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from typing import *
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from ..modules.utils import zero_module, convert_module_to_f16, convert_module_to_f32
from ..modules.transformer import AbsolutePositionEmbedder
from ..modules.norm import LayerNorm32
from ..modules import sparse as sp
from ..modules.sparse.transformer import ModulatedSparseTransformerCrossBlock
from .sparse_structure_flow import TimestepEmbedder
class SparseResBlock3d(nn.Module):
def __init__(
self,
channels: int,
emb_channels: int,
out_channels: Optional[int] = None,
downsample: bool = False,
upsample: bool = False,
):
super().__init__()
self.channels = channels
self.emb_channels = emb_channels
self.out_channels = out_channels or channels
self.downsample = downsample
self.upsample = upsample
assert not (downsample and upsample), "Cannot downsample and upsample at the same time"
self.norm1 = LayerNorm32(channels, elementwise_affine=True, eps=1e-6)
self.norm2 = LayerNorm32(self.out_channels, elementwise_affine=False, eps=1e-6)
self.conv1 = sp.SparseConv3d(channels, self.out_channels, 3)
self.conv2 = zero_module(sp.SparseConv3d(self.out_channels, self.out_channels, 3))
self.emb_layers = nn.Sequential(
nn.SiLU(),
nn.Linear(emb_channels, 2 * self.out_channels, bias=True),
)
self.skip_connection = sp.SparseLinear(channels, self.out_channels) if channels != self.out_channels else nn.Identity()
self.updown = None
if self.downsample:
self.updown = sp.SparseDownsample(2)
elif self.upsample:
self.updown = sp.SparseUpsample(2)
def _updown(self, x: sp.SparseTensor) -> sp.SparseTensor:
if self.updown is not None:
x = self.updown(x)
return x
def forward(self, x: sp.SparseTensor, emb: torch.Tensor) -> sp.SparseTensor:
emb_out = self.emb_layers(emb).type(x.dtype)
scale, shift = torch.chunk(emb_out, 2, dim=1)
x = self._updown(x)
h = x.replace(self.norm1(x.feats))
h = h.replace(F.silu(h.feats))
h = self.conv1(h)
h = h.replace(self.norm2(h.feats)) * (1 + scale) + shift
h = h.replace(F.silu(h.feats))
h = self.conv2(h)
h = h + self.skip_connection(x)
return h
class SLatFlowModel(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,
num_io_res_blocks: int = 2,
io_block_channels: List[int] = None,
pe_mode: Literal["ape", "rope"] = "ape",
use_fp16: bool = False,
use_checkpoint: bool = False,
use_skip_connection: bool = True,
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.num_io_res_blocks = num_io_res_blocks
self.io_block_channels = io_block_channels
self.pe_mode = pe_mode
self.use_fp16 = use_fp16
self.use_checkpoint = use_checkpoint
self.use_skip_connection = use_skip_connection
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
assert int(np.log2(patch_size)) == np.log2(patch_size), "Patch size must be a power of 2"
assert np.log2(patch_size) == len(io_block_channels), "Number of IO ResBlocks must match the number of stages"
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, io_block_channels[0])
self.input_blocks = nn.ModuleList([])
for chs, next_chs in zip(io_block_channels, io_block_channels[1:] + [model_channels]):
self.input_blocks.extend([
SparseResBlock3d(
chs,
model_channels,
out_channels=chs,
)
for _ in range(num_io_res_blocks-1)
])
self.input_blocks.append(
SparseResBlock3d(
chs,
model_channels,
out_channels=next_chs,
downsample=True,
)
)
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"),
share_mod=self.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_blocks = nn.ModuleList([])
for chs, prev_chs in zip(reversed(io_block_channels), [model_channels] + list(reversed(io_block_channels[1:]))):
self.out_blocks.append(
SparseResBlock3d(
prev_chs * 2 if self.use_skip_connection else prev_chs,
model_channels,
out_channels=chs,
upsample=True,
)
)
self.out_blocks.extend([
SparseResBlock3d(
chs * 2 if self.use_skip_connection else chs,
model_channels,
out_channels=chs,
)
for _ in range(num_io_res_blocks-1)
])
self.out_layer = sp.SparseLinear(io_block_channels[0], out_channels)
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.input_blocks.apply(convert_module_to_f16)
self.blocks.apply(convert_module_to_f16)
self.out_blocks.apply(convert_module_to_f16)
def convert_to_fp32(self) -> None:
"""
Convert the torso of the model to float32.
"""
self.input_blocks.apply(convert_module_to_f32)
self.blocks.apply(convert_module_to_f32)
self.out_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: sp.SparseTensor, t: torch.Tensor, cond: torch.Tensor) -> sp.SparseTensor:
h = self.input_layer(x).type(self.dtype)
t_emb = self.t_embedder(t)
if self.share_mod:
t_emb = self.adaLN_modulation(t_emb)
t_emb = t_emb.type(self.dtype)
cond = cond.type(self.dtype)
skips = []
# pack with input blocks
for block in self.input_blocks:
h = block(h, t_emb)
skips.append(h.feats)
if self.pe_mode == "ape":
h = h + self.pos_embedder(h.coords[:, 1:]).type(self.dtype)
for block in self.blocks:
h = block(h, t_emb, cond)
# unpack with output blocks
for block, skip in zip(self.out_blocks, reversed(skips)):
if self.use_skip_connection:
h = block(h.replace(torch.cat([h.feats, skip], dim=1)), t_emb)
else:
h = block(h, t_emb)
h = h.replace(F.layer_norm(h.feats, h.feats.shape[-1:]))
h = self.out_layer(h.type(x.dtype))
return h
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