if self.use_cross_adapter and cross_pos_to_idx is not None and idx in cross_pos_to_idx:
cidx = cross_pos_to_idx[idx]
if self.adapter_ss_to_skl:
out_skl = self.cross_blocks_ss_to_skl[cidx](f_skl, t_emb_skl, f)
h_skl = f_skl + self.cross_blocks_ss_to_skl_out[cidx](out_skl - f_skl)
else:
h_skl = f_skl
if self.adapter_skl_to_ss:
out = self.cross_blocks_skl_to_ss[cidx](f, t_emb, f_skl)
h = f + self.cross_blocks_skl_to_ss_out[cidx](out - f)
else:
h = f
else:
# Non-global (or no cross block at this idx): keep previous behavior.
if self.adapter_ss_to_skl and (not self.use_cross_adapter):
h_skl = f_skl + self.adapter_ss_to_skl_layers[idx](f)
else:
h_skl = f_skl
if self.adapter_skl_to_ss and (not self.use_cross_adapter):
h = f + self.adapter_skl_to_ss_layers[idx](f_skl)
else:
h = f
h = h.type(x.dtype)
h = F.layer_norm(h, h.shape[-1:])
h = self.out_layer(h)
h_skl = h_skl.type(x_skl.dtype)
h_skl = F.layer_norm(h_skl, h_skl.shape[-1:])
h_skl = self.out_layer_skl(h_skl)
if not self.z_is_global:
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()
if not self.z_skl_is_global:
h_skl = h_skl.permute(0, 2, 1).view(h_skl.shape[0], h_skl.shape[2], *[self.resolution // self.patch_size] * 3)
h_skl = unpatchify(h_skl, self.patch_size).contiguous()
if self.predict_x0:
t_normalized = t / self.t_scale
factor = (1 / t_normalized.clamp_min(self.t_eps)).reshape([t.shape[0], *([1] * (x.dim() - 1))])
h = (xt - h) * factor
if self.predict_x0_skl:
t_normalized = t / self.t_scale
factor = (1 / t_normalized.clamp_min(self.t_eps)).reshape([t.shape[0], *([1] * (x_skl.dim() - 1))])
h_skl = (xt_skl - h_skl) * factor
return h, h_skl
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