update

.gitignore

@@ -2,6 +2,7 @@
 __pycache__/
 *.py[cod]
 *$py.class
+.DS_Store
 
 # C extensions
 *.so

hy3dgen/__init__.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
@@ -20,4 +10,4 @@
 # 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.
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.

hy3dgen/rembg.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
@@ -22,7 +12,6 @@
 # fine-tuning enabling code and other elements of the foregoing made publicly available
 # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
 
-
 from PIL import Image
 from rembg import remove, new_session
 

hy3dgen/shapegen/__init__.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

hy3dgen/shapegen/models/autoencoders/__init__.py

@@ -1,5 +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 CrossAttentionProcessor
+from .attention_processors import FlashVDMCrossAttentionProcessor, CrossAttentionProcessor, \
+    FlashVDMTopMCrossAttentionProcessor
 from .model import ShapeVAE, VectsetVAE
 from .surface_extractors import SurfaceExtractors, MCSurfaceExtractor, DMCSurfaceExtractor, Latent2MeshOutput
-from .volume_decoders import  VanillaVolumeDecoder
+from .volume_decoders import HierarchicalVolumeDecoding, FlashVDMVolumeDecoding, VanillaVolumeDecoder

hy3dgen/shapegen/models/autoencoders/attention_blocks.py

@@ -1,3 +1,18 @@
+# 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
 

hy3dgen/shapegen/models/autoencoders/attention_processors.py

@@ -1,3 +1,17 @@
+# 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
@@ -17,3 +31,66 @@ class CrossAttentionProcessor:
         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

@@ -1,3 +1,17 @@
+# 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
@@ -6,8 +20,8 @@ import yaml
 
 from .attention_blocks import FourierEmbedder, Transformer, CrossAttentionDecoder
 from .surface_extractors import MCSurfaceExtractor, SurfaceExtractors
-from .volume_decoders import VanillaVolumeDecoder
-from ...utils import logger, synchronize_timer
+from .volume_decoders import VanillaVolumeDecoder, FlashVDMVolumeDecoding, HierarchicalVolumeDecoding
+from ...utils import logger, synchronize_timer, smart_load_model
 
 
 class VectsetVAE(nn.Module):
@@ -59,34 +73,12 @@ class VectsetVAE(nn.Module):
         subfolder='hunyuan3d-vae-v2-0',
         **kwargs,
     ):
-        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)
+        config_path, ckpt_path = smart_load_model(
+            model_path,
+            subfolder=subfolder,
+            use_safetensors=use_safetensors,
+            variant=variant
+        )
 
         return cls.from_single_file(
             ckpt_path,
@@ -117,6 +109,25 @@ class VectsetVAE(nn.Module):
             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__(

hy3dgen/shapegen/models/autoencoders/surface_extractors.py

@@ -1,3 +1,17 @@
+# 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

hy3dgen/shapegen/models/autoencoders/volume_decoders.py

@@ -1,3 +1,17 @@
+# 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
@@ -8,9 +22,103 @@ 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,
@@ -74,3 +182,254 @@ class VanillaVolumeDecoder:
         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/models/denoisers/__init__.py

@@ -1 +1,15 @@
+# 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 .hunyuan3ddit import Hunyuan3DDiT

hy3dgen/shapegen/models/denoisers/hunyuan3ddit.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

hy3dgen/shapegen/pipelines.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
@@ -447,8 +437,6 @@ class Hunyuan3DDiTPipeline:
 
                 cond = cat_recursive(cond, un_cond_drop_main, un_cond)
             else:
-                un_cond = self.conditioner.unconditional_embedding(bsz, **additional_cond_inputs)
-
                 def cat_recursive(a, b):
                     if isinstance(a, torch.Tensor):
                         return torch.cat([a, b], dim=0).to(self.dtype)
@@ -587,10 +575,14 @@ class Hunyuan3DDiTPipeline:
                                       getattr(self.model, 'guidance_cond_proj_dim', None) is None
         dual_guidance = dual_guidance_scale >= 0 and dual_guidance
 
-        image, mask = self.prepare_image(image)
-        cond = self.encode_cond(image=image,
-                                do_classifier_free_guidance=do_classifier_free_guidance,
-                                dual_guidance=dual_guidance)
+        cond_inputs = self.prepare_image(image)
+        image = cond_inputs.pop('image')
+        cond = self.encode_cond(
+            image=image,
+            additional_cond_inputs=cond_inputs,
+            do_classifier_free_guidance=do_classifier_free_guidance,
+            dual_guidance=False,
+        )
         batch_size = image.shape[0]
 
         t_dtype = torch.long

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
@@ -70,7 +60,7 @@ def remove_floater(mesh: pymeshlab.MeshSet):
 
 
 def pymeshlab2trimesh(mesh: pymeshlab.MeshSet):
-    with tempfile.NamedTemporaryFile(suffix='.ply', delete=True) as temp_file:
+    with tempfile.NamedTemporaryFile(suffix='.ply', delete=False) as temp_file:
         mesh.save_current_mesh(temp_file.name)
         mesh = trimesh.load(temp_file.name)
     # 检查加载的对象类型
@@ -84,7 +74,7 @@ def pymeshlab2trimesh(mesh: pymeshlab.MeshSet):
 
 
 def trimesh2pymeshlab(mesh: trimesh.Trimesh):
-    with tempfile.NamedTemporaryFile(suffix='.ply', delete=True) as temp_file:
+    with tempfile.NamedTemporaryFile(suffix='.ply', delete=False) as temp_file:
         if isinstance(mesh, trimesh.scene.Scene):
             for idx, obj in enumerate(mesh.geometry.values()):
                 if idx == 0:
@@ -158,7 +148,7 @@ class DegenerateFaceRemover:
     ) -> Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput]:
         ms = import_mesh(mesh)
 
-        with tempfile.NamedTemporaryFile(suffix='.ply', delete=True) as temp_file:
+        with tempfile.NamedTemporaryFile(suffix='.ply', delete=False) as temp_file:
             ms.save_current_mesh(temp_file.name)
             ms = pymeshlab.MeshSet()
             ms.load_new_mesh(temp_file.name)
@@ -167,20 +157,6 @@ class DegenerateFaceRemover:
         return mesh
 
 
-def import_pymeshlab_mesh(mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str]) -> pymeshlab.MeshSet:
-    if isinstance(mesh, str):
-        mesh = load_mesh(mesh)
-    elif isinstance(mesh, Latent2MeshOutput):
-        mesh = pymeshlab.MeshSet()
-        mesh_pymeshlab = pymeshlab.Mesh(vertex_matrix=mesh.mesh_v, face_matrix=mesh.mesh_f)
-        mesh.add_mesh(mesh_pymeshlab, "converted_mesh")
-
-    if isinstance(mesh, (trimesh.Trimesh, trimesh.scene.Scene)):
-        mesh = trimesh2pymeshlab(mesh)
-
-    return mesh
-
-
 def mesh_normalize(mesh):
     """
     Normalize mesh vertices to sphere
@@ -212,8 +188,8 @@ class MeshSimplifier:
         self,
         mesh: Union[trimesh.Trimesh],
     ) -> Union[trimesh.Trimesh]:
-        with tempfile.NamedTemporaryFile(suffix='.obj', delete=True) as temp_input:
-            with tempfile.NamedTemporaryFile(suffix='.obj', delete=True) as temp_output:
+        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)

hy3dgen/shapegen/preprocessors.py

@@ -1,12 +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
@@ -96,9 +87,7 @@ class ImageProcessorV2:
         mask = mask.clip(0, 255).astype(np.uint8)
         return result, mask
 
-    def __call__(self, image, border_ratio=0.15, to_tensor=True, **kwargs):
-        if self.border_ratio is not None:
-            border_ratio = self.border_ratio
+    def load_image(self, image, border_ratio=0.15, to_tensor=True):
         if isinstance(image, str):
             image = cv2.imread(image, cv2.IMREAD_UNCHANGED)
             image, mask = self.recenter(image, border_ratio=border_ratio)
@@ -115,7 +104,12 @@ class ImageProcessorV2:
         if to_tensor:
             image = array_to_tensor(image)
             mask = array_to_tensor(mask)
+        return image, mask
 
+    def __call__(self, image, border_ratio=0.15, to_tensor=True, **kwargs):
+        if self.border_ratio is not None:
+            border_ratio = self.border_ratio
+        image, mask = self.load_image(image, border_ratio=border_ratio, to_tensor=to_tensor)
         outputs = {
             'image': image,
             'mask': mask
@@ -147,22 +141,7 @@ class MVImageProcessorV2(ImageProcessorV2):
         view_idxs = []
         for idx, (view_tag, image) in enumerate(image_dict.items()):
             view_idxs.append(self.view2idx[view_tag])
-            if isinstance(image, str):
-                image = cv2.imread(image, cv2.IMREAD_UNCHANGED)
-                image, mask = self.recenter(image, border_ratio=border_ratio)
-                image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-            elif isinstance(image, Image.Image):
-                image = image.convert("RGBA")
-                image = np.asarray(image)
-                image, mask = self.recenter(image, border_ratio=border_ratio)
-
-            image = cv2.resize(image, (self.size, self.size), interpolation=cv2.INTER_CUBIC)
-            mask = cv2.resize(mask, (self.size, self.size), interpolation=cv2.INTER_NEAREST)
-            mask = mask[..., np.newaxis]
-
-            if to_tensor:
-                image = array_to_tensor(image)
-                mask = array_to_tensor(mask)
+            image, mask = self.load_image(image, border_ratio=border_ratio, to_tensor=to_tensor)
             images.append(image)
             masks.append(mask)
 

hy3dgen/shapegen/schedulers.py

@@ -12,6 +12,20 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+# 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 math
 from dataclasses import dataclass
 from typing import List, Optional, Tuple, Union

hy3dgen/shapegen/utils.py

@@ -1,3 +1,17 @@
+# 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
@@ -86,10 +100,13 @@ def smart_load_model(
     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)
+            from huggingface_hub import snapshot_download
+            # 只下载指定子目录
+            path = snapshot_download(
+                repo_id=original_model_path,
+                allow_patterns=[f"{subfolder}/*"],  # 关键修改：模式匹配子文件夹
+            )
+            model_path = os.path.join(path, subfolder)  # 保持路径拼接逻辑不变
         except ImportError:
             logger.warning(
                 "You need to install HuggingFace Hub to load models from the hub."

hy3dgen/texgen/__init__.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

hy3dgen/texgen/custom_rasterizer/custom_rasterizer/__init__.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

hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_glb.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

hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_obj.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

hy3dgen/texgen/custom_rasterizer/custom_rasterizer/render.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

hy3dgen/texgen/differentiable_renderer/__init__.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
@@ -20,4 +10,4 @@
 # 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.
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.

hy3dgen/texgen/differentiable_renderer/camera_utils.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

hy3dgen/texgen/differentiable_renderer/mesh_processor.py

@@ -1,3 +1,17 @@
+# 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 numpy as np
 
 def meshVerticeInpaint_smooth(texture, mask, vtx_pos, vtx_uv, pos_idx, uv_idx):

hy3dgen/texgen/differentiable_renderer/mesh_render.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

hy3dgen/texgen/differentiable_renderer/mesh_utils.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

hy3dgen/texgen/differentiable_renderer/setup.py

@@ -1,3 +1,17 @@
+# 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 setuptools import setup, Extension
 import pybind11
 import sys

hy3dgen/texgen/hunyuanpaint/__init__.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
@@ -20,4 +10,4 @@
 # 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.
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.

hy3dgen/texgen/hunyuanpaint/pipeline.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
@@ -119,6 +109,8 @@ class HunyuanPaintPipeline(StableDiffusionPipeline):
         return_dict=True,
         **cached_condition,
     ):
+        device = self._execution_device
+
         if image is None:
             raise ValueError("Inputting embeddings not supported for this pipeline. Please pass an image.")
         assert not isinstance(image, torch.Tensor)
@@ -127,7 +119,7 @@ class HunyuanPaintPipeline(StableDiffusionPipeline):
 
         image_vae = torch.tensor(np.array(image) / 255.0)
         image_vae = image_vae.unsqueeze(0).permute(0, 3, 1, 2).unsqueeze(0)
-        image_vae = image_vae.to(device=self.vae.device, dtype=self.vae.dtype)
+        image_vae = image_vae.to(device=device, dtype=self.vae.dtype)
 
         batch_size = image_vae.shape[0]
         assert batch_size == 1
@@ -171,13 +163,13 @@ class HunyuanPaintPipeline(StableDiffusionPipeline):
             camera_info = cached_condition['camera_info_gen']  # B,N
             if isinstance(camera_info, List):
                 camera_info = torch.tensor(camera_info)
-            camera_info = camera_info.to(image_vae.device).to(torch.int64)
+            camera_info = camera_info.to(device).to(torch.int64)
             cached_condition['camera_info_gen'] = camera_info
         if 'camera_info_ref' in cached_condition:
             camera_info = cached_condition['camera_info_ref']  # B,N
             if isinstance(camera_info, List):
                 camera_info = torch.tensor(camera_info)
-            camera_info = camera_info.to(image_vae.device).to(torch.int64)
+            camera_info = camera_info.to(device).to(torch.int64)
             cached_condition['camera_info_ref'] = camera_info
 
         cached_condition['ref_latents'] = ref_latents

hy3dgen/texgen/hunyuanpaint/unet/__init__.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
@@ -20,4 +10,4 @@
 # 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.
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.

hy3dgen/texgen/hunyuanpaint/unet/modules.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
@@ -22,7 +12,6 @@
 # fine-tuning enabling code and other elements of the foregoing made publicly available
 # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
 
-
 import copy
 import json
 import os

hy3dgen/texgen/pipelines.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
@@ -28,6 +18,7 @@ import numpy as np
 import os
 import torch
 from PIL import Image
+from typing import Union, Optional
 
 from .differentiable_renderer.mesh_render import MeshRender
 from .utils.dehighlight_utils import Light_Shadow_Remover
@@ -71,7 +62,10 @@ class Hunyuan3DPaintPipeline:
                 try:
                     import huggingface_hub
                     # download from huggingface
-                    model_path = huggingface_hub.snapshot_download(repo_id=original_model_path)
+                    model_path = huggingface_hub.snapshot_download(repo_id=original_model_path,
+                                                                   allow_patterns=["hunyuan3d-delight-v2-0/*"])
+                    model_path = huggingface_hub.snapshot_download(repo_id=original_model_path,
+                                                                   allow_patterns=["hunyuan3d-paint-v2-0/*"])
                     delight_model_path = os.path.join(model_path, 'hunyuan3d-delight-v2-0')
                     multiview_model_path = os.path.join(model_path, 'hunyuan3d-paint-v2-0')
                     return cls(Hunyuan3DTexGenConfig(delight_model_path, multiview_model_path))
@@ -79,7 +73,6 @@ class Hunyuan3DPaintPipeline:
                     logger.warning(
                         "You need to install HuggingFace Hub to load models from the hub."
                     )
-                    import traceback;traceback.print_exc()
                     raise RuntimeError(f"Model path {model_path} not found")
             else:
                 return cls(Hunyuan3DTexGenConfig(delight_model_path, multiview_model_path))
@@ -103,6 +96,10 @@ class Hunyuan3DPaintPipeline:
         self.models['multiview_model'] = Multiview_Diffusion_Net(self.config)
         # self.models['super_model'] = Image_Super_Net(self.config)
 
+    def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"):
+        self.models['delight_model'].pipeline.enable_model_cpu_offload(gpu_id=gpu_id, device=device)
+        self.models['multiview_model'].pipeline.enable_model_cpu_offload(gpu_id=gpu_id, device=device)
+
     def render_normal_multiview(self, camera_elevs, camera_azims, use_abs_coor=True):
         normal_maps = []
         for elev, azim in zip(camera_elevs, camera_azims):
@@ -147,14 +144,14 @@ class Hunyuan3DPaintPipeline:
         texture = torch.tensor(texture_np / 255).float().to(texture.device)
 
         return texture
-    
+
     def recenter_image(self, image, border_ratio=0.2):
         if image.mode == 'RGB':
             return image
         elif image.mode == 'L':
             image = image.convert('RGB')
             return image
-        
+
         alpha_channel = np.array(image)[:, :, 3]
         non_zero_indices = np.argwhere(alpha_channel > 0)
         if non_zero_indices.size == 0:
@@ -189,7 +186,7 @@ class Hunyuan3DPaintPipeline:
             image_prompt = Image.open(image)
         else:
             image_prompt = image
-        
+
         image_prompt = self.recenter_image(image_prompt)
 
         image_prompt = self.models['delight_model'](image_prompt)
@@ -212,7 +209,7 @@ class Hunyuan3DPaintPipeline:
         multiviews = self.models['multiview_model'](image_prompt, normal_maps + position_maps, camera_info)
 
         for i in range(len(multiviews)):
-            # multiviews[i] = self.models['super_model'](multiviews[i])
+            #     multiviews[i] = self.models['super_model'](multiviews[i])
             multiviews[i] = multiviews[i].resize(
                 (self.config.render_size, self.config.render_size))
 

hy3dgen/texgen/utils/__init__.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
@@ -20,4 +10,4 @@
 # 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.
+# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.

hy3dgen/texgen/utils/alignImg4Tex_utils.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
@@ -22,7 +12,6 @@
 # fine-tuning enabling code and other elements of the foregoing made publicly available
 # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
 
-
 import torch
 from diffusers import EulerAncestralDiscreteScheduler
 from diffusers import StableDiffusionControlNetPipeline, StableDiffusionXLControlNetImg2ImgPipeline, ControlNetModel, \

hy3dgen/texgen/utils/counter_utils.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

hy3dgen/texgen/utils/dehighlight_utils.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

hy3dgen/texgen/utils/imagesuper_utils.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

hy3dgen/texgen/utils/multiview_utils.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

hy3dgen/texgen/utils/simplify_mesh_utils.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

hy3dgen/texgen/utils/uv_warp_utils.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

hy3dgen/text2image.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
@@ -22,7 +12,6 @@
 # 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 random