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from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from ..modules.utils import convert_module_to, manual_cast, str_to_dtype
from ..modules.transformer import AbsolutePositionEmbedder
from ..modules import sparse as sp
from ..modules.sparse.transformer import ModulatedSparseTransformerCrossBlock
from .sparse_structure_flow import TimestepEmbedder
from .sparse_elastic_mixin import SparseTransformerElasticMixin
class SLatFlowModel(nn.Module):
"""
Structured Latent Flow Model for 3D generation.
Supports two conditioning modes:
- "cross": Standard cross-attention with image features
- "proj": View-aligned projection attention with camera-aware features
"""
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,
pe_mode: Literal["ape", "rope"] = "ape",
rope_freq: Tuple[float, float] = (1.0, 10000.0),
dtype: str = 'float32',
use_checkpoint: bool = False,
share_mod: bool = False,
initialization: str = 'vanilla',
qk_rms_norm: bool = False,
qk_rms_norm_cross: bool = False,
image_attn_mode: Literal["cross", "proj", "gated_proj"] = "cross",
proj_in_channels: Optional[int] = None,
vae_in_channels: Optional[int] = None,
**kwargs
):
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.pe_mode = pe_mode
self.use_checkpoint = use_checkpoint
self.share_mod = share_mod
self.initialization = initialization
self.qk_rms_norm = qk_rms_norm
self.qk_rms_norm_cross = qk_rms_norm_cross
self.image_attn_mode = image_attn_mode
self.proj_in_channels = proj_in_channels
self.vae_in_channels = vae_in_channels
self.dtype = str_to_dtype(dtype)
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":
self.pos_embedder = AbsolutePositionEmbedder(model_channels)
self.input_layer = sp.SparseLinear(in_channels, model_channels)
self.blocks = nn.ModuleList([
ModulatedSparseTransformerCrossBlock(
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"),
rope_freq=rope_freq,
share_mod=self.share_mod,
qk_rms_norm=self.qk_rms_norm,
qk_rms_norm_cross=self.qk_rms_norm_cross,
image_attn_mode=image_attn_mode,
proj_in_channels=proj_in_channels,
vae_in_channels=vae_in_channels,
)
for _ in range(num_blocks)
])
self.out_layer = sp.SparseLinear(model_channels, out_channels)
self.initialize_weights()
self.convert_to(self.dtype)
@property
def device(self) -> torch.device:
"""
Return the device of the model.
"""
return next(self.parameters()).device
def convert_to(self, dtype: torch.dtype) -> None:
"""
Convert the torso of the model to the specified dtype.
"""
self.dtype = dtype
self.blocks.apply(partial(convert_module_to, dtype=dtype))
def initialize_weights(self) -> None:
if self.initialization == 'vanilla':
# 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)
elif self.initialization == 'scaled':
# Initialize transformer layers:
def _basic_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, std=np.sqrt(2.0 / (5.0 * self.model_channels)))
if module.bias is not None:
nn.init.constant_(module.bias, 0)
self.apply(_basic_init)
# Scaled init for to_out and ffn2
def _scaled_init(module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, std=1.0 / np.sqrt(5 * self.num_blocks * self.model_channels))
if module.bias is not None:
nn.init.constant_(module.bias, 0)
for block in self.blocks:
block.self_attn.to_out.apply(_scaled_init)
# Handle cross, proj, and gated_proj modes
if self.image_attn_mode in ("proj", "gated_proj"):
block.cross_attn.cross_attn_block.to_out.apply(_scaled_init)
else:
block.cross_attn.to_out.apply(_scaled_init)
block.mlp.mlp[2].apply(_scaled_init)
# Initialize input layer to make the initial representation have variance 1
nn.init.normal_(self.input_layer.weight, std=1.0 / np.sqrt(self.in_channels))
nn.init.zeros_(self.input_layer.bias)
# 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: sp.SparseTensor,
t: torch.Tensor,
cond: Union[torch.Tensor, List[torch.Tensor], Dict[str, Union[torch.Tensor, sp.SparseTensor]], Tuple],
concat_cond: Optional[sp.SparseTensor] = None,
**kwargs
) -> sp.SparseTensor:
"""
Forward pass.
Args:
x: SparseTensor input
t: Timestep tensor [B]
cond: Conditioning tensor. For "cross" mode: list of tensors or tensor.
For "proj" mode: dict {'global': global_cond, 'proj': proj_cond}
or tuple of (global_cond, proj_cond)
concat_cond: Optional concatenation condition
Returns:
SparseTensor output
"""
if concat_cond is not None:
x = sp.sparse_cat([x, concat_cond], dim=-1)
h = self.input_layer(x)
h = manual_cast(h, self.dtype)
t_emb = self.t_embedder(t)
if self.share_mod:
t_emb = self.adaLN_modulation(t_emb)
t_emb = manual_cast(t_emb, self.dtype)
if self.pe_mode == "ape":
pe = self.pos_embedder(h.coords[:, 1:])
h = h + manual_cast(pe, self.dtype)
# Handle different conditioning modes
if self.image_attn_mode == 'proj':
if isinstance(cond, dict):
global_cond = cond['global']
proj_cond = cond['proj']
else:
global_cond, proj_cond = cond
if isinstance(global_cond, list):
global_cond = sp.VarLenTensor.from_tensor_list(global_cond)
global_cond = manual_cast(global_cond, self.dtype)
proj_cond = manual_cast(proj_cond, self.dtype)
cond = (global_cond, proj_cond)
elif self.image_attn_mode == 'gated_proj':
global_cond = cond['global']
if isinstance(global_cond, list):
global_cond = sp.VarLenTensor.from_tensor_list(global_cond)
global_cond = manual_cast(global_cond, self.dtype)
proj_semantic = manual_cast(cond['proj_semantic'], self.dtype)
proj_color = manual_cast(cond['proj_color'], self.dtype)
cond = {'global': global_cond, 'proj_semantic': proj_semantic, 'proj_color': proj_color}
else:
if isinstance(cond, list):
cond = sp.VarLenTensor.from_tensor_list(cond)
cond = manual_cast(cond, self.dtype)
for block in self.blocks:
h = block(h, t_emb, cond)
h = manual_cast(h, x.dtype)
h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:]))
h = self.out_layer(h)
return h
class ElasticSLatFlowModel(SparseTransformerElasticMixin, SLatFlowModel):
"""
SLat Flow Model with elastic memory management.
Used for training with low VRAM.
"""
pass
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