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# 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
import torch
import torch.nn as nn
import yaml
from .attention_blocks import FourierEmbedder, Transformer, CrossAttentionDecoder
from .surface_extractors import MCSurfaceExtractor, SurfaceExtractors
from .volume_decoders import VanillaVolumeDecoder, FlashVDMVolumeDecoding, HierarchicalVolumeDecoding
from ...utils import logger, synchronize_timer, smart_load_model
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=True,
variant='fp16',
subfolder='hunyuan3d-vae-v2-0',
**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__(
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,
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,
):
super().__init__()
self.geo_decoder_ln_post = geo_decoder_ln_post
self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi)
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)
def forward(self, latents):
latents = self.post_kl(latents)
latents = self.transformer(latents)
return latents
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