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# and Other Licenses of the Third-Party Components therein:
# The below Model in this distribution may have been modified by THL A29 Limited
# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
# The below software and/or models in this distribution may have been
# modified by THL A29 Limited ("Tencent Modifications").
# All Tencent Modifications are Copyright (C) THL A29 Limited.
# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
# except for the third-party components listed below.
# Hunyuan 3D does not impose any additional limitations beyond what is outlined
# in the repsective licenses of these third-party components.
# Users must comply with all terms and conditions of original licenses of these third-party
# components and must ensure that the usage of the third party components adheres to
# all relevant laws and regulations.
# For avoidance of doubts, Hunyuan 3D means the large language models and
# their software and algorithms, including trained model weights, parameters (including
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
# fine-tuning enabling code and other elements of the foregoing made publicly available
# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
import os
from typing import Union, List
import numpy as np
import torch
import torch.nn as nn
import yaml
from .attention_blocks import FourierEmbedder, Transformer, CrossAttentionDecoder, PointCrossAttentionEncoder
from .surface_extractors import MCSurfaceExtractor, SurfaceExtractors
from .volume_decoders import VanillaVolumeDecoder, FlashVDMVolumeDecoding, HierarchicalVolumeDecoding
from ...utils import logger, synchronize_timer, smart_load_model
class DiagonalGaussianDistribution(object):
def __init__(self, parameters: Union[torch.Tensor, List[torch.Tensor]], deterministic=False, feat_dim=1):
"""
Initialize a diagonal Gaussian distribution with mean and log-variance parameters.
Args:
parameters (Union[torch.Tensor, List[torch.Tensor]]):
Either a single tensor containing concatenated mean and log-variance along `feat_dim`,
or a list of two tensors [mean, logvar].
deterministic (bool, optional): If True, the distribution is deterministic (zero variance).
Default is False. feat_dim (int, optional): Dimension along which mean and logvar are
concatenated if parameters is a single tensor. Default is 1.
"""
self.feat_dim = feat_dim
self.parameters = parameters
if isinstance(parameters, list):
self.mean = parameters[0]
self.logvar = parameters[1]
else:
self.mean, self.logvar = torch.chunk(parameters, 2, dim=feat_dim)
self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
self.deterministic = deterministic
self.std = torch.exp(0.5 * self.logvar)
self.var = torch.exp(self.logvar)
if self.deterministic:
self.var = self.std = torch.zeros_like(self.mean)
def sample(self):
"""
Sample from the diagonal Gaussian distribution.
Returns:
torch.Tensor: A sample tensor with the same shape as the mean.
"""
x = self.mean + self.std * torch.randn_like(self.mean)
return x
def kl(self, other=None, dims=(1, 2, 3)):
"""
Compute the Kullback-Leibler (KL) divergence between this distribution and another.
If `other` is None, compute KL divergence to a standard normal distribution N(0, I).
Args:
other (DiagonalGaussianDistribution, optional): Another diagonal Gaussian distribution.
dims (tuple, optional): Dimensions along which to compute the mean KL divergence.
Default is (1, 2, 3).
Returns:
torch.Tensor: The mean KL divergence value.
"""
if self.deterministic:
return torch.Tensor([0.])
else:
if other is None:
return 0.5 * torch.mean(torch.pow(self.mean, 2)
+ self.var - 1.0 - self.logvar,
dim=dims)
else:
return 0.5 * torch.mean(
torch.pow(self.mean - other.mean, 2) / other.var
+ self.var / other.var - 1.0 - self.logvar + other.logvar,
dim=dims)
def nll(self, sample, dims=(1, 2, 3)):
if self.deterministic:
return torch.Tensor([0.])
logtwopi = np.log(2.0 * np.pi)
return 0.5 * torch.sum(
logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
dim=dims)
def mode(self):
return self.mean
class VectsetVAE(nn.Module):
@classmethod
@synchronize_timer('VectsetVAE Model Loading')
def from_single_file(
cls,
ckpt_path,
config_path,
device='cuda',
dtype=torch.float16,
use_safetensors=None,
**kwargs,
):
# load config
with open(config_path, 'r') as f:
config = yaml.safe_load(f)
# load ckpt
if use_safetensors:
ckpt_path = ckpt_path.replace('.ckpt', '.safetensors')
if not os.path.exists(ckpt_path):
raise FileNotFoundError(f"Model file {ckpt_path} not found")
logger.info(f"Loading model from {ckpt_path}")
if use_safetensors:
import safetensors.torch
ckpt = safetensors.torch.load_file(ckpt_path, device='cpu')
else:
ckpt = torch.load(ckpt_path, map_location='cpu', weights_only=True)
model_kwargs = config['params']
model_kwargs.update(kwargs)
model = cls(**model_kwargs)
model.load_state_dict(ckpt)
model.to(device=device, dtype=dtype)
return model
@classmethod
def from_pretrained(
cls,
model_path,
device='cuda',
dtype=torch.float16,
use_safetensors=False,
variant='fp16',
subfolder='hunyuan3d-vae-v2-1',
**kwargs,
):
config_path, ckpt_path = smart_load_model(
model_path,
subfolder=subfolder,
use_safetensors=use_safetensors,
variant=variant
)
return cls.from_single_file(
ckpt_path,
config_path,
device=device,
dtype=dtype,
use_safetensors=use_safetensors,
**kwargs
)
def init_from_ckpt(self, path, ignore_keys=()):
state_dict = torch.load(path, map_location="cpu")
state_dict = state_dict.get("state_dict", state_dict)
keys = list(state_dict.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del state_dict[k]
missing, unexpected = self.load_state_dict(state_dict, strict=False)
print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
if len(missing) > 0:
print(f"Missing Keys: {missing}")
print(f"Unexpected Keys: {unexpected}")
def __init__(
self,
volume_decoder=None,
surface_extractor=None
):
super().__init__()
if volume_decoder is None:
volume_decoder = VanillaVolumeDecoder()
if surface_extractor is None:
surface_extractor = MCSurfaceExtractor()
self.volume_decoder = volume_decoder
self.surface_extractor = surface_extractor
def latents2mesh(self, latents: torch.FloatTensor, **kwargs):
with synchronize_timer('Volume decoding'):
grid_logits = self.volume_decoder(latents, self.geo_decoder, **kwargs)
with synchronize_timer('Surface extraction'):
outputs = self.surface_extractor(grid_logits, **kwargs)
return outputs
def enable_flashvdm_decoder(
self,
enabled: bool = True,
adaptive_kv_selection=True,
topk_mode='mean',
mc_algo='dmc',
):
if enabled:
if adaptive_kv_selection:
self.volume_decoder = FlashVDMVolumeDecoding(topk_mode)
else:
self.volume_decoder = HierarchicalVolumeDecoding()
if mc_algo not in SurfaceExtractors.keys():
raise ValueError(f'Unsupported mc_algo {mc_algo}, available:{list(SurfaceExtractors.keys())}')
self.surface_extractor = SurfaceExtractors[mc_algo]()
else:
self.volume_decoder = VanillaVolumeDecoder()
self.surface_extractor = MCSurfaceExtractor()
class ShapeVAE(VectsetVAE):
def __init__(
self,
*,
num_latents: int,
embed_dim: int,
width: int,
heads: int,
num_decoder_layers: int,
num_encoder_layers: int = 8,
pc_size: int = 5120,
pc_sharpedge_size: int = 5120,
point_feats: int = 3,
downsample_ratio: int = 20,
geo_decoder_downsample_ratio: int = 1,
geo_decoder_mlp_expand_ratio: int = 4,
geo_decoder_ln_post: bool = True,
num_freqs: int = 8,
include_pi: bool = True,
qkv_bias: bool = True,
qk_norm: bool = False,
label_type: str = "binary",
drop_path_rate: float = 0.0,
scale_factor: float = 1.0,
use_ln_post: bool = True,
ckpt_path = None
):
super().__init__()
self.geo_decoder_ln_post = geo_decoder_ln_post
self.downsample_ratio = downsample_ratio
self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi)
self.encoder = PointCrossAttentionEncoder(
fourier_embedder=self.fourier_embedder,
num_latents=num_latents,
downsample_ratio=self.downsample_ratio,
pc_size=pc_size,
pc_sharpedge_size=pc_sharpedge_size,
point_feats=point_feats,
width=width,
heads=heads,
layers=num_encoder_layers,
qkv_bias=qkv_bias,
use_ln_post=use_ln_post,
qk_norm=qk_norm
)
self.pre_kl = nn.Linear(width, embed_dim * 2)
self.post_kl = nn.Linear(embed_dim, width)
self.transformer = Transformer(
n_ctx=num_latents,
width=width,
layers=num_decoder_layers,
heads=heads,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
drop_path_rate=drop_path_rate
)
self.geo_decoder = CrossAttentionDecoder(
fourier_embedder=self.fourier_embedder,
out_channels=1,
num_latents=num_latents,
mlp_expand_ratio=geo_decoder_mlp_expand_ratio,
downsample_ratio=geo_decoder_downsample_ratio,
enable_ln_post=self.geo_decoder_ln_post,
width=width // geo_decoder_downsample_ratio,
heads=heads // geo_decoder_downsample_ratio,
qkv_bias=qkv_bias,
qk_norm=qk_norm,
label_type=label_type,
)
self.scale_factor = scale_factor
self.latent_shape = (num_latents, embed_dim)
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path)
def forward(self, latents):
latents = self.post_kl(latents)
latents = self.transformer(latents)
return latents
def encode(self, surface, sample_posterior=True):
pc, feats = surface[:, :, :3], surface[:, :, 3:]
latents, _ = self.encoder(pc, feats)
# print(latents.shape, self.pre_kl.weight.shape)
moments = self.pre_kl(latents)
posterior = DiagonalGaussianDistribution(moments, feat_dim=-1)
if sample_posterior:
latents = posterior.sample()
else:
latents = posterior.mode()
return latents
def decode(self, latents):
latents = self.post_kl(latents)
latents = self.transformer(latents)
return latents
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