Add FlashVDM vae

...and it earned that name, whoa!

https://github.com/Tencent/FlashVDM/

hy3dgen/shapegen/models/__init__.py

@@ -25,4 +25,4 @@
 
 from .conditioner import DualImageEncoder, SingleImageEncoder, DinoImageEncoder, CLIPImageEncoder
 from .hunyuan3ddit import Hunyuan3DDiT
-from .vae import ShapeVAE
+from .autoencoders import ShapeVAE

hy3dgen/shapegen/models/autoencoders/__init__.py

@@ -0,0 +1,20 @@
+# 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.
+
+from .attention_blocks import CrossAttentionDecoder
+from .attention_processors import FlashVDMCrossAttentionProcessor, CrossAttentionProcessor, \
+    FlashVDMTopMCrossAttentionProcessor
+from .model import ShapeVAE, VectsetVAE
+from .surface_extractors import SurfaceExtractors, MCSurfaceExtractor, DMCSurfaceExtractor, Latent2MeshOutput
+from .volume_decoders import HierarchicalVolumeDecoding, FlashVDMVolumeDecoding, VanillaVolumeDecoder

hy3dgen/shapegen/models/autoencoders/attention_blocks.py

@@ -0,0 +1,493 @@
+# 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 Optional
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+
+from .attention_processors import CrossAttentionProcessor
+from ...utils import logger
+
+scaled_dot_product_attention = nn.functional.scaled_dot_product_attention
+
+if os.environ.get('USE_SAGEATTN', '0') == '1':
+    try:
+        from sageattention import sageattn
+    except ImportError:
+        raise ImportError('Please install the package "sageattention" to use this USE_SAGEATTN.')
+    scaled_dot_product_attention = sageattn
+
+
+class FourierEmbedder(nn.Module):
+    """The sin/cosine positional embedding. Given an input tensor `x` of shape [n_batch, ..., c_dim], it converts
+    each feature dimension of `x[..., i]` into:
+        [
+            sin(x[..., i]),
+            sin(f_1*x[..., i]),
+            sin(f_2*x[..., i]),
+            ...
+            sin(f_N * x[..., i]),
+            cos(x[..., i]),
+            cos(f_1*x[..., i]),
+            cos(f_2*x[..., i]),
+            ...
+            cos(f_N * x[..., i]),
+            x[..., i]     # only present if include_input is True.
+        ], here f_i is the frequency.
+
+    Denote the space is [0 / num_freqs, 1 / num_freqs, 2 / num_freqs, 3 / num_freqs, ..., (num_freqs - 1) / num_freqs].
+    If logspace is True, then the frequency f_i is [2^(0 / num_freqs), ..., 2^(i / num_freqs), ...];
+    Otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)].
+
+    Args:
+        num_freqs (int): the number of frequencies, default is 6;
+        logspace (bool): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...],
+            otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)];
+        input_dim (int): the input dimension, default is 3;
+        include_input (bool): include the input tensor or not, default is True.
+
+    Attributes:
+        frequencies (torch.Tensor): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...],
+                otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1);
+
+        out_dim (int): the embedding size, if include_input is True, it is input_dim * (num_freqs * 2 + 1),
+            otherwise, it is input_dim * num_freqs * 2.
+
+    """
+
+    def __init__(self,
+                 num_freqs: int = 6,
+                 logspace: bool = True,
+                 input_dim: int = 3,
+                 include_input: bool = True,
+                 include_pi: bool = True) -> None:
+
+        """The initialization"""
+
+        super().__init__()
+
+        if logspace:
+            frequencies = 2.0 ** torch.arange(
+                num_freqs,
+                dtype=torch.float32
+            )
+        else:
+            frequencies = torch.linspace(
+                1.0,
+                2.0 ** (num_freqs - 1),
+                num_freqs,
+                dtype=torch.float32
+            )
+
+        if include_pi:
+            frequencies *= torch.pi
+
+        self.register_buffer("frequencies", frequencies, persistent=False)
+        self.include_input = include_input
+        self.num_freqs = num_freqs
+
+        self.out_dim = self.get_dims(input_dim)
+
+    def get_dims(self, input_dim):
+        temp = 1 if self.include_input or self.num_freqs == 0 else 0
+        out_dim = input_dim * (self.num_freqs * 2 + temp)
+
+        return out_dim
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """ Forward process.
+
+        Args:
+            x: tensor of shape [..., dim]
+
+        Returns:
+            embedding: an embedding of `x` of shape [..., dim * (num_freqs * 2 + temp)]
+                where temp is 1 if include_input is True and 0 otherwise.
+        """
+
+        if self.num_freqs > 0:
+            embed = (x[..., None].contiguous() * self.frequencies).view(*x.shape[:-1], -1)
+            if self.include_input:
+                return torch.cat((x, embed.sin(), embed.cos()), dim=-1)
+            else:
+                return torch.cat((embed.sin(), embed.cos()), dim=-1)
+        else:
+            return x
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+
+    def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+        self.scale_by_keep = scale_by_keep
+
+    def forward(self, x):
+        """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+
+        This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+        the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+        See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+        changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+        'survival rate' as the argument.
+
+        """
+        if self.drop_prob == 0. or not self.training:
+            return x
+        keep_prob = 1 - self.drop_prob
+        shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+        random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
+        if keep_prob > 0.0 and self.scale_by_keep:
+            random_tensor.div_(keep_prob)
+        return x * random_tensor
+
+    def extra_repr(self):
+        return f'drop_prob={round(self.drop_prob, 3):0.3f}'
+
+
+class MLP(nn.Module):
+    def __init__(
+        self, *,
+        width: int,
+        expand_ratio: int = 4,
+        output_width: int = None,
+        drop_path_rate: float = 0.0
+    ):
+        super().__init__()
+        self.width = width
+        self.c_fc = nn.Linear(width, width * expand_ratio)
+        self.c_proj = nn.Linear(width * expand_ratio, output_width if output_width is not None else width)
+        self.gelu = nn.GELU()
+        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+
+    def forward(self, x):
+        return self.drop_path(self.c_proj(self.gelu(self.c_fc(x))))
+
+
+class QKVMultiheadCrossAttention(nn.Module):
+    def __init__(
+        self,
+        *,
+        heads: int,
+        n_data: Optional[int] = None,
+        width=None,
+        qk_norm=False,
+        norm_layer=nn.LayerNorm
+    ):
+        super().__init__()
+        self.heads = heads
+        self.n_data = n_data
+        self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
+
+        self.attn_processor = CrossAttentionProcessor()
+
+    def forward(self, q, kv):
+        _, n_ctx, _ = q.shape
+        bs, n_data, width = kv.shape
+        attn_ch = width // self.heads // 2
+        q = q.view(bs, n_ctx, self.heads, -1)
+        kv = kv.view(bs, n_data, self.heads, -1)
+        k, v = torch.split(kv, attn_ch, dim=-1)
+
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+        q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v))
+        out = self.attn_processor(self, q, k, v)
+        out = out.transpose(1, 2).reshape(bs, n_ctx, -1)
+        return out
+
+
+class MultiheadCrossAttention(nn.Module):
+    def __init__(
+        self,
+        *,
+        width: int,
+        heads: int,
+        qkv_bias: bool = True,
+        n_data: Optional[int] = None,
+        data_width: Optional[int] = None,
+        norm_layer=nn.LayerNorm,
+        qk_norm: bool = False,
+        kv_cache: bool = False,
+    ):
+        super().__init__()
+        self.n_data = n_data
+        self.width = width
+        self.heads = heads
+        self.data_width = width if data_width is None else data_width
+        self.c_q = nn.Linear(width, width, bias=qkv_bias)
+        self.c_kv = nn.Linear(self.data_width, width * 2, bias=qkv_bias)
+        self.c_proj = nn.Linear(width, width)
+        self.attention = QKVMultiheadCrossAttention(
+            heads=heads,
+            n_data=n_data,
+            width=width,
+            norm_layer=norm_layer,
+            qk_norm=qk_norm
+        )
+        self.kv_cache = kv_cache
+        self.data = None
+
+    def forward(self, x, data):
+        x = self.c_q(x)
+        if self.kv_cache:
+            if self.data is None:
+                self.data = self.c_kv(data)
+                logger.info('Save kv cache,this should be called only once for one mesh')
+            data = self.data
+        else:
+            data = self.c_kv(data)
+        x = self.attention(x, data)
+        x = self.c_proj(x)
+        return x
+
+
+class ResidualCrossAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        n_data: Optional[int] = None,
+        width: int,
+        heads: int,
+        mlp_expand_ratio: int = 4,
+        data_width: Optional[int] = None,
+        qkv_bias: bool = True,
+        norm_layer=nn.LayerNorm,
+        qk_norm: bool = False
+    ):
+        super().__init__()
+
+        if data_width is None:
+            data_width = width
+
+        self.attn = MultiheadCrossAttention(
+            n_data=n_data,
+            width=width,
+            heads=heads,
+            data_width=data_width,
+            qkv_bias=qkv_bias,
+            norm_layer=norm_layer,
+            qk_norm=qk_norm
+        )
+        self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6)
+        self.ln_2 = norm_layer(data_width, elementwise_affine=True, eps=1e-6)
+        self.ln_3 = norm_layer(width, elementwise_affine=True, eps=1e-6)
+        self.mlp = MLP(width=width, expand_ratio=mlp_expand_ratio)
+
+    def forward(self, x: torch.Tensor, data: torch.Tensor):
+        x = x + self.attn(self.ln_1(x), self.ln_2(data))
+        x = x + self.mlp(self.ln_3(x))
+        return x
+
+
+class QKVMultiheadAttention(nn.Module):
+    def __init__(
+        self,
+        *,
+        heads: int,
+        n_ctx: int,
+        width=None,
+        qk_norm=False,
+        norm_layer=nn.LayerNorm
+    ):
+        super().__init__()
+        self.heads = heads
+        self.n_ctx = n_ctx
+        self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
+        self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity()
+
+    def forward(self, qkv):
+        bs, n_ctx, width = qkv.shape
+        attn_ch = width // self.heads // 3
+        qkv = qkv.view(bs, n_ctx, self.heads, -1)
+        q, k, v = torch.split(qkv, attn_ch, dim=-1)
+
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v))
+        out = scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1)
+        return out
+
+
+class MultiheadAttention(nn.Module):
+    def __init__(
+        self,
+        *,
+        n_ctx: int,
+        width: int,
+        heads: int,
+        qkv_bias: bool,
+        norm_layer=nn.LayerNorm,
+        qk_norm: bool = False,
+        drop_path_rate: float = 0.0
+    ):
+        super().__init__()
+        self.n_ctx = n_ctx
+        self.width = width
+        self.heads = heads
+        self.c_qkv = nn.Linear(width, width * 3, bias=qkv_bias)
+        self.c_proj = nn.Linear(width, width)
+        self.attention = QKVMultiheadAttention(
+            heads=heads,
+            n_ctx=n_ctx,
+            width=width,
+            norm_layer=norm_layer,
+            qk_norm=qk_norm
+        )
+        self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity()
+
+    def forward(self, x):
+        x = self.c_qkv(x)
+        x = self.attention(x)
+        x = self.drop_path(self.c_proj(x))
+        return x
+
+
+class ResidualAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        *,
+        n_ctx: int,
+        width: int,
+        heads: int,
+        qkv_bias: bool = True,
+        norm_layer=nn.LayerNorm,
+        qk_norm: bool = False,
+        drop_path_rate: float = 0.0,
+    ):
+        super().__init__()
+        self.attn = MultiheadAttention(
+            n_ctx=n_ctx,
+            width=width,
+            heads=heads,
+            qkv_bias=qkv_bias,
+            norm_layer=norm_layer,
+            qk_norm=qk_norm,
+            drop_path_rate=drop_path_rate
+        )
+        self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6)
+        self.mlp = MLP(width=width, drop_path_rate=drop_path_rate)
+        self.ln_2 = norm_layer(width, elementwise_affine=True, eps=1e-6)
+
+    def forward(self, x: torch.Tensor):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        *,
+        n_ctx: int,
+        width: int,
+        layers: int,
+        heads: int,
+        qkv_bias: bool = True,
+        norm_layer=nn.LayerNorm,
+        qk_norm: bool = False,
+        drop_path_rate: float = 0.0
+    ):
+        super().__init__()
+        self.n_ctx = n_ctx
+        self.width = width
+        self.layers = layers
+        self.resblocks = nn.ModuleList(
+            [
+                ResidualAttentionBlock(
+                    n_ctx=n_ctx,
+                    width=width,
+                    heads=heads,
+                    qkv_bias=qkv_bias,
+                    norm_layer=norm_layer,
+                    qk_norm=qk_norm,
+                    drop_path_rate=drop_path_rate
+                )
+                for _ in range(layers)
+            ]
+        )
+
+    def forward(self, x: torch.Tensor):
+        for block in self.resblocks:
+            x = block(x)
+        return x
+
+
+class CrossAttentionDecoder(nn.Module):
+
+    def __init__(
+        self,
+        *,
+        num_latents: int,
+        out_channels: int,
+        fourier_embedder: FourierEmbedder,
+        width: int,
+        heads: int,
+        mlp_expand_ratio: int = 4,
+        downsample_ratio: int = 1,
+        enable_ln_post: bool = True,
+        qkv_bias: bool = True,
+        qk_norm: bool = False,
+        label_type: str = "binary"
+    ):
+        super().__init__()
+
+        self.enable_ln_post = enable_ln_post
+        self.fourier_embedder = fourier_embedder
+        self.downsample_ratio = downsample_ratio
+        self.query_proj = nn.Linear(self.fourier_embedder.out_dim, width)
+        if self.downsample_ratio != 1:
+            self.latents_proj = nn.Linear(width * downsample_ratio, width)
+        if self.enable_ln_post == False:
+            qk_norm = False
+        self.cross_attn_decoder = ResidualCrossAttentionBlock(
+            n_data=num_latents,
+            width=width,
+            mlp_expand_ratio=mlp_expand_ratio,
+            heads=heads,
+            qkv_bias=qkv_bias,
+            qk_norm=qk_norm
+        )
+
+        if self.enable_ln_post:
+            self.ln_post = nn.LayerNorm(width)
+        self.output_proj = nn.Linear(width, out_channels)
+        self.label_type = label_type
+        self.count = 0
+
+    def set_cross_attention_processor(self, processor):
+        self.cross_attn_decoder.attn.attention.attn_processor = processor
+
+    def set_default_cross_attention_processor(self):
+        self.cross_attn_decoder.attn.attention.attn_processor = CrossAttentionProcessor
+
+    def forward(self, queries=None, query_embeddings=None, latents=None):
+        if query_embeddings is None:
+            query_embeddings = self.query_proj(self.fourier_embedder(queries).to(latents.dtype))
+        self.count += query_embeddings.shape[1]
+        if self.downsample_ratio != 1:
+            latents = self.latents_proj(latents)
+        x = self.cross_attn_decoder(query_embeddings, latents)
+        if self.enable_ln_post:
+            x = self.ln_post(x)
+        occ = self.output_proj(x)
+        return occ

hy3dgen/shapegen/models/autoencoders/attention_processors.py

@@ -0,0 +1,96 @@
+# 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.functional as F
+
+scaled_dot_product_attention = F.scaled_dot_product_attention
+if os.environ.get('CA_USE_SAGEATTN', '0') == '1':
+    try:
+        from sageattention import sageattn
+    except ImportError:
+        raise ImportError('Please install the package "sageattention" to use this USE_SAGEATTN.')
+    scaled_dot_product_attention = sageattn
+
+
+class CrossAttentionProcessor:
+    def __call__(self, attn, q, k, v):
+        out = scaled_dot_product_attention(q, k, v)
+        return out
+
+
+class FlashVDMCrossAttentionProcessor:
+    def __init__(self, topk=None):
+        self.topk = topk
+
+    def __call__(self, attn, q, k, v):
+        if k.shape[-2] == 3072:
+            topk = 1024
+        elif k.shape[-2] == 512:
+            topk = 256
+        else:
+            topk = k.shape[-2] // 3
+
+        if self.topk is True:
+            q1 = q[:, :, ::100, :]
+            sim = q1 @ k.transpose(-1, -2)
+            sim = torch.mean(sim, -2)
+            topk_ind = torch.topk(sim, dim=-1, k=topk).indices.squeeze(-2).unsqueeze(-1)
+            topk_ind = topk_ind.expand(-1, -1, -1, v.shape[-1])
+            v0 = torch.gather(v, dim=-2, index=topk_ind)
+            k0 = torch.gather(k, dim=-2, index=topk_ind)
+            out = scaled_dot_product_attention(q, k0, v0)
+        elif self.topk is False:
+            out = scaled_dot_product_attention(q, k, v)
+        else:
+            idx, counts = self.topk
+            start = 0
+            outs = []
+            for grid_coord, count in zip(idx, counts):
+                end = start + count
+                q_chunk = q[:, :, start:end, :]
+                k0, v0 = self.select_topkv(q_chunk, k, v, topk)
+                out = scaled_dot_product_attention(q_chunk, k0, v0)
+                outs.append(out)
+                start += count
+            out = torch.cat(outs, dim=-2)
+        self.topk = False
+        return out
+
+    def select_topkv(self, q_chunk, k, v, topk):
+        q1 = q_chunk[:, :, ::50, :]
+        sim = q1 @ k.transpose(-1, -2)
+        sim = torch.mean(sim, -2)
+        topk_ind = torch.topk(sim, dim=-1, k=topk).indices.squeeze(-2).unsqueeze(-1)
+        topk_ind = topk_ind.expand(-1, -1, -1, v.shape[-1])
+        v0 = torch.gather(v, dim=-2, index=topk_ind)
+        k0 = torch.gather(k, dim=-2, index=topk_ind)
+        return k0, v0
+
+
+class FlashVDMTopMCrossAttentionProcessor(FlashVDMCrossAttentionProcessor):
+    def select_topkv(self, q_chunk, k, v, topk):
+        q1 = q_chunk[:, :, ::30, :]
+        sim = q1 @ k.transpose(-1, -2)
+        # sim = sim.to(torch.float32)
+        sim = sim.softmax(-1)
+        sim = torch.mean(sim, 1)
+        activated_token = torch.where(sim > 1e-6)[2]
+        index = torch.unique(activated_token, return_counts=True)[0].unsqueeze(0).unsqueeze(0).unsqueeze(-1)
+        index = index.expand(-1, v.shape[1], -1, v.shape[-1])
+        v0 = torch.gather(v, dim=-2, index=index)
+        k0 = torch.gather(k, dim=-2, index=index)
+        return k0, v0

hy3dgen/shapegen/models/autoencoders/model.py

@@ -0,0 +1,189 @@
+# 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

hy3dgen/shapegen/models/autoencoders/surface_extractors.py

@@ -0,0 +1,100 @@
+# 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.
+
+from typing import Union, Tuple, List
+
+import numpy as np
+import torch
+from skimage import measure
+
+
+class Latent2MeshOutput:
+
+    def __init__(self, mesh_v=None, mesh_f=None):
+        self.mesh_v = mesh_v
+        self.mesh_f = mesh_f
+
+
+def center_vertices(vertices):
+    """Translate the vertices so that bounding box is centered at zero."""
+    vert_min = vertices.min(dim=0)[0]
+    vert_max = vertices.max(dim=0)[0]
+    vert_center = 0.5 * (vert_min + vert_max)
+    return vertices - vert_center
+
+
+class SurfaceExtractor:
+    def _compute_box_stat(self, bounds: Union[Tuple[float], List[float], float], octree_resolution: int):
+        if isinstance(bounds, float):
+            bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
+
+        bbox_min, bbox_max = np.array(bounds[0:3]), np.array(bounds[3:6])
+        bbox_size = bbox_max - bbox_min
+        grid_size = [int(octree_resolution) + 1, int(octree_resolution) + 1, int(octree_resolution) + 1]
+        return grid_size, bbox_min, bbox_size
+
+    def run(self, *args, **kwargs):
+        return NotImplementedError
+
+    def __call__(self, grid_logits, **kwargs):
+        outputs = []
+        for i in range(grid_logits.shape[0]):
+            try:
+                vertices, faces = self.run(grid_logits[i], **kwargs)
+                vertices = vertices.astype(np.float32)
+                faces = np.ascontiguousarray(faces)
+                outputs.append(Latent2MeshOutput(mesh_v=vertices, mesh_f=faces))
+
+            except Exception:
+                import traceback
+                traceback.print_exc()
+                outputs.append(None)
+
+        return outputs
+
+
+class MCSurfaceExtractor(SurfaceExtractor):
+    def run(self, grid_logit, *, mc_level, bounds, octree_resolution, **kwargs):
+        vertices, faces, normals, _ = measure.marching_cubes(
+            grid_logit.cpu().numpy(),
+            mc_level,
+            method="lewiner"
+        )
+        grid_size, bbox_min, bbox_size = self._compute_box_stat(bounds, octree_resolution)
+        vertices = vertices / grid_size * bbox_size + bbox_min
+        return vertices, faces
+
+
+class DMCSurfaceExtractor(SurfaceExtractor):
+    def run(self, grid_logit, *, octree_resolution, **kwargs):
+        device = grid_logit.device
+        if not hasattr(self, 'dmc'):
+            try:
+                from diso import DiffDMC
+            except:
+                raise ImportError("Please install diso via `pip install diso`, or set mc_algo to 'mc'")
+            self.dmc = DiffDMC(dtype=torch.float32).to(device)
+        sdf = -grid_logit / octree_resolution
+        sdf = sdf.to(torch.float32).contiguous()
+        verts, faces = self.dmc(sdf, deform=None, return_quads=False, normalize=True)
+        verts = center_vertices(verts)
+        vertices = verts.detach().cpu().numpy()
+        faces = faces.detach().cpu().numpy()[:, ::-1]
+        return vertices, faces
+
+
+SurfaceExtractors = {
+    'mc': MCSurfaceExtractor,
+    'dmc': DMCSurfaceExtractor,
+}

hy3dgen/shapegen/models/autoencoders/volume_decoders.py

@@ -0,0 +1,435 @@
+# 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.
+
+from typing import Union, Tuple, List, Callable
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import repeat
+from tqdm import tqdm
+
+from .attention_blocks import CrossAttentionDecoder
+from .attention_processors import FlashVDMCrossAttentionProcessor, FlashVDMTopMCrossAttentionProcessor
+from ...utils import logger
+
+
+def extract_near_surface_volume_fn(input_tensor: torch.Tensor, alpha: float):
+    device = input_tensor.device
+    D = input_tensor.shape[0]
+    signed_val = 0.0
+
+    # 添加偏移并处理无效值
+    val = input_tensor + alpha
+    valid_mask = val > -9000  # 假设-9000是无效值
+
+    # 改进的邻居获取函数（保持维度一致）
+    def get_neighbor(t, shift, axis):
+        """根据指定轴进行位移并保持维度一致"""
+        if shift == 0:
+            return t.clone()
+
+        # 确定填充轴（输入为[D, D, D]对应z,y,x轴）
+        pad_dims = [0, 0, 0, 0, 0, 0]  # 格式：[x前，x后，y前，y后，z前，z后]
+
+        # 根据轴类型设置填充
+        if axis == 0:  # x轴（最后一个维度）
+            pad_idx = 0 if shift > 0 else 1
+            pad_dims[pad_idx] = abs(shift)
+        elif axis == 1:  # y轴（中间维度）
+            pad_idx = 2 if shift > 0 else 3
+            pad_dims[pad_idx] = abs(shift)
+        elif axis == 2:  # z轴（第一个维度）
+            pad_idx = 4 if shift > 0 else 5
+            pad_dims[pad_idx] = abs(shift)
+
+        # 执行填充（添加batch和channel维度适配F.pad）
+        padded = F.pad(t.unsqueeze(0).unsqueeze(0), pad_dims[::-1], mode='replicate')  # 反转顺序适配F.pad
+
+        # 构建动态切片索引
+        slice_dims = [slice(None)] * 3  # 初始化为全切片
+        if axis == 0:  # x轴（dim=2）
+            if shift > 0:
+                slice_dims[0] = slice(shift, None)
+            else:
+                slice_dims[0] = slice(None, shift)
+        elif axis == 1:  # y轴（dim=1）
+            if shift > 0:
+                slice_dims[1] = slice(shift, None)
+            else:
+                slice_dims[1] = slice(None, shift)
+        elif axis == 2:  # z轴（dim=0）
+            if shift > 0:
+                slice_dims[2] = slice(shift, None)
+            else:
+                slice_dims[2] = slice(None, shift)
+
+        # 应用切片并恢复维度
+        padded = padded.squeeze(0).squeeze(0)
+        sliced = padded[slice_dims]
+        return sliced
+
+    # 获取各方向邻居（确保维度一致）
+    left = get_neighbor(val, 1, axis=0)  # x方向
+    right = get_neighbor(val, -1, axis=0)
+    back = get_neighbor(val, 1, axis=1)  # y方向
+    front = get_neighbor(val, -1, axis=1)
+    down = get_neighbor(val, 1, axis=2)  # z方向
+    up = get_neighbor(val, -1, axis=2)
+
+    # 处理边界无效值（使用where保持维度一致）
+    def safe_where(neighbor):
+        return torch.where(neighbor > -9000, neighbor, val)
+
+    left = safe_where(left)
+    right = safe_where(right)
+    back = safe_where(back)
+    front = safe_where(front)
+    down = safe_where(down)
+    up = safe_where(up)
+
+    # 计算符号一致性（转换为float32确保精度）
+    sign = torch.sign(val.to(torch.float32))
+    neighbors_sign = torch.stack([
+        torch.sign(left.to(torch.float32)),
+        torch.sign(right.to(torch.float32)),
+        torch.sign(back.to(torch.float32)),
+        torch.sign(front.to(torch.float32)),
+        torch.sign(down.to(torch.float32)),
+        torch.sign(up.to(torch.float32))
+    ], dim=0)
+
+    # 检查所有符号是否一致
+    same_sign = torch.all(neighbors_sign == sign, dim=0)
+
+    # 生成最终掩码
+    mask = (~same_sign).to(torch.int32)
+    return mask * valid_mask.to(torch.int32)
+
+
+def generate_dense_grid_points(
+    bbox_min: np.ndarray,
+    bbox_max: np.ndarray,
+    octree_resolution: int,
+    indexing: str = "ij",
+):
+    length = bbox_max - bbox_min
+    num_cells = octree_resolution
+
+    x = np.linspace(bbox_min[0], bbox_max[0], int(num_cells) + 1, dtype=np.float32)
+    y = np.linspace(bbox_min[1], bbox_max[1], int(num_cells) + 1, dtype=np.float32)
+    z = np.linspace(bbox_min[2], bbox_max[2], int(num_cells) + 1, dtype=np.float32)
+    [xs, ys, zs] = np.meshgrid(x, y, z, indexing=indexing)
+    xyz = np.stack((xs, ys, zs), axis=-1)
+    grid_size = [int(num_cells) + 1, int(num_cells) + 1, int(num_cells) + 1]
+
+    return xyz, grid_size, length
+
+
+class VanillaVolumeDecoder:
+    @torch.no_grad()
+    def __call__(
+        self,
+        latents: torch.FloatTensor,
+        geo_decoder: Callable,
+        bounds: Union[Tuple[float], List[float], float] = 1.01,
+        num_chunks: int = 10000,
+        octree_resolution: int = None,
+        enable_pbar: bool = True,
+        **kwargs,
+    ):
+        device = latents.device
+        dtype = latents.dtype
+        batch_size = latents.shape[0]
+
+        # 1. generate query points
+        if isinstance(bounds, float):
+            bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
+
+        bbox_min, bbox_max = np.array(bounds[0:3]), np.array(bounds[3:6])
+        xyz_samples, grid_size, length = generate_dense_grid_points(
+            bbox_min=bbox_min,
+            bbox_max=bbox_max,
+            octree_resolution=octree_resolution,
+            indexing="ij"
+        )
+        xyz_samples = torch.from_numpy(xyz_samples).to(device, dtype=dtype).contiguous().reshape(-1, 3)
+
+        # 2. latents to 3d volume
+        batch_logits = []
+        for start in tqdm(range(0, xyz_samples.shape[0], num_chunks), desc=f"Volume Decoding",
+                          disable=not enable_pbar):
+            chunk_queries = xyz_samples[start: start + num_chunks, :]
+            chunk_queries = repeat(chunk_queries, "p c -> b p c", b=batch_size)
+            logits = geo_decoder(queries=chunk_queries, latents=latents)
+            batch_logits.append(logits)
+
+        grid_logits = torch.cat(batch_logits, dim=1)
+        grid_logits = grid_logits.view((batch_size, *grid_size)).float()
+
+        return grid_logits
+
+
+class HierarchicalVolumeDecoding:
+    @torch.no_grad()
+    def __call__(
+        self,
+        latents: torch.FloatTensor,
+        geo_decoder: Callable,
+        bounds: Union[Tuple[float], List[float], float] = 1.01,
+        num_chunks: int = 10000,
+        mc_level: float = 0.0,
+        octree_resolution: int = None,
+        min_resolution: int = 63,
+        enable_pbar: bool = True,
+        **kwargs,
+    ):
+        device = latents.device
+        dtype = latents.dtype
+
+        resolutions = []
+        if octree_resolution < min_resolution:
+            resolutions.append(octree_resolution)
+        while octree_resolution >= min_resolution:
+            resolutions.append(octree_resolution)
+            octree_resolution = octree_resolution // 2
+        resolutions.reverse()
+
+        # 1. generate query points
+        if isinstance(bounds, float):
+            bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
+        bbox_min = np.array(bounds[0:3])
+        bbox_max = np.array(bounds[3:6])
+        bbox_size = bbox_max - bbox_min
+
+        xyz_samples, grid_size, length = generate_dense_grid_points(
+            bbox_min=bbox_min,
+            bbox_max=bbox_max,
+            octree_resolution=resolutions[0],
+            indexing="ij"
+        )
+
+        dilate = nn.Conv3d(1, 1, 3, padding=1, bias=False, device=device, dtype=dtype)
+        dilate.weight = torch.nn.Parameter(torch.ones(dilate.weight.shape, dtype=dtype, device=device))
+
+        grid_size = np.array(grid_size)
+        xyz_samples = torch.from_numpy(xyz_samples).to(device, dtype=dtype).contiguous().reshape(-1, 3)
+
+        # 2. latents to 3d volume
+        batch_logits = []
+        batch_size = latents.shape[0]
+        for start in tqdm(range(0, xyz_samples.shape[0], num_chunks),
+                          desc=f"Hierarchical Volume Decoding [r{resolutions[0] + 1}]"):
+            queries = xyz_samples[start: start + num_chunks, :]
+            batch_queries = repeat(queries, "p c -> b p c", b=batch_size)
+            logits = geo_decoder(queries=batch_queries, latents=latents)
+            batch_logits.append(logits)
+
+        grid_logits = torch.cat(batch_logits, dim=1).view((batch_size, grid_size[0], grid_size[1], grid_size[2]))
+
+        for octree_depth_now in resolutions[1:]:
+            grid_size = np.array([octree_depth_now + 1] * 3)
+            resolution = bbox_size / octree_depth_now
+            next_index = torch.zeros(tuple(grid_size), dtype=dtype, device=device)
+            next_logits = torch.full(next_index.shape, -10000., dtype=dtype, device=device)
+            curr_points = extract_near_surface_volume_fn(grid_logits.squeeze(0), mc_level)
+            curr_points += grid_logits.squeeze(0).abs() < 0.95
+
+            if octree_depth_now == resolutions[-1]:
+                expand_num = 0
+            else:
+                expand_num = 1
+            for i in range(expand_num):
+                curr_points = dilate(curr_points.unsqueeze(0).to(dtype)).squeeze(0)
+            (cidx_x, cidx_y, cidx_z) = torch.where(curr_points > 0)
+            next_index[cidx_x * 2, cidx_y * 2, cidx_z * 2] = 1
+            for i in range(2 - expand_num):
+                next_index = dilate(next_index.unsqueeze(0)).squeeze(0)
+            nidx = torch.where(next_index > 0)
+
+            next_points = torch.stack(nidx, dim=1)
+            next_points = (next_points * torch.tensor(resolution, dtype=next_points.dtype, device=device) +
+                           torch.tensor(bbox_min, dtype=next_points.dtype, device=device))
+            batch_logits = []
+            for start in tqdm(range(0, next_points.shape[0], num_chunks),
+                              desc=f"Hierarchical Volume Decoding [r{octree_depth_now + 1}]"):
+                queries = next_points[start: start + num_chunks, :]
+                batch_queries = repeat(queries, "p c -> b p c", b=batch_size)
+                logits = geo_decoder(queries=batch_queries.to(latents.dtype), latents=latents)
+                batch_logits.append(logits)
+            grid_logits = torch.cat(batch_logits, dim=1)
+            next_logits[nidx] = grid_logits[0, ..., 0]
+            grid_logits = next_logits.unsqueeze(0)
+        grid_logits[grid_logits == -10000.] = float('nan')
+
+        return grid_logits
+
+
+class FlashVDMVolumeDecoding:
+    def __init__(self, topk_mode='mean'):
+        if topk_mode not in ['mean', 'merge']:
+            raise ValueError(f'Unsupported topk_mode {topk_mode}, available: {["mean", "merge"]}')
+
+        if topk_mode == 'mean':
+            self.processor = FlashVDMCrossAttentionProcessor()
+        else:
+            self.processor = FlashVDMTopMCrossAttentionProcessor()
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        latents: torch.FloatTensor,
+        geo_decoder: CrossAttentionDecoder,
+        bounds: Union[Tuple[float], List[float], float] = 1.01,
+        num_chunks: int = 10000,
+        mc_level: float = 0.0,
+        octree_resolution: int = None,
+        min_resolution: int = 63,
+        mini_grid_num: int = 4,
+        enable_pbar: bool = True,
+        **kwargs,
+    ):
+        processor = self.processor
+        geo_decoder.set_cross_attention_processor(processor)
+
+        device = latents.device
+        dtype = latents.dtype
+
+        resolutions = []
+        if octree_resolution < min_resolution:
+            resolutions.append(octree_resolution)
+        while octree_resolution >= min_resolution:
+            resolutions.append(octree_resolution)
+            octree_resolution = octree_resolution // 2
+        resolutions.reverse()
+        resolutions[0] = round(resolutions[0] / mini_grid_num) * mini_grid_num - 1
+        for i, resolution in enumerate(resolutions[1:]):
+            resolutions[i + 1] = resolutions[0] * 2 ** (i + 1)
+
+        logger.info(f"FlashVDMVolumeDecoding Resolution: {resolutions}")
+
+        # 1. generate query points
+        if isinstance(bounds, float):
+            bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
+        bbox_min = np.array(bounds[0:3])
+        bbox_max = np.array(bounds[3:6])
+        bbox_size = bbox_max - bbox_min
+
+        xyz_samples, grid_size, length = generate_dense_grid_points(
+            bbox_min=bbox_min,
+            bbox_max=bbox_max,
+            octree_resolution=resolutions[0],
+            indexing="ij"
+        )
+
+        dilate = nn.Conv3d(1, 1, 3, padding=1, bias=False, device=device, dtype=dtype)
+        dilate.weight = torch.nn.Parameter(torch.ones(dilate.weight.shape, dtype=dtype, device=device))
+
+        grid_size = np.array(grid_size)
+
+        # 2. latents to 3d volume
+        xyz_samples = torch.from_numpy(xyz_samples).to(device, dtype=dtype)
+        batch_size = latents.shape[0]
+        mini_grid_size = xyz_samples.shape[0] // mini_grid_num
+        xyz_samples = xyz_samples.view(
+            mini_grid_num, mini_grid_size,
+            mini_grid_num, mini_grid_size,
+            mini_grid_num, mini_grid_size, 3
+        ).permute(
+            0, 2, 4, 1, 3, 5, 6
+        ).reshape(
+            -1, mini_grid_size * mini_grid_size * mini_grid_size, 3
+        )
+        batch_logits = []
+        num_batchs = max(num_chunks // xyz_samples.shape[1], 1)
+        for start in tqdm(range(0, xyz_samples.shape[0], num_batchs),
+                          desc=f"FlashVDM Volume Decoding", disable=not enable_pbar):
+            queries = xyz_samples[start: start + num_batchs, :]
+            batch = queries.shape[0]
+            batch_latents = repeat(latents.squeeze(0), "p c -> b p c", b=batch)
+            processor.topk = True
+            logits = geo_decoder(queries=queries, latents=batch_latents)
+            batch_logits.append(logits)
+        grid_logits = torch.cat(batch_logits, dim=0).reshape(
+            mini_grid_num, mini_grid_num, mini_grid_num,
+            mini_grid_size, mini_grid_size,
+            mini_grid_size
+        ).permute(0, 3, 1, 4, 2, 5).contiguous().view(
+            (batch_size, grid_size[0], grid_size[1], grid_size[2])
+        )
+
+        for octree_depth_now in resolutions[1:]:
+            grid_size = np.array([octree_depth_now + 1] * 3)
+            resolution = bbox_size / octree_depth_now
+            next_index = torch.zeros(tuple(grid_size), dtype=dtype, device=device)
+            next_logits = torch.full(next_index.shape, -10000., dtype=dtype, device=device)
+            curr_points = extract_near_surface_volume_fn(grid_logits.squeeze(0), mc_level)
+            curr_points += grid_logits.squeeze(0).abs() < 0.95
+
+            if octree_depth_now == resolutions[-1]:
+                expand_num = 0
+            else:
+                expand_num = 1
+            for i in range(expand_num):
+                curr_points = dilate(curr_points.unsqueeze(0).to(dtype)).squeeze(0)
+            (cidx_x, cidx_y, cidx_z) = torch.where(curr_points > 0)
+
+            next_index[cidx_x * 2, cidx_y * 2, cidx_z * 2] = 1
+            for i in range(2 - expand_num):
+                next_index = dilate(next_index.unsqueeze(0)).squeeze(0)
+            nidx = torch.where(next_index > 0)
+
+            next_points = torch.stack(nidx, dim=1)
+            next_points = (next_points * torch.tensor(resolution, dtype=torch.float32, device=device) +
+                           torch.tensor(bbox_min, dtype=torch.float32, device=device))
+
+            query_grid_num = 6
+            min_val = next_points.min(axis=0).values
+            max_val = next_points.max(axis=0).values
+            vol_queries_index = (next_points - min_val) / (max_val - min_val) * (query_grid_num - 0.001)
+            index = torch.floor(vol_queries_index).long()
+            index = index[..., 0] * (query_grid_num ** 2) + index[..., 1] * query_grid_num + index[..., 2]
+            index = index.sort()
+            next_points = next_points[index.indices].unsqueeze(0).contiguous()
+            unique_values = torch.unique(index.values, return_counts=True)
+            grid_logits = torch.zeros((next_points.shape[1]), dtype=latents.dtype, device=latents.device)
+            input_grid = [[], []]
+            logits_grid_list = []
+            start_num = 0
+            sum_num = 0
+            for grid_index, count in zip(unique_values[0].cpu().tolist(), unique_values[1].cpu().tolist()):
+                if sum_num + count < num_chunks or sum_num == 0:
+                    sum_num += count
+                    input_grid[0].append(grid_index)
+                    input_grid[1].append(count)
+                else:
+                    processor.topk = input_grid
+                    logits_grid = geo_decoder(queries=next_points[:, start_num:start_num + sum_num], latents=latents)
+                    start_num = start_num + sum_num
+                    logits_grid_list.append(logits_grid)
+                    input_grid = [[grid_index], [count]]
+                    sum_num = count
+            if sum_num > 0:
+                processor.topk = input_grid
+                logits_grid = geo_decoder(queries=next_points[:, start_num:start_num + sum_num], latents=latents)
+                logits_grid_list.append(logits_grid)
+            logits_grid = torch.cat(logits_grid_list, dim=1)
+            grid_logits[index.indices] = logits_grid.squeeze(0).squeeze(-1)
+            next_logits[nidx] = grid_logits
+            grid_logits = next_logits.unsqueeze(0)
+
+        grid_logits[grid_logits == -10000.] = float('nan')
+
+        return grid_logits

hy3dgen/shapegen/pipelines.py

@@ -204,7 +204,7 @@ class Hunyuan3DDiTPipeline:
             config['model']['params']['guidance_embed'] = True
             config['conditioner']['params']['main_image_encoder']['kwargs']['has_guidance_embed'] = True
         config['model']['params']['attention_mode'] = attention_mode
-        config['vae']['params']['attention_mode'] = attention_mode
+        #config['vae']['params']['attention_mode'] = attention_mode
 
         if cublas_ops:
             config['vae']['params']['cublas_ops'] = True

hy3dgen/shapegen/postprocessors.py

@@ -1,13 +1,3 @@
-# Open Source Model Licensed under the Apache License Version 2.0
-# 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
@@ -25,11 +15,12 @@
 import tempfile
 import os
 from typing import Union
-
+import torch
+import numpy as np
 import pymeshlab
 import trimesh
 
-from .models.vae import Latent2MeshOutput
+from .models.autoencoders import Latent2MeshOutput
 
 import folder_paths
 
@@ -44,6 +35,9 @@ def load_mesh(path):
 
 
 def reduce_face(mesh: pymeshlab.MeshSet, max_facenum: int = 200000):
+    if max_facenum > mesh.current_mesh().face_number():
+        return mesh
+
     mesh.apply_filter(
         "meshing_decimation_quadric_edge_collapse",
         targetfacenum=max_facenum,
@@ -287,3 +281,47 @@ class DegenerateFaceRemover:
 
         mesh = export_mesh(mesh, ms)
         return mesh
+
+
+def mesh_normalize(mesh):
+    """
+    Normalize mesh vertices to sphere
+    """
+    scale_factor = 1.2
+    vtx_pos = np.asarray(mesh.vertices)
+    max_bb = (vtx_pos - 0).max(0)[0]
+    min_bb = (vtx_pos - 0).min(0)[0]
+
+    center = (max_bb + min_bb) / 2
+
+    scale = torch.norm(torch.tensor(vtx_pos - center, dtype=torch.float32), dim=1).max() * 2.0
+
+    vtx_pos = (vtx_pos - center) * (scale_factor / float(scale))
+    mesh.vertices = vtx_pos
+
+    return mesh
+
+
+class MeshSimplifier:
+    def __init__(self, executable: str = None):
+        if executable is None:
+            CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
+            executable = os.path.join(CURRENT_DIR, "mesh_simplifier.bin")
+        self.executable = executable
+
+    def __call__(
+        self,
+        mesh: Union[trimesh.Trimesh],
+    ) -> Union[trimesh.Trimesh]:
+        with tempfile.NamedTemporaryFile(suffix='.obj', delete=False) as temp_input:
+            with tempfile.NamedTemporaryFile(suffix='.obj', delete=False) as temp_output:
+                mesh.export(temp_input.name)
+                os.system(f'{self.executable} {temp_input.name} {temp_output.name}')
+                ms = trimesh.load(temp_output.name, process=False)
+                if isinstance(ms, trimesh.Scene):
+                    combined_mesh = trimesh.Trimesh()
+                    for geom in ms.geometry.values():
+                        combined_mesh = trimesh.util.concatenate([combined_mesh, geom])
+                    ms = combined_mesh
+                ms = mesh_normalize(ms)
+                return ms

hy3dgen/shapegen/utils.py

@@ -0,0 +1,123 @@
+# 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 logging
+import os
+from functools import wraps
+
+import torch
+
+
+def get_logger(name):
+    logger = logging.getLogger(name)
+    logger.setLevel(logging.INFO)
+
+    console_handler = logging.StreamHandler()
+    console_handler.setLevel(logging.INFO)
+
+    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+    console_handler.setFormatter(formatter)
+    logger.addHandler(console_handler)
+    return logger
+
+
+logger = get_logger('hy3dgen.shapgen')
+
+
+class synchronize_timer:
+    """ Synchronized timer to count the inference time of `nn.Module.forward`.
+
+        Supports both context manager and decorator usage.
+
+        Example as context manager:
+        ```python
+        with synchronize_timer('name') as t:
+            run()
+        ```
+
+        Example as decorator:
+        ```python
+        @synchronize_timer('Export to trimesh')
+        def export_to_trimesh(mesh_output):
+            pass
+        ```
+    """
+
+    def __init__(self, name=None):
+        self.name = name
+
+    def __enter__(self):
+        """Context manager entry: start timing."""
+        if os.environ.get('HY3DGEN_DEBUG', '0') == '1':
+            self.start = torch.cuda.Event(enable_timing=True)
+            self.end = torch.cuda.Event(enable_timing=True)
+            self.start.record()
+            return lambda: self.time
+
+    def __exit__(self, exc_type, exc_value, exc_tb):
+        """Context manager exit: stop timing and log results."""
+        if os.environ.get('HY3DGEN_DEBUG', '0') == '1':
+            self.end.record()
+            torch.cuda.synchronize()
+            self.time = self.start.elapsed_time(self.end)
+            if self.name is not None:
+                logger.info(f'{self.name} takes {self.time} ms')
+
+    def __call__(self, func):
+        """Decorator: wrap the function to time its execution."""
+
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            with self:
+                result = func(*args, **kwargs)
+            return result
+
+        return wrapper
+
+
+def smart_load_model(
+    model_path,
+    subfolder,
+    use_safetensors,
+    variant,
+):
+    original_model_path = model_path
+    # try local path
+    base_dir = os.environ.get('HY3DGEN_MODELS', '~/.cache/hy3dgen')
+    model_path = os.path.expanduser(os.path.join(base_dir, model_path, subfolder))
+    logger.info(f'Try to load model from local path: {model_path}')
+    if not os.path.exists(model_path):
+        logger.info('Model path not exists, try to download from huggingface')
+        try:
+            import huggingface_hub
+            # download from huggingface
+            path = huggingface_hub.snapshot_download(repo_id=original_model_path)
+            model_path = os.path.join(path, subfolder)
+        except ImportError:
+            logger.warning(
+                "You need to install HuggingFace Hub to load models from the hub."
+            )
+            raise RuntimeError(f"Model path {model_path} not found")
+        except Exception as e:
+            raise e
+
+    if not os.path.exists(model_path):
+        raise FileNotFoundError(f"Model path {original_model_path} not found")
+
+    extension = 'ckpt' if not use_safetensors else 'safetensors'
+    variant = '' if variant is None else f'.{variant}'
+    ckpt_name = f'model{variant}.{extension}'
+    config_path = os.path.join(model_path, 'config.yaml')
+    ckpt_path = os.path.join(model_path, ckpt_name)
+    return config_path, ckpt_path

nodes.py

@@ -1194,8 +1194,12 @@ class Hy3DVAEDecode:
                 "octree_resolution": ("INT", {"default": 384, "min": 8, "max": 4096, "step": 8}),
                 "num_chunks": ("INT", {"default": 8000, "min": 1, "max": 10000000, "step": 1, "tooltip": "Number of chunks to process at once, higher values use more memory, but make the process faster"}),
                 "mc_level": ("FLOAT", {"default": 0, "min": -1.0, "max": 1.0, "step": 0.0001}),
-                "mc_algo": (["mc", "dmc", "odc", "none"], {"default": "mc"}),
+                #"mc_algo": (["mc", "dmc", "odc", "none"], {"default": "mc"}),
+                "mc_algo": (["mc", "dmc"], {"default": "mc"}),
             },
+            "optional": {
+                "enable_flash_vdm": ("BOOLEAN", {"default": True}),
+            }
         }
 
     RETURN_TYPES = ("TRIMESH",)
@@ -1203,11 +1207,14 @@ class Hy3DVAEDecode:
     FUNCTION = "process"
     CATEGORY = "Hunyuan3DWrapper"
 
-    def process(self, vae, latents, box_v, octree_resolution, mc_level, num_chunks, mc_algo):
+    def process(self, vae, latents, box_v, octree_resolution, mc_level, num_chunks, mc_algo, enable_flash_vdm=True):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
 
         vae.to(device)
+
+        vae.enable_flashvdm_decoder(enabled=enable_flash_vdm)
+        
         latents = 1. / vae.scale_factor * latents
         latents = vae(latents)